2025-07-17T07:00:43.0640584Z Current runner version: '2.326.0' 2025-07-17T07:00:43.0647590Z Runner name: 'i-075a75ec57b65bb84' 2025-07-17T07:00:43.0648480Z Runner group name: 'default' 2025-07-17T07:00:43.0649378Z Machine name: 'ip-10-0-72-236' 2025-07-17T07:00:43.0652238Z ##[group]GITHUB_TOKEN Permissions 2025-07-17T07:00:43.0654568Z Contents: read 2025-07-17T07:00:43.0655227Z Metadata: read 2025-07-17T07:00:43.0655728Z ##[endgroup] 2025-07-17T07:00:43.0658169Z Secret source: Actions 2025-07-17T07:00:43.0658908Z Prepare workflow directory 2025-07-17T07:00:43.1171982Z Prepare all required actions 2025-07-17T07:00:43.1212821Z Getting action download info 2025-07-17T07:00:43.3680270Z Download action repository 'pytorch/test-infra@main' (SHA:a9ec424ad5e5851e47d68139cfd953b4031778d5) 2025-07-17T07:00:44.9099539Z Download action repository 'pytorch/pytorch@main' (SHA:d76323d41742cbc05ec6857319b267d2c7ea8fd9) 2025-07-17T07:00:58.2419606Z Download action repository 'actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065' (SHA:a26af69be951a213d495a4c3e4e4022e16d87065) 2025-07-17T07:00:58.5507012Z Download action repository 'aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722' (SHA:ececac1a45f3b08a01d2dd070d28d111c5fe6722) 2025-07-17T07:00:58.7279557Z Download action repository 'aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076' (SHA:062b18b96a7aff071d4dc91bc00c4c1a7945b076) 2025-07-17T07:00:58.8910586Z Download action repository 'seemethere/upload-artifact-s3@baba72d0712b404f646cebe0730933554ebce96a' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-07-17T07:00:59.1641507Z Getting action download info 2025-07-17T07:00:59.2863827Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683) 2025-07-17T07:00:59.5227520Z Getting action download info 2025-07-17T07:00:59.6127492Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-07-17T07:00:59.7760762Z Getting action download info 2025-07-17T07:00:59.8969048Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-07-17T07:01:00.0718086Z Getting action download info 2025-07-17T07:01:00.1970501Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/main (a38f433be2e94a64b095a44ba39879d02d0c2316) 2025-07-17T07:01:00.1974661Z ##[group] Inputs 2025-07-17T07:01:00.1975032Z build-environment: linux-jammy-py3.13-clang12 2025-07-17T07:01:00.1978278Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]} 2025-07-17T07:01:00.1981810Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:01:00.1982649Z sync-tag: 2025-07-17T07:01:00.1983442Z timeout-minutes: 600 2025-07-17T07:01:00.1983712Z use-gha: 2025-07-17T07:01:00.1983941Z dashboard-tag: 2025-07-17T07:01:00.1984194Z s3-bucket: gha-artifacts 2025-07-17T07:01:00.1984476Z aws-role-to-assume: 2025-07-17T07:01:00.1985335Z disable-monitor: false 2025-07-17T07:01:00.1985655Z monitor-log-interval: 5 2025-07-17T07:01:00.1986204Z monitor-data-collect-interval: 1 2025-07-17T07:01:00.1986657Z ##[endgroup] 2025-07-17T07:01:00.1987074Z Complete job name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T07:01:00.2493287Z A job started hook has been configured by the self-hosted runner administrator 2025-07-17T07:01:00.2592943Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-07-17T07:01:00.2601885Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:01:00.2602573Z ##[endgroup] 2025-07-17T07:01:01.4553212Z Runner Type: linux.4xlarge 2025-07-17T07:01:01.4553725Z Instance Type: c5.4xlarge 2025-07-17T07:01:01.4554011Z AMI Name: unknown 2025-07-17T07:01:01.4581288Z AMI ID: ami-05ffe3c48a9991133 2025-07-17T07:01:06.6747880Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2025-07-17T07:01:06.6748341Z with: 2025-07-17T07:01:06.6748953Z github-secret: *** 2025-07-17T07:01:06.6749710Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-07-17T07:01:06.6750544Z activate-with-label: false 2025-07-17T07:01:06.6750824Z label: with-ssh 2025-07-17T07:01:06.6751080Z remove-existing-keys: true 2025-07-17T07:01:06.6751373Z fail-silently: true 2025-07-17T07:01:06.6751619Z env: 2025-07-17T07:01:06.6751847Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:01:06.6752128Z ##[endgroup] 2025-07-17T07:01:06.7866978Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-07-17T07:01:06.7868394Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys 2025-07-17T07:01:06.8026767Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-07-17T07:01:06.8027238Z with: 2025-07-17T07:01:06.8027471Z no-sudo: true 2025-07-17T07:01:06.8027709Z submodules: recursive 2025-07-17T07:01:06.8027985Z fetch-depth: 0 2025-07-17T07:01:06.8028218Z env: 2025-07-17T07:01:06.8028443Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:01:06.8028725Z ##[endgroup] 2025-07-17T07:01:06.8112512Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-07-17T07:01:06.8113525Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-07-17T07:01:06.8121899Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:01:06.8122302Z env: 2025-07-17T07:01:06.8122569Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:01:06.8122876Z ##[endgroup] 2025-07-17T07:01:06.8209382Z ##[group]Run # Use all available CPUs for fetching 2025-07-17T07:01:06.8209831Z # Use all available CPUs for fetching 2025-07-17T07:01:06.8210184Z cd "${GITHUB_WORKSPACE}" 2025-07-17T07:01:06.8210547Z git config --global fetch.parallel 0 2025-07-17T07:01:06.8210953Z git config --global submodule.fetchJobs 0 2025-07-17T07:01:06.8211309Z  2025-07-17T07:01:06.8211694Z # Clean workspace. The default checkout action should also do this, but 2025-07-17T07:01:06.8212190Z # do it here as well just in case 2025-07-17T07:01:06.8212523Z if [[ -d .git ]]; then 2025-07-17T07:01:06.8212818Z  if [ -z "${NO_SUDO}" ]; then 2025-07-17T07:01:06.8213148Z  sudo git clean -ffdx 2025-07-17T07:01:06.8213444Z  else 2025-07-17T07:01:06.8213676Z  git clean -ffdx 2025-07-17T07:01:06.8213951Z  fi 2025-07-17T07:01:06.8214174Z fi 2025-07-17T07:01:06.8219797Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:01:06.8220203Z env: 2025-07-17T07:01:06.8220520Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:01:06.8220827Z NO_SUDO: true 2025-07-17T07:01:06.8221050Z ##[endgroup] 2025-07-17T07:01:06.8383052Z ##[group]Run actions/checkout@v4 2025-07-17T07:01:06.8383368Z with: 2025-07-17T07:01:06.8383632Z ref: a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:01:06.8384141Z fetch-depth: 0 2025-07-17T07:01:06.8384392Z submodules: recursive 2025-07-17T07:01:06.8384663Z show-progress: false 2025-07-17T07:01:06.8384933Z repository: pytorch/pytorch 2025-07-17T07:01:06.8385397Z token: *** 2025-07-17T07:01:06.8385632Z ssh-strict: true 2025-07-17T07:01:06.8385864Z ssh-user: git 2025-07-17T07:01:06.8386117Z persist-credentials: true 2025-07-17T07:01:06.8386400Z clean: true 2025-07-17T07:01:06.8386667Z sparse-checkout-cone-mode: true 2025-07-17T07:01:06.8386965Z fetch-tags: false 2025-07-17T07:01:06.8387209Z lfs: false 2025-07-17T07:01:06.8387452Z set-safe-directory: true 2025-07-17T07:01:06.8387723Z env: 2025-07-17T07:01:06.8387948Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:01:06.8388217Z ##[endgroup] 2025-07-17T07:01:06.9441178Z Syncing repository: pytorch/pytorch 2025-07-17T07:01:06.9442536Z ##[group]Getting Git version info 2025-07-17T07:01:06.9443024Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-07-17T07:01:06.9443745Z [command]/usr/bin/git version 2025-07-17T07:01:06.9444044Z git version 2.47.1 2025-07-17T07:01:06.9446910Z ##[endgroup] 2025-07-17T07:01:06.9455690Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/388165ca-27e7-43e7-b804-3b7685483464/.gitconfig' 2025-07-17T07:01:06.9473796Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/388165ca-27e7-43e7-b804-3b7685483464' before making global git config changes 2025-07-17T07:01:06.9474839Z Adding repository directory to the temporary git global config as a safe directory 2025-07-17T07:01:06.9484952Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-07-17T07:01:06.9515942Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-07-17T07:01:06.9518437Z ##[group]Initializing the repository 2025-07-17T07:01:06.9522307Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-07-17T07:01:06.9551385Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-07-17T07:01:06.9552557Z hint: is subject to change. To configure the initial branch name to use in all 2025-07-17T07:01:06.9553602Z hint: of your new repositories, which will suppress this warning, call: 2025-07-17T07:01:06.9554347Z hint: 2025-07-17T07:01:06.9554871Z hint: git config --global init.defaultBranch 2025-07-17T07:01:06.9555519Z hint: 2025-07-17T07:01:06.9556108Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-07-17T07:01:06.9557180Z hint: 'development'. The just-created branch can be renamed via this command: 2025-07-17T07:01:06.9557983Z hint: 2025-07-17T07:01:06.9558399Z hint: git branch -m 2025-07-17T07:01:06.9559299Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2025-07-17T07:01:06.9563923Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-07-17T07:01:06.9588129Z ##[endgroup] 2025-07-17T07:01:06.9588949Z ##[group]Disabling automatic garbage collection 2025-07-17T07:01:06.9592495Z [command]/usr/bin/git config --local gc.auto 0 2025-07-17T07:01:06.9615298Z ##[endgroup] 2025-07-17T07:01:06.9616048Z ##[group]Setting up auth 2025-07-17T07:01:06.9622043Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-07-17T07:01:06.9646462Z [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' || :" 2025-07-17T07:01:06.9913917Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-07-17T07:01:06.9936309Z [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' || :" 2025-07-17T07:01:07.0182028Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-07-17T07:01:07.0221428Z ##[endgroup] 2025-07-17T07:01:07.0221890Z ##[group]Fetching the repository 2025-07-17T07:01:07.0228749Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-07-17T07:01:51.1808893Z From https://github.com/pytorch/pytorch 2025-07-17T07:01:51.1809439Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-07-17T07:01:51.1810238Z * [new branch] 20250616_dtype_docs -> origin/20250616_dtype_docs 2025-07-17T07:01:51.1810927Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-07-17T07:01:51.1812990Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-07-17T07:01:51.1813795Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-07-17T07:01:51.1814460Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-07-17T07:01:51.1815078Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-07-17T07:01:51.1816137Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-07-17T07:01:51.1817269Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-07-17T07:01:51.1818596Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-07-17T07:01:51.1820031Z * [new branch] ZainRizvi-patch-1 -> origin/ZainRizvi-patch-1 2025-07-17T07:01:51.1820936Z * [new branch] add-missing-args-normalization -> origin/add-missing-args-normalization 2025-07-17T07:01:51.1821952Z * [new branch] addUtilForLinuxBuild -> origin/addUtilForLinuxBuild 2025-07-17T07:01:51.1823105Z * [new branch] add_windows_testing_back -> origin/add_windows_testing_back 2025-07-17T07:01:51.1824128Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-07-17T07:01:51.1825426Z * [new branch] addsimde -> origin/addsimde 2025-07-17T07:01:51.1827568Z * [new branch] adi/test -> origin/adi/test 2025-07-17T07:01:51.1828599Z * [new branch] adi/test_fusions -> origin/adi/test_fusions 2025-07-17T07:01:51.1829754Z * [new branch] adi/test_presve_change -> origin/adi/test_presve_change 2025-07-17T07:01:51.1830790Z * [new branch] adi/testpresve_change -> origin/adi/testpresve_change 2025-07-17T07:01:51.1831942Z * [new branch] adi/update_openblas -> origin/adi/update_openblas 2025-07-17T07:01:51.1833966Z * [new branch] aditew01/test/vec_bf16 -> origin/aditew01/test/vec_bf16 2025-07-17T07:01:51.1835095Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-07-17T07:01:51.1836257Z * [new branch] albanD-patch-1 -> origin/albanD-patch-1 2025-07-17T07:01:51.1837355Z * [new branch] alt-disable -> origin/alt-disable 2025-07-17T07:01:51.1838980Z * [new branch] angelayi/155426 -> origin/angelayi/155426 2025-07-17T07:01:51.1839981Z * [new branch] angelayi/157183 -> origin/angelayi/157183 2025-07-17T07:01:51.1841155Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-07-17T07:01:51.1842342Z * [new branch] angelayi/aoti_custom_op -> origin/angelayi/aoti_custom_op 2025-07-17T07:01:51.1843238Z * [new branch] angelayi/benchmark -> origin/angelayi/benchmark 2025-07-17T07:01:51.1844345Z * [new branch] angelayi/benchmark2 -> origin/angelayi/benchmark2 2025-07-17T07:01:51.1845835Z * [new branch] angelayi/change_pytree_serialization -> origin/angelayi/change_pytree_serialization 2025-07-17T07:01:51.1847571Z * [new branch] angelayi/cpp_loader -> origin/angelayi/cpp_loader 2025-07-17T07:01:51.1848323Z * [new branch] angelayi/customop -> origin/angelayi/customop 2025-07-17T07:01:51.1849827Z * [new branch] angelayi/del_lib -> origin/angelayi/del_lib 2025-07-17T07:01:51.1850836Z * [new branch] angelayi/docs -> origin/angelayi/docs 2025-07-17T07:01:51.1851961Z * [new branch] angelayi/docs2 -> origin/angelayi/docs2 2025-07-17T07:01:51.1853315Z * [new branch] angelayi/dynamo_fake_input -> origin/angelayi/dynamo_fake_input 2025-07-17T07:01:51.1854242Z * [new branch] angelayi/errmsg -> origin/angelayi/errmsg 2025-07-17T07:01:51.1855367Z * [new branch] angelayi/fake_device -> origin/angelayi/fake_device 2025-07-17T07:01:51.1856561Z * [new branch] angelayi/int_lift_constants -> origin/angelayi/int_lift_constants 2025-07-17T07:01:51.1857693Z * [new branch] angelayi/logging.bak -> origin/angelayi/logging.bak 2025-07-17T07:01:51.1858738Z * [new branch] angelayi/logging2 -> origin/angelayi/logging2 2025-07-17T07:01:51.1860055Z * [new branch] angelayi/no_so_weight -> origin/angelayi/no_so_weight 2025-07-17T07:01:51.1861168Z * [new branch] angelayi/pytree -> origin/angelayi/pytree 2025-07-17T07:01:51.1862379Z * [new branch] angelayi/scan_layers -> origin/angelayi/scan_layers 2025-07-17T07:01:51.1863419Z * [new branch] angelayi/symint_input -> origin/angelayi/symint_input 2025-07-17T07:01:51.1864529Z * [new branch] angelayi/torch_size -> origin/angelayi/torch_size 2025-07-17T07:01:51.1865626Z * [new branch] angelayi/torchgenooops -> origin/angelayi/torchgenooops 2025-07-17T07:01:51.1866681Z * [new branch] angelayi/update_schema_msg -> origin/angelayi/update_schema_msg 2025-07-17T07:01:51.1867738Z * [new branch] aoti_static_linkage -> origin/aoti_static_linkage 2025-07-17T07:01:51.1868976Z * [new branch] atalman-inductor-perf-cu124 -> origin/atalman-inductor-perf-cu124 2025-07-17T07:01:51.1870132Z * [new branch] atalman-inductor-perf-cu124.1 -> origin/atalman-inductor-perf-cu124.1 2025-07-17T07:01:51.1871143Z * [new branch] atalman-patch-1 -> origin/atalman-patch-1 2025-07-17T07:01:51.1872764Z * [new branch] atalman-patch-2 -> origin/atalman-patch-2 2025-07-17T07:01:51.1873861Z * [new branch] atalman-patch-3 -> origin/atalman-patch-3 2025-07-17T07:01:51.1875164Z * [new branch] atalman-patch-4 -> origin/atalman-patch-4 2025-07-17T07:01:51.1876166Z * [new branch] atalman-patch-5 -> origin/atalman-patch-5 2025-07-17T07:01:51.1877246Z * [new branch] atalman-patch-6 -> origin/atalman-patch-6 2025-07-17T07:01:51.1878360Z * [new branch] atalman-patch-7 -> origin/atalman-patch-7 2025-07-17T07:01:51.1879487Z * [new branch] atalman-patch-8 -> origin/atalman-patch-8 2025-07-17T07:01:51.1880815Z * [new branch] atalman_inductor_2.3.0 -> origin/atalman_inductor_2.3.0 2025-07-17T07:01:51.1881790Z * [new branch] atalman_inductor_2.3.1 -> origin/atalman_inductor_2.3.1 2025-07-17T07:01:51.1882831Z * [new branch] atalman_inductor_2.4.0 -> 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* [new tag] ciflow/binaries_wheel/157928 -> ciflow/binaries_wheel/157928 2025-07-17T07:01:51.8121227Z * [new tag] ciflow/binaries_wheel/158408 -> ciflow/binaries_wheel/158408 2025-07-17T07:01:51.8122316Z * [new tag] ciflow/h100-cutlass-backend/156626 -> ciflow/h100-cutlass-backend/156626 2025-07-17T07:01:51.8122999Z * [new tag] ciflow/h100-distributed/144552 -> ciflow/h100-distributed/144552 2025-07-17T07:01:51.8123684Z * [new tag] ciflow/h100-distributed/150312 -> ciflow/h100-distributed/150312 2025-07-17T07:01:51.8124370Z * [new tag] ciflow/h100-distributed/156605 -> ciflow/h100-distributed/156605 2025-07-17T07:01:51.8125202Z * [new tag] ciflow/h100-distributed/156703 -> ciflow/h100-distributed/156703 2025-07-17T07:01:51.8126133Z * [new tag] ciflow/h100-symm-mem/151845 -> ciflow/h100-symm-mem/151845 2025-07-17T07:01:51.8126695Z * [new tag] ciflow/h100-symm-mem/157180 -> ciflow/h100-symm-mem/157180 2025-07-17T07:01:51.8127385Z * [new tag] ciflow/h100-symm-mem/157970 -> ciflow/h100-symm-mem/157970 2025-07-17T07:01:51.8128196Z * [new tag] ciflow/h100-symm-mem/158511 -> ciflow/h100-symm-mem/158511 2025-07-17T07:01:51.8128769Z * [new tag] ciflow/h100-symm-mem/158512 -> ciflow/h100-symm-mem/158512 2025-07-17T07:01:51.8129436Z * [new tag] ciflow/h100-symm-mem/158513 -> ciflow/h100-symm-mem/158513 2025-07-17T07:01:51.8130111Z * [new tag] ciflow/h100-symm-mem/158514 -> ciflow/h100-symm-mem/158514 2025-07-17T07:01:51.8130765Z * [new tag] ciflow/h100-symm-mem/158515 -> ciflow/h100-symm-mem/158515 2025-07-17T07:01:51.8131769Z * [new tag] ciflow/h100-symm-mem/158523 -> ciflow/h100-symm-mem/158523 2025-07-17T07:01:51.8132352Z * [new tag] ciflow/h100/156980 -> ciflow/h100/156980 2025-07-17T07:01:51.8133027Z * [new tag] ciflow/h100/158459 -> ciflow/h100/158459 2025-07-17T07:01:51.8134377Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/151845 -> ciflow/inductor-perf-test-nightly-rocm/151845 2025-07-17T07:01:51.8135082Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/156592 -> ciflow/inductor-perf-test-nightly-rocm/156592 2025-07-17T07:01:51.8135939Z * [new tag] ciflow/inductor-perf-test-nightly/156592 -> ciflow/inductor-perf-test-nightly/156592 2025-07-17T07:01:51.8136632Z * [new tag] ciflow/inductor-rocm/151845 -> ciflow/inductor-rocm/151845 2025-07-17T07:01:51.8137619Z * [new tag] ciflow/inductor-rocm/156192 -> ciflow/inductor-rocm/156192 2025-07-17T07:01:51.8138794Z * [new tag] ciflow/inductor-rocm/157191 -> ciflow/inductor-rocm/157191 2025-07-17T07:01:51.8139440Z * [new tag] ciflow/inductor-rocm/157520 -> ciflow/inductor-rocm/157520 2025-07-17T07:01:51.8140072Z * [new tag] ciflow/inductor-rocm/158074 -> ciflow/inductor-rocm/158074 2025-07-17T07:01:51.8140871Z * [new tag] ciflow/inductor-rocm/158102 -> ciflow/inductor-rocm/158102 2025-07-17T07:01:51.8141807Z * [new tag] ciflow/inductor-rocm/158103 -> ciflow/inductor-rocm/158103 2025-07-17T07:01:51.8142500Z * [new tag] ciflow/inductor-rocm/158459 -> ciflow/inductor-rocm/158459 2025-07-17T07:01:51.8143395Z * [new tag] ciflow/inductor/137400 -> ciflow/inductor/137400 2025-07-17T07:01:51.8143980Z * [new tag] ciflow/inductor/138214 -> ciflow/inductor/138214 2025-07-17T07:01:51.8144699Z * [new tag] ciflow/inductor/139561 -> ciflow/inductor/139561 2025-07-17T07:01:51.8145341Z * [new tag] ciflow/inductor/143712 -> ciflow/inductor/143712 2025-07-17T07:01:51.8146007Z * [new tag] ciflow/inductor/144516 -> ciflow/inductor/144516 2025-07-17T07:01:51.8146684Z * [new tag] ciflow/inductor/144556 -> ciflow/inductor/144556 2025-07-17T07:01:51.8147340Z * [new tag] ciflow/inductor/147470 -> ciflow/inductor/147470 2025-07-17T07:01:51.8148014Z * [new tag] ciflow/inductor/148180 -> ciflow/inductor/148180 2025-07-17T07:01:51.8148684Z * [new tag] ciflow/inductor/148328 -> ciflow/inductor/148328 2025-07-17T07:01:51.8149361Z * [new tag] ciflow/inductor/148484 -> ciflow/inductor/148484 2025-07-17T07:01:51.8150006Z * [new tag] ciflow/inductor/148492 -> ciflow/inductor/148492 2025-07-17T07:01:51.8151010Z * [new tag] ciflow/inductor/148569 -> ciflow/inductor/148569 2025-07-17T07:01:51.8151901Z * [new tag] ciflow/inductor/149003 -> ciflow/inductor/149003 2025-07-17T07:01:51.8152786Z * [new tag] ciflow/inductor/149961 -> ciflow/inductor/149961 2025-07-17T07:01:51.8153436Z * [new tag] ciflow/inductor/150762 -> ciflow/inductor/150762 2025-07-17T07:01:51.8154336Z * [new tag] ciflow/inductor/151777 -> ciflow/inductor/151777 2025-07-17T07:01:51.8155043Z * [new tag] ciflow/inductor/151845 -> ciflow/inductor/151845 2025-07-17T07:01:51.8155700Z * [new tag] ciflow/inductor/152624 -> ciflow/inductor/152624 2025-07-17T07:01:51.8156400Z * [new tag] ciflow/inductor/154149 -> ciflow/inductor/154149 2025-07-17T07:01:51.8157150Z * [new tag] ciflow/inductor/154199 -> ciflow/inductor/154199 2025-07-17T07:01:51.8157837Z * [new tag] ciflow/inductor/154551 -> ciflow/inductor/154551 2025-07-17T07:01:51.8158554Z * [new tag] ciflow/inductor/154694 -> ciflow/inductor/154694 2025-07-17T07:01:51.8159254Z * [new tag] ciflow/inductor/155452 -> ciflow/inductor/155452 2025-07-17T07:01:51.8159944Z * [new tag] ciflow/inductor/155608 -> ciflow/inductor/155608 2025-07-17T07:01:51.8160670Z * [new tag] ciflow/inductor/155877 -> ciflow/inductor/155877 2025-07-17T07:01:51.8161606Z * [new tag] ciflow/inductor/155958 -> ciflow/inductor/155958 2025-07-17T07:01:51.8162236Z * [new tag] ciflow/inductor/156049 -> ciflow/inductor/156049 2025-07-17T07:01:51.8162919Z * [new tag] ciflow/inductor/156118 -> ciflow/inductor/156118 2025-07-17T07:01:51.8163643Z * [new tag] ciflow/inductor/156141 -> ciflow/inductor/156141 2025-07-17T07:01:51.8164334Z * [new tag] ciflow/inductor/156175 -> ciflow/inductor/156175 2025-07-17T07:01:51.8165125Z * [new tag] ciflow/inductor/156192 -> ciflow/inductor/156192 2025-07-17T07:01:51.8166008Z * [new tag] ciflow/inductor/156296 -> ciflow/inductor/156296 2025-07-17T07:01:51.8166675Z * [new tag] ciflow/inductor/156369 -> ciflow/inductor/156369 2025-07-17T07:01:51.8167388Z * [new tag] ciflow/inductor/156370 -> ciflow/inductor/156370 2025-07-17T07:01:51.8168073Z * [new tag] ciflow/inductor/156371 -> ciflow/inductor/156371 2025-07-17T07:01:51.8168795Z * [new tag] ciflow/inductor/156416 -> ciflow/inductor/156416 2025-07-17T07:01:51.8170003Z * [new tag] ciflow/inductor/156460 -> ciflow/inductor/156460 2025-07-17T07:01:51.8170570Z * [new tag] ciflow/inductor/156592 -> ciflow/inductor/156592 2025-07-17T07:01:51.8171335Z * [new tag] ciflow/inductor/156605 -> ciflow/inductor/156605 2025-07-17T07:01:51.8172126Z * [new tag] ciflow/inductor/156626 -> ciflow/inductor/156626 2025-07-17T07:01:51.8172891Z * [new tag] ciflow/inductor/156652 -> ciflow/inductor/156652 2025-07-17T07:01:51.8173608Z * [new tag] ciflow/inductor/156781 -> ciflow/inductor/156781 2025-07-17T07:01:51.8174336Z * [new tag] ciflow/inductor/156851 -> ciflow/inductor/156851 2025-07-17T07:01:51.8175399Z * [new tag] ciflow/inductor/156874 -> ciflow/inductor/156874 2025-07-17T07:01:51.8176072Z * [new tag] ciflow/inductor/156977 -> ciflow/inductor/156977 2025-07-17T07:01:51.8176769Z * [new tag] ciflow/inductor/156980 -> ciflow/inductor/156980 2025-07-17T07:01:51.8177459Z * [new tag] ciflow/inductor/157152 -> ciflow/inductor/157152 2025-07-17T07:01:51.8178170Z * [new tag] ciflow/inductor/157298 -> ciflow/inductor/157298 2025-07-17T07:01:51.8178865Z * [new tag] ciflow/inductor/157520 -> ciflow/inductor/157520 2025-07-17T07:01:51.8179569Z * [new tag] ciflow/inductor/157572 -> ciflow/inductor/157572 2025-07-17T07:01:51.8180288Z * [new tag] ciflow/inductor/157580 -> ciflow/inductor/157580 2025-07-17T07:01:51.8180991Z * [new tag] ciflow/inductor/157594 -> ciflow/inductor/157594 2025-07-17T07:01:51.8182062Z * [new tag] ciflow/inductor/157633 -> ciflow/inductor/157633 2025-07-17T07:01:51.8182545Z * [new tag] ciflow/inductor/157635 -> ciflow/inductor/157635 2025-07-17T07:01:51.8183273Z * [new tag] ciflow/inductor/157685 -> ciflow/inductor/157685 2025-07-17T07:01:51.8184012Z * [new tag] ciflow/inductor/157686 -> ciflow/inductor/157686 2025-07-17T07:01:51.8184718Z * [new tag] ciflow/inductor/157689 -> ciflow/inductor/157689 2025-07-17T07:01:51.8185427Z * [new tag] ciflow/inductor/157699 -> ciflow/inductor/157699 2025-07-17T07:01:51.8186396Z * [new tag] ciflow/inductor/157743 -> ciflow/inductor/157743 2025-07-17T07:01:51.8187353Z * [new tag] ciflow/inductor/157748 -> ciflow/inductor/157748 2025-07-17T07:01:51.8187963Z * [new tag] ciflow/inductor/157804 -> ciflow/inductor/157804 2025-07-17T07:01:51.8188691Z * [new tag] ciflow/inductor/157822 -> ciflow/inductor/157822 2025-07-17T07:01:51.8189675Z * [new tag] ciflow/inductor/157854 -> ciflow/inductor/157854 2025-07-17T07:01:51.8190609Z * [new tag] ciflow/inductor/157887 -> ciflow/inductor/157887 2025-07-17T07:01:51.8191257Z * [new tag] ciflow/inductor/157902 -> ciflow/inductor/157902 2025-07-17T07:01:51.8191961Z * [new tag] ciflow/inductor/157927 -> ciflow/inductor/157927 2025-07-17T07:01:51.8192672Z * [new tag] ciflow/inductor/157944 -> ciflow/inductor/157944 2025-07-17T07:01:51.8193599Z * [new tag] ciflow/inductor/157951 -> ciflow/inductor/157951 2025-07-17T07:01:51.8194692Z * [new tag] ciflow/inductor/157954 -> ciflow/inductor/157954 2025-07-17T07:01:51.8195410Z * [new tag] ciflow/inductor/157967 -> ciflow/inductor/157967 2025-07-17T07:01:51.8196115Z * [new tag] ciflow/inductor/157969 -> ciflow/inductor/157969 2025-07-17T07:01:51.8196846Z * [new tag] ciflow/inductor/157971 -> ciflow/inductor/157971 2025-07-17T07:01:51.8197562Z * [new tag] ciflow/inductor/157979 -> ciflow/inductor/157979 2025-07-17T07:01:51.8198536Z * [new tag] ciflow/inductor/157982 -> ciflow/inductor/157982 2025-07-17T07:01:51.8199167Z * [new tag] ciflow/inductor/157993 -> ciflow/inductor/157993 2025-07-17T07:01:51.8199909Z * [new tag] ciflow/inductor/158004 -> ciflow/inductor/158004 2025-07-17T07:01:51.8200599Z * [new tag] ciflow/inductor/158011 -> ciflow/inductor/158011 2025-07-17T07:01:51.8201322Z * [new tag] ciflow/inductor/158015 -> ciflow/inductor/158015 2025-07-17T07:01:51.8202049Z * [new tag] ciflow/inductor/158046 -> ciflow/inductor/158046 2025-07-17T07:01:51.8203053Z * [new tag] ciflow/inductor/158047 -> ciflow/inductor/158047 2025-07-17T07:01:51.8203699Z * [new tag] ciflow/inductor/158048 -> ciflow/inductor/158048 2025-07-17T07:01:51.8204425Z * [new tag] ciflow/inductor/158049 -> ciflow/inductor/158049 2025-07-17T07:01:51.8205126Z * [new tag] ciflow/inductor/158061 -> ciflow/inductor/158061 2025-07-17T07:01:51.8205892Z * [new tag] ciflow/inductor/158062 -> ciflow/inductor/158062 2025-07-17T07:01:51.8206595Z * [new tag] ciflow/inductor/158072 -> ciflow/inductor/158072 2025-07-17T07:01:51.8207328Z * [new tag] ciflow/inductor/158074 -> ciflow/inductor/158074 2025-07-17T07:01:51.8208025Z * [new tag] ciflow/inductor/158075 -> ciflow/inductor/158075 2025-07-17T07:01:51.8208744Z * [new tag] ciflow/inductor/158084 -> ciflow/inductor/158084 2025-07-17T07:01:51.8209450Z * [new tag] ciflow/inductor/158091 -> ciflow/inductor/158091 2025-07-17T07:01:51.8210208Z * [new tag] ciflow/inductor/158097 -> ciflow/inductor/158097 2025-07-17T07:01:51.8210882Z * [new tag] ciflow/inductor/158098 -> ciflow/inductor/158098 2025-07-17T07:01:51.8211584Z * [new tag] ciflow/inductor/158104 -> ciflow/inductor/158104 2025-07-17T07:01:51.8212336Z * [new tag] ciflow/inductor/158119 -> ciflow/inductor/158119 2025-07-17T07:01:51.8213060Z * [new tag] ciflow/inductor/158156 -> ciflow/inductor/158156 2025-07-17T07:01:51.8213862Z * [new tag] ciflow/inductor/158171 -> ciflow/inductor/158171 2025-07-17T07:01:51.8214952Z * [new tag] ciflow/inductor/158174 -> ciflow/inductor/158174 2025-07-17T07:01:51.8215693Z * [new tag] ciflow/inductor/158188 -> ciflow/inductor/158188 2025-07-17T07:01:51.8216436Z * [new tag] ciflow/inductor/158193 -> ciflow/inductor/158193 2025-07-17T07:01:51.8217137Z * [new tag] ciflow/inductor/158211 -> ciflow/inductor/158211 2025-07-17T07:01:51.8217854Z * [new tag] ciflow/inductor/158223 -> ciflow/inductor/158223 2025-07-17T07:01:51.8219122Z * [new tag] ciflow/inductor/158237 -> ciflow/inductor/158237 2025-07-17T07:01:51.8219773Z * [new tag] ciflow/inductor/158259 -> ciflow/inductor/158259 2025-07-17T07:01:51.8220682Z * [new tag] ciflow/inductor/158284 -> ciflow/inductor/158284 2025-07-17T07:01:51.8221426Z * [new tag] ciflow/inductor/158289 -> ciflow/inductor/158289 2025-07-17T07:01:51.8222170Z * [new tag] ciflow/inductor/158311 -> ciflow/inductor/158311 2025-07-17T07:01:51.8223098Z * [new tag] ciflow/inductor/158312 -> ciflow/inductor/158312 2025-07-17T07:01:51.8223745Z * [new tag] ciflow/inductor/158318 -> ciflow/inductor/158318 2025-07-17T07:01:51.8224446Z * [new tag] ciflow/inductor/158321 -> ciflow/inductor/158321 2025-07-17T07:01:51.8225348Z * [new tag] ciflow/inductor/158338 -> ciflow/inductor/158338 2025-07-17T07:01:51.8225975Z * [new tag] ciflow/inductor/158349 -> ciflow/inductor/158349 2025-07-17T07:01:51.8226692Z * [new tag] ciflow/inductor/158350 -> ciflow/inductor/158350 2025-07-17T07:01:51.8227432Z * [new tag] ciflow/inductor/158351 -> ciflow/inductor/158351 2025-07-17T07:01:51.8228147Z * [new tag] ciflow/inductor/158360 -> ciflow/inductor/158360 2025-07-17T07:01:51.8228851Z * [new tag] ciflow/inductor/158361 -> ciflow/inductor/158361 2025-07-17T07:01:51.8229556Z * [new tag] ciflow/inductor/158363 -> ciflow/inductor/158363 2025-07-17T07:01:51.8230276Z * [new tag] ciflow/inductor/158368 -> ciflow/inductor/158368 2025-07-17T07:01:51.8230986Z * [new tag] ciflow/inductor/158377 -> ciflow/inductor/158377 2025-07-17T07:01:51.8231746Z * [new tag] ciflow/inductor/158379 -> ciflow/inductor/158379 2025-07-17T07:01:51.8232474Z * [new tag] ciflow/inductor/158381 -> ciflow/inductor/158381 2025-07-17T07:01:51.8233173Z * [new tag] ciflow/inductor/158391 -> ciflow/inductor/158391 2025-07-17T07:01:51.8234215Z * [new tag] ciflow/inductor/158394 -> ciflow/inductor/158394 2025-07-17T07:01:51.8234810Z * [new tag] ciflow/inductor/158397 -> ciflow/inductor/158397 2025-07-17T07:01:51.8235802Z * [new tag] ciflow/inductor/158400 -> ciflow/inductor/158400 2025-07-17T07:01:51.8236524Z * [new tag] ciflow/inductor/158404 -> ciflow/inductor/158404 2025-07-17T07:01:51.8237234Z * [new tag] ciflow/inductor/158406 -> ciflow/inductor/158406 2025-07-17T07:01:51.8237974Z * [new tag] ciflow/inductor/158410 -> ciflow/inductor/158410 2025-07-17T07:01:51.8238968Z * [new tag] ciflow/inductor/158418 -> ciflow/inductor/158418 2025-07-17T07:01:51.8239649Z * [new tag] ciflow/inductor/158424 -> ciflow/inductor/158424 2025-07-17T07:01:51.8240340Z * [new tag] ciflow/inductor/158426 -> ciflow/inductor/158426 2025-07-17T07:01:51.8241045Z * [new tag] ciflow/inductor/158427 -> ciflow/inductor/158427 2025-07-17T07:01:51.8242048Z * [new tag] ciflow/inductor/158430 -> ciflow/inductor/158430 2025-07-17T07:01:51.8242764Z * [new tag] ciflow/inductor/158435 -> ciflow/inductor/158435 2025-07-17T07:01:51.8243645Z * [new tag] ciflow/inductor/158442 -> ciflow/inductor/158442 2025-07-17T07:01:51.8244289Z * [new tag] ciflow/inductor/158449 -> ciflow/inductor/158449 2025-07-17T07:01:51.8245037Z * [new tag] ciflow/inductor/158456 -> ciflow/inductor/158456 2025-07-17T07:01:51.8245717Z * [new tag] ciflow/inductor/158458 -> ciflow/inductor/158458 2025-07-17T07:01:51.8246441Z * [new tag] ciflow/inductor/158459 -> ciflow/inductor/158459 2025-07-17T07:01:51.8247158Z * [new tag] ciflow/inductor/158460 -> ciflow/inductor/158460 2025-07-17T07:01:51.8247883Z * [new tag] ciflow/inductor/158462 -> ciflow/inductor/158462 2025-07-17T07:01:51.8248614Z * [new tag] ciflow/inductor/158467 -> ciflow/inductor/158467 2025-07-17T07:01:51.8249314Z * [new tag] ciflow/inductor/158468 -> ciflow/inductor/158468 2025-07-17T07:01:51.8250036Z * [new tag] ciflow/inductor/158476 -> ciflow/inductor/158476 2025-07-17T07:01:51.8251023Z * [new tag] ciflow/inductor/158479 -> ciflow/inductor/158479 2025-07-17T07:01:51.8251646Z * [new tag] ciflow/inductor/158480 -> ciflow/inductor/158480 2025-07-17T07:01:51.8252362Z * [new tag] ciflow/inductor/158485 -> ciflow/inductor/158485 2025-07-17T07:01:51.8253063Z * [new tag] ciflow/inductor/158490 -> ciflow/inductor/158490 2025-07-17T07:01:51.8253790Z * [new tag] ciflow/inductor/158492 -> ciflow/inductor/158492 2025-07-17T07:01:51.8256737Z * [new tag] ciflow/inductor/158494 -> ciflow/inductor/158494 2025-07-17T07:01:51.8257023Z * [new tag] ciflow/inductor/158495 -> ciflow/inductor/158495 2025-07-17T07:01:51.8257222Z * [new tag] ciflow/inductor/158498 -> ciflow/inductor/158498 2025-07-17T07:01:51.8257414Z * [new tag] ciflow/inductor/158499 -> ciflow/inductor/158499 2025-07-17T07:01:51.8257776Z * [new tag] ciflow/inductor/158500 -> ciflow/inductor/158500 2025-07-17T07:01:51.8258396Z * [new tag] ciflow/inductor/158501 -> ciflow/inductor/158501 2025-07-17T07:01:51.8259087Z * [new tag] ciflow/inductor/158504 -> ciflow/inductor/158504 2025-07-17T07:01:51.8259767Z * [new tag] ciflow/inductor/158509 -> ciflow/inductor/158509 2025-07-17T07:01:51.8260485Z * [new tag] ciflow/inductor/158520 -> ciflow/inductor/158520 2025-07-17T07:01:51.8261210Z * [new tag] ciflow/inductor/158524 -> ciflow/inductor/158524 2025-07-17T07:01:51.8262147Z * [new tag] ciflow/inductor/158525 -> ciflow/inductor/158525 2025-07-17T07:01:51.8262890Z * [new tag] ciflow/inductor/158526 -> ciflow/inductor/158526 2025-07-17T07:01:51.8263597Z * [new tag] ciflow/inductor/158528 -> ciflow/inductor/158528 2025-07-17T07:01:51.8264318Z * [new tag] ciflow/inductor/158534 -> ciflow/inductor/158534 2025-07-17T07:01:51.8265293Z * [new tag] ciflow/inductor/158535 -> ciflow/inductor/158535 2025-07-17T07:01:51.8265966Z * [new tag] ciflow/inductor/158537 -> ciflow/inductor/158537 2025-07-17T07:01:51.8266661Z * [new tag] ciflow/inductor/158538 -> ciflow/inductor/158538 2025-07-17T07:01:51.8267998Z * [new tag] ciflow/inductor/158543 -> ciflow/inductor/158543 2025-07-17T07:01:51.8268944Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-07-17T07:01:51.8269738Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-07-17T07:01:51.8270672Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-07-17T07:01:51.8271639Z * [new tag] ciflow/linux-aarch64/157520 -> ciflow/linux-aarch64/157520 2025-07-17T07:01:51.8272381Z * [new tag] ciflow/linux-aarch64/157782 -> ciflow/linux-aarch64/157782 2025-07-17T07:01:51.8273193Z * [new tag] ciflow/linux-aarch64/157994 -> ciflow/linux-aarch64/157994 2025-07-17T07:01:51.8273986Z * [new tag] ciflow/linux-aarch64/158445 -> ciflow/linux-aarch64/158445 2025-07-17T07:01:51.8274872Z * [new tag] ciflow/mps/155200 -> ciflow/mps/155200 2025-07-17T07:01:51.8275537Z * [new tag] ciflow/mps/157553 -> ciflow/mps/157553 2025-07-17T07:01:51.8276108Z * [new tag] ciflow/mps/157875 -> ciflow/mps/157875 2025-07-17T07:01:51.8276784Z * [new tag] ciflow/mps/157876 -> ciflow/mps/157876 2025-07-17T07:01:51.8277493Z * [new tag] ciflow/mps/158237 -> ciflow/mps/158237 2025-07-17T07:01:51.8278164Z * [new tag] ciflow/mps/158349 -> ciflow/mps/158349 2025-07-17T07:01:51.8278810Z * [new tag] ciflow/mps/158350 -> ciflow/mps/158350 2025-07-17T07:01:51.8279495Z * [new tag] ciflow/mps/158351 -> ciflow/mps/158351 2025-07-17T07:01:51.8280542Z * [new tag] ciflow/periodic-rocm-mi300/156192 -> ciflow/periodic-rocm-mi300/156192 2025-07-17T07:01:51.8281071Z * [new tag] ciflow/periodic-rocm-mi300/157191 -> ciflow/periodic-rocm-mi300/157191 2025-07-17T07:01:51.8281787Z * [new tag] ciflow/periodic-rocm-mi300/158102 -> ciflow/periodic-rocm-mi300/158102 2025-07-17T07:01:51.8282450Z * [new tag] ciflow/periodic-rocm-mi300/158103 -> ciflow/periodic-rocm-mi300/158103 2025-07-17T07:01:51.8283585Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-07-17T07:01:51.8284181Z * [new tag] ciflow/periodic/143959 -> ciflow/periodic/143959 2025-07-17T07:01:51.8285097Z * [new tag] ciflow/periodic/156559 -> ciflow/periodic/156559 2025-07-17T07:01:51.8285816Z * [new tag] ciflow/periodic/156900 -> ciflow/periodic/156900 2025-07-17T07:01:51.8286471Z * [new tag] ciflow/periodic/157748 -> ciflow/periodic/157748 2025-07-17T07:01:51.8287288Z * [new tag] ciflow/periodic/157939 -> ciflow/periodic/157939 2025-07-17T07:01:51.8288018Z * [new tag] ciflow/periodic/158145 -> ciflow/periodic/158145 2025-07-17T07:01:51.8289020Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-07-17T07:01:51.8289718Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-07-17T07:01:51.8290507Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-07-17T07:01:51.8291494Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-07-17T07:01:51.8292719Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-07-17T07:01:51.8293796Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-07-17T07:01:51.8294855Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-07-17T07:01:51.8295825Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-07-17T07:01:51.8296740Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-07-17T07:01:51.8297759Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-07-17T07:01:51.8298742Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-07-17T07:01:51.8299471Z * [new tag] ciflow/rocm-mi300/156192 -> ciflow/rocm-mi300/156192 2025-07-17T07:01:51.8300164Z * [new tag] ciflow/rocm-mi300/157191 -> ciflow/rocm-mi300/157191 2025-07-17T07:01:51.8300852Z * [new tag] ciflow/rocm-mi300/157520 -> ciflow/rocm-mi300/157520 2025-07-17T07:01:51.8301508Z * [new tag] ciflow/rocm-mi300/158102 -> ciflow/rocm-mi300/158102 2025-07-17T07:01:51.8302173Z * [new tag] ciflow/rocm-mi300/158103 -> ciflow/rocm-mi300/158103 2025-07-17T07:01:51.8303106Z * [new tag] ciflow/rocm-mi300/158221 -> ciflow/rocm-mi300/158221 2025-07-17T07:01:51.8303639Z * [new tag] ciflow/rocm-mi300/158459 -> ciflow/rocm-mi300/158459 2025-07-17T07:01:51.8304547Z * [new tag] ciflow/rocm/148492 -> ciflow/rocm/148492 2025-07-17T07:01:51.8305188Z * [new tag] ciflow/rocm/149601 -> ciflow/rocm/149601 2025-07-17T07:01:51.8305838Z * [new tag] ciflow/rocm/150312 -> ciflow/rocm/150312 2025-07-17T07:01:51.8306490Z * [new tag] ciflow/rocm/151845 -> ciflow/rocm/151845 2025-07-17T07:01:51.8307145Z * [new tag] ciflow/rocm/155200 -> ciflow/rocm/155200 2025-07-17T07:01:51.8307818Z * [new tag] ciflow/rocm/155877 -> ciflow/rocm/155877 2025-07-17T07:01:51.8308688Z * [new tag] ciflow/rocm/156165 -> ciflow/rocm/156165 2025-07-17T07:01:51.8309286Z * [new tag] ciflow/rocm/156192 -> ciflow/rocm/156192 2025-07-17T07:01:51.8309917Z * [new tag] ciflow/rocm/156592 -> ciflow/rocm/156592 2025-07-17T07:01:51.8310593Z * [new tag] ciflow/rocm/157520 -> ciflow/rocm/157520 2025-07-17T07:01:51.8311493Z * [new tag] ciflow/rocm/157964 -> ciflow/rocm/157964 2025-07-17T07:01:51.8312296Z * [new tag] ciflow/rocm/158037 -> ciflow/rocm/158037 2025-07-17T07:01:51.8313360Z * [new tag] ciflow/rocm/158074 -> ciflow/rocm/158074 2025-07-17T07:01:51.8314114Z * [new tag] ciflow/rocm/158219 -> ciflow/rocm/158219 2025-07-17T07:01:51.8314869Z * [new tag] ciflow/rocm/158220 -> ciflow/rocm/158220 2025-07-17T07:01:51.8315536Z * [new tag] ciflow/rocm/158224 -> ciflow/rocm/158224 2025-07-17T07:01:51.8316431Z * [new tag] ciflow/rocm/158271 -> ciflow/rocm/158271 2025-07-17T07:01:51.8317051Z * [new tag] ciflow/rocm/158408 -> ciflow/rocm/158408 2025-07-17T07:01:51.8317744Z * [new tag] ciflow/rocm/158459 -> ciflow/rocm/158459 2025-07-17T07:01:51.8318679Z * [new tag] ciflow/s390/143959 -> ciflow/s390/143959 2025-07-17T07:01:51.8319376Z * [new tag] ciflow/s390/151447 -> ciflow/s390/151447 2025-07-17T07:01:51.8320428Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-07-17T07:01:51.8321204Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-07-17T07:01:51.8322611Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-07-17T07:01:51.8323020Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-07-17T07:01:51.8323765Z * [new tag] ciflow/slow/157385 -> ciflow/slow/157385 2025-07-17T07:01:51.8324532Z * [new tag] ciflow/slow/157748 -> ciflow/slow/157748 2025-07-17T07:01:51.8325108Z * [new tag] ciflow/slow/158222 -> ciflow/slow/158222 2025-07-17T07:01:51.8325770Z * [new tag] ciflow/slow/158312 -> ciflow/slow/158312 2025-07-17T07:01:51.8326757Z * [new tag] ciflow/slow/158424 -> ciflow/slow/158424 2025-07-17T07:01:51.8327834Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-07-17T07:01:51.8328904Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-07-17T07:01:51.8329812Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-07-17T07:01:51.8330590Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-07-17T07:01:51.8331560Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-07-17T07:01:51.8332435Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-07-17T07:01:51.8333362Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-07-17T07:01:51.8334088Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-07-17T07:01:51.8335552Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-07-17T07:01:51.8336114Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-07-17T07:01:51.8337025Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-07-17T07:01:51.8337770Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-07-17T07:01:51.8338714Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-07-17T07:01:51.8339588Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-07-17T07:01:51.8340913Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-07-17T07:01:51.8341405Z * [new tag] ciflow/triton_binaries/158408 -> ciflow/triton_binaries/158408 2025-07-17T07:01:51.8342535Z * [new tag] ciflow/triton_binaries/158459 -> ciflow/triton_binaries/158459 2025-07-17T07:01:51.8343241Z * [new tag] ciflow/trunk/113258 -> ciflow/trunk/113258 2025-07-17T07:01:51.8343894Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-07-17T07:01:51.8344571Z * [new tag] ciflow/trunk/139971 -> ciflow/trunk/139971 2025-07-17T07:01:51.8345239Z * [new tag] ciflow/trunk/143712 -> ciflow/trunk/143712 2025-07-17T07:01:51.8345891Z * [new tag] ciflow/trunk/144557 -> ciflow/trunk/144557 2025-07-17T07:01:51.8346572Z * [new tag] ciflow/trunk/147470 -> ciflow/trunk/147470 2025-07-17T07:01:51.8347221Z * [new tag] ciflow/trunk/148180 -> ciflow/trunk/148180 2025-07-17T07:01:51.8347896Z * [new tag] ciflow/trunk/148328 -> ciflow/trunk/148328 2025-07-17T07:01:51.8349102Z * [new tag] ciflow/trunk/148492 -> ciflow/trunk/148492 2025-07-17T07:01:51.8349660Z * [new tag] ciflow/trunk/149003 -> ciflow/trunk/149003 2025-07-17T07:01:51.8350359Z * [new tag] ciflow/trunk/149601 -> ciflow/trunk/149601 2025-07-17T07:01:51.8351325Z * [new tag] ciflow/trunk/149961 -> ciflow/trunk/149961 2025-07-17T07:01:51.8352352Z * [new tag] ciflow/trunk/150312 -> ciflow/trunk/150312 2025-07-17T07:01:51.8353213Z * [new tag] ciflow/trunk/150691 -> ciflow/trunk/150691 2025-07-17T07:01:51.8353859Z * [new tag] ciflow/trunk/150762 -> ciflow/trunk/150762 2025-07-17T07:01:51.8354637Z * [new tag] ciflow/trunk/151777 -> ciflow/trunk/151777 2025-07-17T07:01:51.8355281Z * [new tag] ciflow/trunk/151845 -> ciflow/trunk/151845 2025-07-17T07:01:51.8355988Z * [new tag] ciflow/trunk/152624 -> ciflow/trunk/152624 2025-07-17T07:01:51.8356961Z * [new tag] ciflow/trunk/153666 -> ciflow/trunk/153666 2025-07-17T07:01:51.8357679Z * [new tag] ciflow/trunk/154149 -> ciflow/trunk/154149 2025-07-17T07:01:51.8358380Z * [new tag] ciflow/trunk/154199 -> ciflow/trunk/154199 2025-07-17T07:01:51.8359079Z * [new tag] ciflow/trunk/154694 -> ciflow/trunk/154694 2025-07-17T07:01:51.8359826Z * [new tag] ciflow/trunk/154983 -> ciflow/trunk/154983 2025-07-17T07:01:51.8360490Z * [new tag] ciflow/trunk/155489 -> ciflow/trunk/155489 2025-07-17T07:01:51.8361207Z * [new tag] ciflow/trunk/155958 -> ciflow/trunk/155958 2025-07-17T07:01:51.8361929Z * [new tag] ciflow/trunk/156049 -> ciflow/trunk/156049 2025-07-17T07:01:51.8362850Z * [new tag] ciflow/trunk/156097 -> ciflow/trunk/156097 2025-07-17T07:01:51.8363394Z * [new tag] ciflow/trunk/156141 -> ciflow/trunk/156141 2025-07-17T07:01:51.8364136Z * [new tag] ciflow/trunk/156165 -> ciflow/trunk/156165 2025-07-17T07:01:51.8364889Z * [new tag] ciflow/trunk/156175 -> ciflow/trunk/156175 2025-07-17T07:01:51.8365595Z * [new tag] ciflow/trunk/156192 -> ciflow/trunk/156192 2025-07-17T07:01:51.8366290Z * [new tag] ciflow/trunk/156296 -> ciflow/trunk/156296 2025-07-17T07:01:51.8366982Z * [new tag] ciflow/trunk/156370 -> ciflow/trunk/156370 2025-07-17T07:01:51.8367705Z * [new tag] ciflow/trunk/156559 -> ciflow/trunk/156559 2025-07-17T07:01:51.8368485Z * [new tag] ciflow/trunk/156605 -> ciflow/trunk/156605 2025-07-17T07:01:51.8369165Z * [new tag] ciflow/trunk/156626 -> ciflow/trunk/156626 2025-07-17T07:01:51.8370952Z * [new tag] ciflow/trunk/156666 -> ciflow/trunk/156666 2025-07-17T07:01:51.8371675Z * [new tag] ciflow/trunk/156753 -> ciflow/trunk/156753 2025-07-17T07:01:51.8375935Z * [new tag] ciflow/trunk/156781 -> ciflow/trunk/156781 2025-07-17T07:01:51.8376615Z * [new tag] ciflow/trunk/156874 -> ciflow/trunk/156874 2025-07-17T07:01:51.8377336Z * [new tag] ciflow/trunk/157199 -> ciflow/trunk/157199 2025-07-17T07:01:51.8378068Z * [new tag] ciflow/trunk/157432 -> ciflow/trunk/157432 2025-07-17T07:01:51.8378752Z * [new tag] ciflow/trunk/157520 -> ciflow/trunk/157520 2025-07-17T07:01:51.8379462Z * [new tag] ciflow/trunk/157550 -> ciflow/trunk/157550 2025-07-17T07:01:51.8380139Z * [new tag] ciflow/trunk/157552 -> ciflow/trunk/157552 2025-07-17T07:01:51.8380851Z * [new tag] ciflow/trunk/157580 -> ciflow/trunk/157580 2025-07-17T07:01:51.8381556Z * [new tag] ciflow/trunk/157685 -> ciflow/trunk/157685 2025-07-17T07:01:51.8382234Z * [new tag] ciflow/trunk/157689 -> ciflow/trunk/157689 2025-07-17T07:01:51.8382960Z * [new tag] ciflow/trunk/157699 -> ciflow/trunk/157699 2025-07-17T07:01:51.8383652Z * [new tag] ciflow/trunk/157748 -> ciflow/trunk/157748 2025-07-17T07:01:51.8384375Z * [new tag] ciflow/trunk/157791 -> ciflow/trunk/157791 2025-07-17T07:01:51.8385132Z * [new tag] ciflow/trunk/157804 -> ciflow/trunk/157804 2025-07-17T07:01:51.8385931Z * [new tag] ciflow/trunk/157887 -> ciflow/trunk/157887 2025-07-17T07:01:51.8386561Z * [new tag] ciflow/trunk/157908 -> ciflow/trunk/157908 2025-07-17T07:01:51.8387269Z * [new tag] ciflow/trunk/157910 -> ciflow/trunk/157910 2025-07-17T07:01:51.8388194Z * [new tag] ciflow/trunk/157935 -> ciflow/trunk/157935 2025-07-17T07:01:51.8388961Z * [new tag] ciflow/trunk/157963 -> ciflow/trunk/157963 2025-07-17T07:01:51.8389673Z * [new tag] ciflow/trunk/157994 -> ciflow/trunk/157994 2025-07-17T07:01:51.8390405Z * [new tag] ciflow/trunk/158015 -> ciflow/trunk/158015 2025-07-17T07:01:51.8391081Z * [new tag] ciflow/trunk/158037 -> ciflow/trunk/158037 2025-07-17T07:01:51.8391792Z * [new tag] ciflow/trunk/158062 -> ciflow/trunk/158062 2025-07-17T07:01:51.8392499Z * [new tag] ciflow/trunk/158072 -> ciflow/trunk/158072 2025-07-17T07:01:51.8393270Z * [new tag] ciflow/trunk/158091 -> ciflow/trunk/158091 2025-07-17T07:01:51.8393980Z * [new tag] ciflow/trunk/158104 -> ciflow/trunk/158104 2025-07-17T07:01:51.8394703Z * [new tag] ciflow/trunk/158119 -> ciflow/trunk/158119 2025-07-17T07:01:51.8395402Z * [new tag] ciflow/trunk/158148 -> ciflow/trunk/158148 2025-07-17T07:01:51.8396359Z * [new tag] ciflow/trunk/158185 -> ciflow/trunk/158185 2025-07-17T07:01:51.8397262Z * [new tag] ciflow/trunk/158203 -> ciflow/trunk/158203 2025-07-17T07:01:51.8397883Z * [new tag] ciflow/trunk/158219 -> ciflow/trunk/158219 2025-07-17T07:01:51.8398599Z * [new tag] ciflow/trunk/158220 -> ciflow/trunk/158220 2025-07-17T07:01:51.8399290Z * [new tag] ciflow/trunk/158222 -> ciflow/trunk/158222 2025-07-17T07:01:51.8400033Z * [new tag] ciflow/trunk/158223 -> ciflow/trunk/158223 2025-07-17T07:01:51.8401236Z * [new tag] ciflow/trunk/158224 -> ciflow/trunk/158224 2025-07-17T07:01:51.8402097Z * [new tag] ciflow/trunk/158229 -> ciflow/trunk/158229 2025-07-17T07:01:51.8402751Z * [new tag] ciflow/trunk/158249 -> ciflow/trunk/158249 2025-07-17T07:01:51.8403469Z * [new tag] ciflow/trunk/158259 -> ciflow/trunk/158259 2025-07-17T07:01:51.8404186Z * [new tag] ciflow/trunk/158289 -> ciflow/trunk/158289 2025-07-17T07:01:51.8404885Z * [new tag] ciflow/trunk/158312 -> ciflow/trunk/158312 2025-07-17T07:01:51.8405913Z * [new tag] ciflow/trunk/158323 -> ciflow/trunk/158323 2025-07-17T07:01:51.8406513Z * [new tag] ciflow/trunk/158356 -> ciflow/trunk/158356 2025-07-17T07:01:51.8407246Z * [new tag] ciflow/trunk/158363 -> ciflow/trunk/158363 2025-07-17T07:01:51.8407958Z * [new tag] ciflow/trunk/158368 -> ciflow/trunk/158368 2025-07-17T07:01:51.8408945Z * [new tag] ciflow/trunk/158373 -> ciflow/trunk/158373 2025-07-17T07:01:51.8409685Z * [new tag] ciflow/trunk/158377 -> ciflow/trunk/158377 2025-07-17T07:01:51.8410610Z * [new tag] ciflow/trunk/158380 -> ciflow/trunk/158380 2025-07-17T07:01:51.8411251Z * [new tag] ciflow/trunk/158400 -> ciflow/trunk/158400 2025-07-17T07:01:51.8411998Z * [new tag] ciflow/trunk/158418 -> ciflow/trunk/158418 2025-07-17T07:01:51.8412701Z * [new tag] ciflow/trunk/158424 -> ciflow/trunk/158424 2025-07-17T07:01:51.8413422Z * [new tag] ciflow/trunk/158430 -> ciflow/trunk/158430 2025-07-17T07:01:51.8414178Z * [new tag] ciflow/trunk/158442 -> ciflow/trunk/158442 2025-07-17T07:01:51.8414973Z * [new tag] ciflow/trunk/158458 -> ciflow/trunk/158458 2025-07-17T07:01:51.8415680Z * [new tag] ciflow/trunk/158459 -> ciflow/trunk/158459 2025-07-17T07:01:51.8416645Z * [new tag] ciflow/trunk/158473 -> ciflow/trunk/158473 2025-07-17T07:01:51.8417302Z * [new tag] ciflow/trunk/158479 -> ciflow/trunk/158479 2025-07-17T07:01:51.8418012Z * [new tag] ciflow/trunk/158485 -> ciflow/trunk/158485 2025-07-17T07:01:51.8418736Z * [new tag] ciflow/trunk/158492 -> ciflow/trunk/158492 2025-07-17T07:01:51.8419425Z * [new tag] ciflow/trunk/158524 -> ciflow/trunk/158524 2025-07-17T07:01:51.8420348Z * [new tag] ciflow/trunk/158532 -> ciflow/trunk/158532 2025-07-17T07:01:51.8420973Z * [new tag] ciflow/trunk/158543 -> ciflow/trunk/158543 2025-07-17T07:01:51.8422152Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-07-17T07:01:51.8422940Z * [new tag] ciflow/win-arm64/157935 -> ciflow/win-arm64/157935 2025-07-17T07:01:51.8423707Z * [new tag] ciflow/xpu/139971 -> ciflow/xpu/139971 2025-07-17T07:01:51.8424425Z * [new tag] ciflow/xpu/150218 -> ciflow/xpu/150218 2025-07-17T07:01:51.8425078Z * [new tag] ciflow/xpu/155200 -> ciflow/xpu/155200 2025-07-17T07:01:51.8425731Z * [new tag] ciflow/xpu/156812 -> ciflow/xpu/156812 2025-07-17T07:01:51.8426432Z * [new tag] ciflow/xpu/157699 -> ciflow/xpu/157699 2025-07-17T07:01:51.8427067Z * [new tag] ciflow/xpu/157954 -> ciflow/xpu/157954 2025-07-17T07:01:51.8427751Z * [new tag] ciflow/xpu/158336 -> ciflow/xpu/158336 2025-07-17T07:01:51.8428661Z * [new tag] ciflow/xpu/158337 -> ciflow/xpu/158337 2025-07-17T07:01:51.8429375Z * [new tag] ciflow/xpu/158340 -> ciflow/xpu/158340 2025-07-17T07:01:51.8430120Z * [new tag] ciflow/xpu/158533 -> ciflow/xpu/158533 2025-07-17T07:01:51.8430802Z * [new tag] ciflow/xpu/158542 -> ciflow/xpu/158542 2025-07-17T07:01:51.8431754Z * [new tag] cslpull75 -> cslpull75 2025-07-17T07:01:51.8432413Z * [new tag] cslpull76 -> cslpull76 2025-07-17T07:01:51.8433232Z * [new tag] cslpull77 -> cslpull77 2025-07-17T07:01:51.8434427Z * [new tag] cslpull78 -> cslpull78 2025-07-17T07:01:51.8435426Z * [new tag] cslpull79 -> cslpull79 2025-07-17T07:01:51.8436480Z * [new tag] cslpull80 -> cslpull80 2025-07-17T07:01:51.8437308Z * [new tag] cslpull81 -> cslpull81 2025-07-17T07:01:51.8438193Z * [new tag] cslpull82 -> cslpull82 2025-07-17T07:01:51.8438910Z * [new tag] cslpull83 -> cslpull83 2025-07-17T07:01:51.8439838Z * [new tag] cslpull84 -> cslpull84 2025-07-17T07:01:51.8440510Z * [new tag] cslpull85 -> cslpull85 2025-07-17T07:01:51.8441428Z * [new tag] cslpull86 -> cslpull86 2025-07-17T07:01:51.8442153Z * [new tag] cslpull87 -> cslpull87 2025-07-17T07:01:51.8443089Z * [new tag] cslpull88 -> cslpull88 2025-07-17T07:01:51.8443825Z * [new tag] cslpull89 -> cslpull89 2025-07-17T07:01:51.8444488Z * [new tag] cslpull90 -> cslpull90 2025-07-17T07:01:51.8445744Z * [new tag] cslpull91 -> cslpull91 2025-07-17T07:01:51.8446509Z * [new tag] cslpull92 -> cslpull92 2025-07-17T07:01:51.8447340Z * [new tag] flight_5 -> flight_5 2025-07-17T07:01:51.8448246Z * [new tag] flight_5.1 -> flight_5.1 2025-07-17T07:01:51.8448995Z * [new tag] flight_5.2 -> flight_5.2 2025-07-17T07:01:51.8449749Z * [new tag] flight_5.3 -> flight_5.3 2025-07-17T07:01:51.8450594Z * [new tag] forpull1 -> forpull1 2025-07-17T07:01:51.8451601Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-07-17T07:01:51.8452344Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-07-17T07:01:51.8453240Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-07-17T07:01:51.8454146Z * [new tag] nightly-binary -> nightly-binary 2025-07-17T07:01:51.8454697Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-07-17T07:01:51.8455864Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-07-17T07:01:51.8457067Z * [new tag] trunk/0083032e7559dc8f02483ba60373adfcdaf9dae6 -> trunk/0083032e7559dc8f02483ba60373adfcdaf9dae6 2025-07-17T07:01:51.8458189Z * [new tag] trunk/008345be9d0c32f67459bcf3e6705be43d496f74 -> trunk/008345be9d0c32f67459bcf3e6705be43d496f74 2025-07-17T07:01:51.8459179Z * [new tag] trunk/00ae620b9f72cc751672c2fd92a04fc86704a87a -> trunk/00ae620b9f72cc751672c2fd92a04fc86704a87a 2025-07-17T07:01:51.8460018Z * [new tag] trunk/0105cd89ab508ec56126c1de85c8f5b5acc446b5 -> trunk/0105cd89ab508ec56126c1de85c8f5b5acc446b5 2025-07-17T07:01:51.8460904Z * [new tag] trunk/011026205a9d4c38458130f8ca242028f6184bf0 -> trunk/011026205a9d4c38458130f8ca242028f6184bf0 2025-07-17T07:01:51.8461887Z * [new tag] trunk/013cf1e3302d27de36588cf7a7130d76a5686bad -> trunk/013cf1e3302d27de36588cf7a7130d76a5686bad 2025-07-17T07:01:51.8462727Z * [new tag] trunk/013dfeabb405274546f19637c04f8a5f75923316 -> trunk/013dfeabb405274546f19637c04f8a5f75923316 2025-07-17T07:01:51.8463575Z * [new tag] trunk/018e9826a2ed09cfcf7424e0b3215bef5ad6499e -> trunk/018e9826a2ed09cfcf7424e0b3215bef5ad6499e 2025-07-17T07:01:51.8464429Z * [new tag] trunk/019e30e3b80d091d64253df4cdd149713e3e910e -> trunk/019e30e3b80d091d64253df4cdd149713e3e910e 2025-07-17T07:01:51.8465239Z * [new tag] trunk/01b0f09931d47bd2716398a0c335b2807dc3074d -> trunk/01b0f09931d47bd2716398a0c335b2807dc3074d 2025-07-17T07:01:51.8466081Z * [new tag] trunk/01b8f5e685fdb34aa50b73a84a55fc285e6b904a -> trunk/01b8f5e685fdb34aa50b73a84a55fc285e6b904a 2025-07-17T07:01:51.8467001Z * [new tag] trunk/02080c2cd94b37ad92e3d8803bc773f6abd2ae2f -> trunk/02080c2cd94b37ad92e3d8803bc773f6abd2ae2f 2025-07-17T07:01:51.8467791Z * [new tag] trunk/020da744370f6ee23e377357e9acc330b5610a67 -> trunk/020da744370f6ee23e377357e9acc330b5610a67 2025-07-17T07:01:51.8468633Z * [new tag] trunk/023887fc5af5a7273d7eb0388fdf1f0e87eafb28 -> trunk/023887fc5af5a7273d7eb0388fdf1f0e87eafb28 2025-07-17T07:01:51.8469478Z * [new tag] trunk/02608e560a1d7b669ad450a89542f7e9ba068658 -> trunk/02608e560a1d7b669ad450a89542f7e9ba068658 2025-07-17T07:01:51.8470307Z * [new tag] trunk/02715d0876bc66f46b9c7b4f277bedf8e251cc82 -> trunk/02715d0876bc66f46b9c7b4f277bedf8e251cc82 2025-07-17T07:01:51.8471598Z * [new tag] trunk/02724b5f649b93ef7960962bdde7a667c0893d21 -> trunk/02724b5f649b93ef7960962bdde7a667c0893d21 2025-07-17T07:01:51.8472426Z * [new tag] trunk/029e2b05c225588098d3eba445fd04189691f77d -> trunk/029e2b05c225588098d3eba445fd04189691f77d 2025-07-17T07:01:51.8473486Z * [new tag] trunk/02a9d9095f397f56fe64ef07ced8e7ffed6dcba1 -> trunk/02a9d9095f397f56fe64ef07ced8e7ffed6dcba1 2025-07-17T07:01:51.8474275Z * [new tag] trunk/02c7ab2f9baac05bd199392b70bc016d55f99b13 -> trunk/02c7ab2f9baac05bd199392b70bc016d55f99b13 2025-07-17T07:01:51.8474942Z * [new tag] trunk/03023f178c611249d41c62369ba95fe54316fe90 -> trunk/03023f178c611249d41c62369ba95fe54316fe90 2025-07-17T07:01:51.8476283Z * [new tag] trunk/03488d820c292b8ec4bfd9a4e25d5f28068c9375 -> trunk/03488d820c292b8ec4bfd9a4e25d5f28068c9375 2025-07-17T07:01:51.8477064Z * [new tag] trunk/034a7f6437bec853a938dbc19b549b9319b67f69 -> trunk/034a7f6437bec853a938dbc19b549b9319b67f69 2025-07-17T07:01:51.8477775Z * [new tag] trunk/0364db7cd14ffa67b48ef8c27fefbb3eed2b065d -> trunk/0364db7cd14ffa67b48ef8c27fefbb3eed2b065d 2025-07-17T07:01:51.8478610Z * [new tag] trunk/03852ddc22350eb8b6ed6b61777639ce6080f3dc -> trunk/03852ddc22350eb8b6ed6b61777639ce6080f3dc 2025-07-17T07:01:51.8479466Z * [new tag] trunk/039a1ce0ebbeb0adacbf9537c4085d977dba150a -> trunk/039a1ce0ebbeb0adacbf9537c4085d977dba150a 2025-07-17T07:01:51.8480323Z * [new tag] trunk/03b307575a98dc1d953c9d3521a9489e0e61e70c -> trunk/03b307575a98dc1d953c9d3521a9489e0e61e70c 2025-07-17T07:01:51.8481203Z * [new tag] trunk/04178d347c6baec5613f8063a2c77cf6f9657ee2 -> trunk/04178d347c6baec5613f8063a2c77cf6f9657ee2 2025-07-17T07:01:51.8482103Z * [new tag] trunk/04349f9ee541c7d07cc057bbe739f46bd4c30dcc -> trunk/04349f9ee541c7d07cc057bbe739f46bd4c30dcc 2025-07-17T07:01:51.8483040Z * [new tag] trunk/049dc48d1edf49c26d493fdb271fe5e5adc9c985 -> trunk/049dc48d1edf49c26d493fdb271fe5e5adc9c985 2025-07-17T07:01:51.8483913Z * [new tag] trunk/04b91a9e43518fcd34a474a832bac05d4583fcc3 -> trunk/04b91a9e43518fcd34a474a832bac05d4583fcc3 2025-07-17T07:01:51.8484800Z * [new tag] trunk/04bd7e6850e8efec77994963ffee87549555b9c3 -> trunk/04bd7e6850e8efec77994963ffee87549555b9c3 2025-07-17T07:01:51.8485682Z * [new tag] trunk/04cf2c9d24b7d9e7f7cdd4cf98c8bd0908c38110 -> trunk/04cf2c9d24b7d9e7f7cdd4cf98c8bd0908c38110 2025-07-17T07:01:51.8486598Z * [new tag] trunk/0504480f37714a289b2ba32c9cf32a5e50e86d38 -> trunk/0504480f37714a289b2ba32c9cf32a5e50e86d38 2025-07-17T07:01:51.8487558Z * [new tag] trunk/054cd4ca28d17507df46054fe355c697f6a07ce8 -> trunk/054cd4ca28d17507df46054fe355c697f6a07ce8 2025-07-17T07:01:51.8488490Z * [new tag] trunk/058fb1790f2c474cd4ecb5ec625eef896c554544 -> trunk/058fb1790f2c474cd4ecb5ec625eef896c554544 2025-07-17T07:01:51.8489256Z * [new tag] trunk/0596323c35d245099a4f4d3172b0efa20a639c84 -> trunk/0596323c35d245099a4f4d3172b0efa20a639c84 2025-07-17T07:01:51.8490107Z * [new tag] trunk/05d7288e316ae5c9c661c4529f9f130a46263e5b -> trunk/05d7288e316ae5c9c661c4529f9f130a46263e5b 2025-07-17T07:01:51.8491061Z * [new tag] trunk/05dd638ee98b36254c84095894c36fd0e7d95544 -> trunk/05dd638ee98b36254c84095894c36fd0e7d95544 2025-07-17T07:01:51.8491921Z * [new tag] trunk/05dfd312cfbfdecc6cb1e7d1d0bb4ee18370ae7e -> trunk/05dfd312cfbfdecc6cb1e7d1d0bb4ee18370ae7e 2025-07-17T07:01:51.8492625Z * [new tag] trunk/05faba40287cf7d8734da96cb2e904f39710bf29 -> trunk/05faba40287cf7d8734da96cb2e904f39710bf29 2025-07-17T07:01:51.8494699Z * [new tag] trunk/060838c2312ad207c7afe2c86f8a484afea5f328 -> trunk/060838c2312ad207c7afe2c86f8a484afea5f328 2025-07-17T07:01:51.8495509Z * [new tag] trunk/0629dfb860b00a10550d91dd395968f663f45fdd -> trunk/0629dfb860b00a10550d91dd395968f663f45fdd 2025-07-17T07:01:51.8496433Z * [new tag] trunk/06408dae49d06b6146fdd9d7a37eb5dde4f5e78d -> trunk/06408dae49d06b6146fdd9d7a37eb5dde4f5e78d 2025-07-17T07:01:51.8497348Z * [new tag] trunk/0640cfa38c1426a41ab4a0b3e3dab7c730cdc2ad -> trunk/0640cfa38c1426a41ab4a0b3e3dab7c730cdc2ad 2025-07-17T07:01:51.8498207Z * [new tag] trunk/064288cbab94c9931ca2296a2b9723e864f9050a -> trunk/064288cbab94c9931ca2296a2b9723e864f9050a 2025-07-17T07:01:51.8498991Z * [new tag] trunk/064a7db7fc3b06eb8e14d6c7d767f87fcf396ab6 -> trunk/064a7db7fc3b06eb8e14d6c7d767f87fcf396ab6 2025-07-17T07:01:51.8499855Z * [new tag] trunk/067fd0b3abd28f1b7dae883ffec5bc7459d35970 -> trunk/067fd0b3abd28f1b7dae883ffec5bc7459d35970 2025-07-17T07:01:51.8500668Z * [new tag] trunk/06930706a195144cd6172a5f2eb505cec56e9ec3 -> trunk/06930706a195144cd6172a5f2eb505cec56e9ec3 2025-07-17T07:01:51.8501608Z * [new tag] trunk/06a40b685056f60c4fbdc10d09a85b9fc3fca34f -> trunk/06a40b685056f60c4fbdc10d09a85b9fc3fca34f 2025-07-17T07:01:51.8502465Z * [new tag] trunk/06a67a8948dac9d02f22b3e2591a43b60981cdb4 -> trunk/06a67a8948dac9d02f22b3e2591a43b60981cdb4 2025-07-17T07:01:51.8503333Z * [new tag] trunk/06b3265cb1a27ea3090dc1fc86a6ce17518f989c -> trunk/06b3265cb1a27ea3090dc1fc86a6ce17518f989c 2025-07-17T07:01:51.8504153Z * [new tag] trunk/06f39a71b6cd14e0844b5eb1e0e085f7ae78d221 -> trunk/06f39a71b6cd14e0844b5eb1e0e085f7ae78d221 2025-07-17T07:01:51.8504902Z * [new tag] trunk/070aa59e491aa6987459cd1941ef647f2ad13858 -> trunk/070aa59e491aa6987459cd1941ef647f2ad13858 2025-07-17T07:01:51.8505752Z * [new tag] trunk/070e580d301efd626e327d1f88e0921b476d0f30 -> trunk/070e580d301efd626e327d1f88e0921b476d0f30 2025-07-17T07:01:51.8506593Z * [new tag] trunk/0756ebcd4890b19072e586fb2cbd2b25a80f91f4 -> trunk/0756ebcd4890b19072e586fb2cbd2b25a80f91f4 2025-07-17T07:01:51.8507434Z * [new tag] trunk/0797b2b6a80cf70a7accc3d5413186e7693d4451 -> trunk/0797b2b6a80cf70a7accc3d5413186e7693d4451 2025-07-17T07:01:51.8508271Z * [new tag] trunk/07bb097698223fe42d54b019638dd026c75f09a9 -> trunk/07bb097698223fe42d54b019638dd026c75f09a9 2025-07-17T07:01:51.8509100Z * [new tag] trunk/07da8a469bdd69966e1f832e3d09d991798f854c -> trunk/07da8a469bdd69966e1f832e3d09d991798f854c 2025-07-17T07:01:51.8509963Z * [new tag] trunk/07e340e29ccbe18ef59e8f60ec3f03ccc44e5e9c -> trunk/07e340e29ccbe18ef59e8f60ec3f03ccc44e5e9c 2025-07-17T07:01:51.8511066Z * [new tag] trunk/07eb374e7eefaa84a997d0adaaeecb4706d60e74 -> trunk/07eb374e7eefaa84a997d0adaaeecb4706d60e74 2025-07-17T07:01:51.8511843Z * [new tag] trunk/08274640028f68418d8f889d01af577b90ac2ec1 -> trunk/08274640028f68418d8f889d01af577b90ac2ec1 2025-07-17T07:01:51.8512749Z * [new tag] trunk/085f270a00b4452bbb005d6b3d448e9d0b9d6fa0 -> trunk/085f270a00b4452bbb005d6b3d448e9d0b9d6fa0 2025-07-17T07:01:51.8513624Z * [new tag] trunk/0860606729d16b9855157404da9b0d4654f3c626 -> trunk/0860606729d16b9855157404da9b0d4654f3c626 2025-07-17T07:01:51.8514330Z * [new tag] trunk/08799217aeb17128d89d675ce5b537761286417a -> trunk/08799217aeb17128d89d675ce5b537761286417a 2025-07-17T07:01:51.8515435Z * [new tag] trunk/08d15d3ec15b9b3fce81713cde20901806ecef42 -> trunk/08d15d3ec15b9b3fce81713cde20901806ecef42 2025-07-17T07:01:51.8516144Z * [new tag] trunk/08dae945ae380d80efbaf140a95abfc5d96e5100 -> trunk/08dae945ae380d80efbaf140a95abfc5d96e5100 2025-07-17T07:01:51.8516979Z * [new tag] trunk/08e9dd280f497fc820e35c458c843dc44f0282c6 -> trunk/08e9dd280f497fc820e35c458c843dc44f0282c6 2025-07-17T07:01:51.8517848Z * [new tag] trunk/092aed1b18c31042d7772a6f9ec1959df4bf5c3c -> trunk/092aed1b18c31042d7772a6f9ec1959df4bf5c3c 2025-07-17T07:01:51.8518750Z * [new tag] trunk/09328eb02f5412d2211b5fd638ce82d0e03b9c1f -> trunk/09328eb02f5412d2211b5fd638ce82d0e03b9c1f 2025-07-17T07:01:51.8519591Z * [new tag] trunk/0935a97d959918727700ae2c1ebeadc5b12a2b6c -> trunk/0935a97d959918727700ae2c1ebeadc5b12a2b6c 2025-07-17T07:01:51.8520381Z * [new tag] trunk/093aaccae254355b3637ef8760e762a3f9c3783c -> trunk/093aaccae254355b3637ef8760e762a3f9c3783c 2025-07-17T07:01:51.8521163Z * [new tag] trunk/093fd47dbef4f08691210de6c08cf269f28e4de5 -> trunk/093fd47dbef4f08691210de6c08cf269f28e4de5 2025-07-17T07:01:51.8521981Z * [new tag] trunk/099d0d6121125062ebc05771c8330cb7cd8d053a -> trunk/099d0d6121125062ebc05771c8330cb7cd8d053a 2025-07-17T07:01:51.8522807Z * [new tag] trunk/09db3a22e8783c4841697317688ba9467c7cc457 -> trunk/09db3a22e8783c4841697317688ba9467c7cc457 2025-07-17T07:01:51.8523776Z * [new tag] trunk/09ffba3cf79eeedf48a0fb39a26dcc0294872480 -> trunk/09ffba3cf79eeedf48a0fb39a26dcc0294872480 2025-07-17T07:01:51.8524660Z * [new tag] trunk/0a0023d9840570938a10472418eaac85098ec41b -> trunk/0a0023d9840570938a10472418eaac85098ec41b 2025-07-17T07:01:51.8525521Z * [new tag] trunk/0a092c7de67767eed3adb7abc0d49dfd3d26d0cd -> trunk/0a092c7de67767eed3adb7abc0d49dfd3d26d0cd 2025-07-17T07:01:51.8526429Z * [new tag] trunk/0a16818d5b3fdf0fb8148dd6b849687251a56376 -> trunk/0a16818d5b3fdf0fb8148dd6b849687251a56376 2025-07-17T07:01:51.8527237Z * [new tag] trunk/0a2ec7681d2af973d8daaf7905431a088739dc90 -> trunk/0a2ec7681d2af973d8daaf7905431a088739dc90 2025-07-17T07:01:51.8528115Z * [new tag] trunk/0a624c2dc5675e35917976467a8c533baf2a98a0 -> trunk/0a624c2dc5675e35917976467a8c533baf2a98a0 2025-07-17T07:01:51.8528955Z * [new tag] trunk/0a63053fe90405cc6ee5e42fa007e32ed1e2cbac -> trunk/0a63053fe90405cc6ee5e42fa007e32ed1e2cbac 2025-07-17T07:01:51.8529693Z * [new tag] trunk/0a6b66c881cba3f6a6c1a3cb8ddf698846d99822 -> trunk/0a6b66c881cba3f6a6c1a3cb8ddf698846d99822 2025-07-17T07:01:51.8530636Z * [new tag] trunk/0a99b026d6bd0f67dc2c0a20fe3228ddc4144854 -> trunk/0a99b026d6bd0f67dc2c0a20fe3228ddc4144854 2025-07-17T07:01:51.8531470Z * [new tag] trunk/0a9d450168ce58b2bb7f2cedc27a61012123564f -> trunk/0a9d450168ce58b2bb7f2cedc27a61012123564f 2025-07-17T07:01:51.8532797Z * [new tag] trunk/0ab075a69e4577a60c4dcbff7bcc2ecd0a15ce46 -> trunk/0ab075a69e4577a60c4dcbff7bcc2ecd0a15ce46 2025-07-17T07:01:51.8533557Z * [new tag] trunk/0ad88a2224e9f0a2ed4f405e827a745945d505ce -> trunk/0ad88a2224e9f0a2ed4f405e827a745945d505ce 2025-07-17T07:01:51.8534488Z * [new tag] trunk/0aed855b2bde6d9bd045bb20cc24544a9f2fb72b -> trunk/0aed855b2bde6d9bd045bb20cc24544a9f2fb72b 2025-07-17T07:01:51.8535390Z * [new tag] trunk/0b19d463d963a0b2ee5558d2c0bb79b2cbff6e64 -> trunk/0b19d463d963a0b2ee5558d2c0bb79b2cbff6e64 2025-07-17T07:01:51.8536294Z * [new tag] trunk/0b62465b99b23cb4afcd07424676cce34a676041 -> trunk/0b62465b99b23cb4afcd07424676cce34a676041 2025-07-17T07:01:51.8537184Z * [new tag] trunk/0b677560e686d828f9f5ed1cb87b1d7a54ee2781 -> trunk/0b677560e686d828f9f5ed1cb87b1d7a54ee2781 2025-07-17T07:01:51.8538116Z * [new tag] trunk/0b6c0898e6c352c8ea93daec854e704b41485375 -> trunk/0b6c0898e6c352c8ea93daec854e704b41485375 2025-07-17T07:01:51.8538890Z * [new tag] trunk/0bb733ba230051301b3fb3fa49d1d6662744b395 -> trunk/0bb733ba230051301b3fb3fa49d1d6662744b395 2025-07-17T07:01:51.8539771Z * [new tag] trunk/0bce39026917d10995ff47352c3da9fff0ec31ff -> trunk/0bce39026917d10995ff47352c3da9fff0ec31ff 2025-07-17T07:01:51.8540901Z * [new tag] trunk/0c58bdd8fb5f269aef100af8e2c43cfcf5f1f9dd -> trunk/0c58bdd8fb5f269aef100af8e2c43cfcf5f1f9dd 2025-07-17T07:01:51.8541703Z * [new tag] trunk/0ca2a79f5b80c1ec8be95e6f7de4182dd90f3502 -> trunk/0ca2a79f5b80c1ec8be95e6f7de4182dd90f3502 2025-07-17T07:01:51.8542532Z * [new tag] trunk/0cb36e2d62c811fcddea4c6d28b1c65246cdd160 -> trunk/0cb36e2d62c811fcddea4c6d28b1c65246cdd160 2025-07-17T07:01:51.8543499Z * [new tag] trunk/0cb85c188f3665c23766cda25c8169f1a436d36b -> trunk/0cb85c188f3665c23766cda25c8169f1a436d36b 2025-07-17T07:01:51.8544435Z * [new tag] trunk/0d01bafc34fc99a0b3e46cbf1ecfd8f97563efa6 -> trunk/0d01bafc34fc99a0b3e46cbf1ecfd8f97563efa6 2025-07-17T07:01:51.8545041Z * [new tag] trunk/0d17029fea3d96bb88b19912946648b47f8e003d -> trunk/0d17029fea3d96bb88b19912946648b47f8e003d 2025-07-17T07:01:51.8545942Z * [new tag] trunk/0d3d84d866040fad1b21a618c44802951a3cb15e -> trunk/0d3d84d866040fad1b21a618c44802951a3cb15e 2025-07-17T07:01:51.8546858Z * [new tag] trunk/0d77364ee3ff6435aa93df3b0303db66b5fc3254 -> trunk/0d77364ee3ff6435aa93df3b0303db66b5fc3254 2025-07-17T07:01:51.8547720Z * [new tag] trunk/0d8c029584b61da059b6225537764eadd2fa1223 -> trunk/0d8c029584b61da059b6225537764eadd2fa1223 2025-07-17T07:01:51.8548538Z * [new tag] trunk/0d8e4e2327137b370d91bd23931cd7691a95cc33 -> trunk/0d8e4e2327137b370d91bd23931cd7691a95cc33 2025-07-17T07:01:51.8549426Z * [new tag] trunk/0db3e0cf29604dae1007a678603e4dffd1c57562 -> trunk/0db3e0cf29604dae1007a678603e4dffd1c57562 2025-07-17T07:01:51.8550232Z * [new tag] trunk/0decd966af9cdcb7ab4410cf475d2fc09f2dea0c -> trunk/0decd966af9cdcb7ab4410cf475d2fc09f2dea0c 2025-07-17T07:01:51.8551066Z * [new tag] trunk/0e2013a12da944930012265bd1b75d0a19af4d7c -> trunk/0e2013a12da944930012265bd1b75d0a19af4d7c 2025-07-17T07:01:51.8551967Z * [new tag] trunk/0e47312ae5a687f0aed61db753d03180118cddc4 -> trunk/0e47312ae5a687f0aed61db753d03180118cddc4 2025-07-17T07:01:51.8552791Z * [new tag] trunk/0e9d8032a3431505c8a6a341375af9cb27afd537 -> trunk/0e9d8032a3431505c8a6a341375af9cb27afd537 2025-07-17T07:01:51.8553660Z * [new tag] trunk/0edc1b91f708c7635cf0f286f43724665934dc89 -> trunk/0edc1b91f708c7635cf0f286f43724665934dc89 2025-07-17T07:01:51.8554463Z * [new tag] trunk/0f0c01071476145fca7d70c68d029a88aeefe72c -> trunk/0f0c01071476145fca7d70c68d029a88aeefe72c 2025-07-17T07:01:51.8555305Z * [new tag] trunk/0f21fa84fb605c61482e4218df89f8bb1ef70c14 -> trunk/0f21fa84fb605c61482e4218df89f8bb1ef70c14 2025-07-17T07:01:51.8556116Z * [new tag] trunk/0f31445139d7ffaeeae95a230c536ef97970af5c -> trunk/0f31445139d7ffaeeae95a230c536ef97970af5c 2025-07-17T07:01:51.8556826Z * [new tag] trunk/0f47e76937f092263dd579e71d45e3193fbbc5df -> trunk/0f47e76937f092263dd579e71d45e3193fbbc5df 2025-07-17T07:01:51.8557570Z * [new tag] trunk/0f9c1b374fbb6f3b999855b26ebb869387b33dfb -> trunk/0f9c1b374fbb6f3b999855b26ebb869387b33dfb 2025-07-17T07:01:51.8558481Z * [new tag] trunk/0fa361e4295bbd8ac8b1f1ea74852535326f08f2 -> trunk/0fa361e4295bbd8ac8b1f1ea74852535326f08f2 2025-07-17T07:01:51.8559346Z * [new tag] trunk/0fd711df19a6523c9de6c820640ae53e32de14c6 -> trunk/0fd711df19a6523c9de6c820640ae53e32de14c6 2025-07-17T07:01:51.8560201Z * [new tag] trunk/1036f6d114bc22a9b4cf620cf7f8364ea2fd7a60 -> trunk/1036f6d114bc22a9b4cf620cf7f8364ea2fd7a60 2025-07-17T07:01:51.8561172Z * [new tag] trunk/104493487852f0cc232547ecbf7bebf5c7fe5982 -> trunk/104493487852f0cc232547ecbf7bebf5c7fe5982 2025-07-17T07:01:51.8561860Z * [new tag] trunk/1051b93192710d2e4bdf5fecd8e66c61731b55e1 -> trunk/1051b93192710d2e4bdf5fecd8e66c61731b55e1 2025-07-17T07:01:51.8562791Z * [new tag] trunk/10cd1de5183e4fcd6e316be497904b9c86d4db67 -> trunk/10cd1de5183e4fcd6e316be497904b9c86d4db67 2025-07-17T07:01:51.8563672Z * [new tag] trunk/10cef1e25d39c1ff1ac93036302ce1b008cd4469 -> trunk/10cef1e25d39c1ff1ac93036302ce1b008cd4469 2025-07-17T07:01:51.8564537Z * [new tag] trunk/10d41c7d20747829a4ee5b994bf466071e4c7e32 -> trunk/10d41c7d20747829a4ee5b994bf466071e4c7e32 2025-07-17T07:01:51.8565441Z * [new tag] trunk/10fb98a004e129ea2a2074cc05e5173c056f728c -> trunk/10fb98a004e129ea2a2074cc05e5173c056f728c 2025-07-17T07:01:51.8566215Z * [new tag] trunk/110ae0f4333b289b1ee798ab93447e6ede16300f -> trunk/110ae0f4333b289b1ee798ab93447e6ede16300f 2025-07-17T07:01:51.8567149Z * [new tag] trunk/1155c53e7d8c25dabc618d7f7b324ee97c89f40e -> trunk/1155c53e7d8c25dabc618d7f7b324ee97c89f40e 2025-07-17T07:01:51.8568039Z * [new tag] trunk/117db5601d78cbc746b35eef71fc815e042e903f -> trunk/117db5601d78cbc746b35eef71fc815e042e903f 2025-07-17T07:01:51.8568815Z * [new tag] trunk/11a86ad2fa3be5cf91a7a2b99c4df44017bc92c7 -> trunk/11a86ad2fa3be5cf91a7a2b99c4df44017bc92c7 2025-07-17T07:01:51.8569655Z * [new tag] trunk/11bb1ece50ade57b963afe29b27cab6a0a56ff7d -> trunk/11bb1ece50ade57b963afe29b27cab6a0a56ff7d 2025-07-17T07:01:51.8570600Z * [new tag] trunk/11bc29856d8180571146209cea987ac9397ab444 -> trunk/11bc29856d8180571146209cea987ac9397ab444 2025-07-17T07:01:51.8571461Z * [new tag] trunk/11d6ad8b2e7359e2f654474f31692ba1aec67980 -> trunk/11d6ad8b2e7359e2f654474f31692ba1aec67980 2025-07-17T07:01:51.8572446Z * [new tag] trunk/11f7e2f1459b09b1ece26ea785b6be77daa36923 -> trunk/11f7e2f1459b09b1ece26ea785b6be77daa36923 2025-07-17T07:01:51.8573352Z * [new tag] trunk/12151c96d9202875638ea2c695d5647c38368c46 -> trunk/12151c96d9202875638ea2c695d5647c38368c46 2025-07-17T07:01:51.8574262Z * [new tag] trunk/127695eb5c973f9fdba24c47b465e30a19292582 -> trunk/127695eb5c973f9fdba24c47b465e30a19292582 2025-07-17T07:01:51.8575639Z * [new tag] trunk/12b02137af5cd6743adfe5a0ffe0d9b453cd013e -> trunk/12b02137af5cd6743adfe5a0ffe0d9b453cd013e 2025-07-17T07:01:51.8576423Z * [new tag] trunk/12cb06e574738a15a59ee9384493682fb5bc6d2b -> trunk/12cb06e574738a15a59ee9384493682fb5bc6d2b 2025-07-17T07:01:51.8577322Z * [new tag] trunk/12f9942b107acc9d7acf9591818c826ef972a0f5 -> trunk/12f9942b107acc9d7acf9591818c826ef972a0f5 2025-07-17T07:01:51.8578173Z * [new tag] trunk/130d4973bd036e539e995dd0bc20080c2570b6da -> trunk/130d4973bd036e539e995dd0bc20080c2570b6da 2025-07-17T07:01:51.8579082Z * [new tag] trunk/132babe7e0b668d31e6efdf339687d48b2ccedc7 -> trunk/132babe7e0b668d31e6efdf339687d48b2ccedc7 2025-07-17T07:01:51.8579906Z * [new tag] trunk/1339e88105cf72586d728d4f7e0d71b81e08d933 -> trunk/1339e88105cf72586d728d4f7e0d71b81e08d933 2025-07-17T07:01:51.8580802Z * [new tag] trunk/134dfb3fe64323d7c615e866c43ea346ad987556 -> trunk/134dfb3fe64323d7c615e866c43ea346ad987556 2025-07-17T07:01:51.8581639Z * [new tag] trunk/1393f71e0714d067ed5ec1f57f135431d20619bc -> trunk/1393f71e0714d067ed5ec1f57f135431d20619bc 2025-07-17T07:01:51.8582758Z * [new tag] trunk/13bf2655c1404aff64e05e50dd7b0ce4c8dc0fcf -> trunk/13bf2655c1404aff64e05e50dd7b0ce4c8dc0fcf 2025-07-17T07:01:51.8583762Z * [new tag] trunk/13ea0f2c0a06101d9aab9ce12c30043be7c92d38 -> trunk/13ea0f2c0a06101d9aab9ce12c30043be7c92d38 2025-07-17T07:01:51.8584569Z * [new tag] trunk/13efb2c858c41141776ad42f7c44f1709948de71 -> trunk/13efb2c858c41141776ad42f7c44f1709948de71 2025-07-17T07:01:51.8585380Z * [new tag] trunk/144965ca9af478515736665b0577cded22fa692e -> trunk/144965ca9af478515736665b0577cded22fa692e 2025-07-17T07:01:51.8586227Z * [new tag] trunk/14ecc0336185f2ca5591858bc74cd4aadf2d1161 -> trunk/14ecc0336185f2ca5591858bc74cd4aadf2d1161 2025-07-17T07:01:51.8587188Z * [new tag] trunk/14f3639e09d692e53c9b9714776e3ca48fed7c4c -> trunk/14f3639e09d692e53c9b9714776e3ca48fed7c4c 2025-07-17T07:01:51.8588108Z * [new tag] trunk/154a39bfbd6e0cc6b7e6f3bce708ab0157ce1c50 -> trunk/154a39bfbd6e0cc6b7e6f3bce708ab0157ce1c50 2025-07-17T07:01:51.8589477Z * [new tag] trunk/156a377f4cf9b5b5255575e26d27f745c111a6ae -> trunk/156a377f4cf9b5b5255575e26d27f745c111a6ae 2025-07-17T07:01:51.8590289Z * [new tag] trunk/156b28e62a225214a5685088a848c5efe6a4b95e -> trunk/156b28e62a225214a5685088a848c5efe6a4b95e 2025-07-17T07:01:51.8591284Z * [new tag] trunk/156bc243f0edbf79d4a24d5affb48198551b58dd -> trunk/156bc243f0edbf79d4a24d5affb48198551b58dd 2025-07-17T07:01:51.8592075Z * [new tag] trunk/157683d8623a683a7e05755176f86a41c7bf518a -> trunk/157683d8623a683a7e05755176f86a41c7bf518a 2025-07-17T07:01:51.8592937Z * [new tag] trunk/1586521461c8dc642735466fc143b7d366a858d0 -> trunk/1586521461c8dc642735466fc143b7d366a858d0 2025-07-17T07:01:51.8593912Z * [new tag] trunk/159a39ad344cca84347e7b3be653ec13834cefdc -> trunk/159a39ad344cca84347e7b3be653ec13834cefdc 2025-07-17T07:01:51.8594841Z * [new tag] trunk/162ca185ff06fc7440e9f52d249e0b465976449a -> trunk/162ca185ff06fc7440e9f52d249e0b465976449a 2025-07-17T07:01:51.8595743Z * [new tag] trunk/163f0d8f2ab0a602a16f606db6d873298088e3a7 -> trunk/163f0d8f2ab0a602a16f606db6d873298088e3a7 2025-07-17T07:01:51.8596877Z * [new tag] trunk/16c3b4143b6fdabbee45ea5a64bb922a5969145a -> trunk/16c3b4143b6fdabbee45ea5a64bb922a5969145a 2025-07-17T07:01:51.8597683Z * [new tag] trunk/172853547ac842bb5ed8bf6e07702c76ff2cf043 -> trunk/172853547ac842bb5ed8bf6e07702c76ff2cf043 2025-07-17T07:01:51.8598540Z * [new tag] trunk/178fe7aa98987111a73534375099f4ad255e8b59 -> trunk/178fe7aa98987111a73534375099f4ad255e8b59 2025-07-17T07:01:51.8599444Z * [new tag] trunk/179dcc10e4e0c742fb7d93b832021d0c177798bf -> trunk/179dcc10e4e0c742fb7d93b832021d0c177798bf 2025-07-17T07:01:51.8600436Z * [new tag] trunk/17b38b850e5362c2f5d7cd75e6552e3d149232f2 -> trunk/17b38b850e5362c2f5d7cd75e6552e3d149232f2 2025-07-17T07:01:51.8601336Z * [new tag] trunk/17eb649d5596c52bae65a069e03b4550155ad57f -> trunk/17eb649d5596c52bae65a069e03b4550155ad57f 2025-07-17T07:01:51.8602256Z * [new tag] trunk/1839e8d04b81ee6eda0cff6fbfc218a7a600f6f7 -> trunk/1839e8d04b81ee6eda0cff6fbfc218a7a600f6f7 2025-07-17T07:01:51.8603217Z * [new tag] trunk/1851f50866a11e7ddede5d07c583e2c5407e7708 -> trunk/1851f50866a11e7ddede5d07c583e2c5407e7708 2025-07-17T07:01:51.8604112Z * [new tag] trunk/187828dcb4145cdfda916ff670207bfb17abdef8 -> trunk/187828dcb4145cdfda916ff670207bfb17abdef8 2025-07-17T07:01:51.8605049Z * [new tag] trunk/18b01afa9ed1dbc696a06de1b69bf5c021c18c10 -> trunk/18b01afa9ed1dbc696a06de1b69bf5c021c18c10 2025-07-17T07:01:51.8605941Z * [new tag] trunk/18bf6addc4741852953ac0b8d555411c347e67aa -> trunk/18bf6addc4741852953ac0b8d555411c347e67aa 2025-07-17T07:01:51.8606849Z * [new tag] trunk/18e4c461fb4e2bbdef889c3b1a2cdb29c0fb31cd -> trunk/18e4c461fb4e2bbdef889c3b1a2cdb29c0fb31cd 2025-07-17T07:01:51.8607743Z * [new tag] trunk/190f76fa313410df8dbb4111c586a516bf55515c -> trunk/190f76fa313410df8dbb4111c586a516bf55515c 2025-07-17T07:01:51.8608647Z * [new tag] trunk/1913c915e0d2b08e7af00889dc50e78d0a9b9198 -> trunk/1913c915e0d2b08e7af00889dc50e78d0a9b9198 2025-07-17T07:01:51.8609548Z * [new tag] trunk/191693ac854de1bb5fcf4280b7a93ab29cc12b5c -> trunk/191693ac854de1bb5fcf4280b7a93ab29cc12b5c 2025-07-17T07:01:51.8610465Z * [new tag] trunk/194539e9c33dc793fe67fbb68c7cee12f399e276 -> trunk/194539e9c33dc793fe67fbb68c7cee12f399e276 2025-07-17T07:01:51.8611400Z * [new tag] trunk/194c221e0abc9daf178e14ca2608c5d6804a3eb7 -> trunk/194c221e0abc9daf178e14ca2608c5d6804a3eb7 2025-07-17T07:01:51.8612321Z * [new tag] trunk/195ef1bce8f17419139fb81406b91f476eba7257 -> trunk/195ef1bce8f17419139fb81406b91f476eba7257 2025-07-17T07:01:51.8613168Z * [new tag] trunk/19625daf889f0a6192a76e200205817e3ee27f26 -> trunk/19625daf889f0a6192a76e200205817e3ee27f26 2025-07-17T07:01:51.8614363Z * [new tag] trunk/196c95d463367f15999c0cddc9eb89031e9988ab -> trunk/196c95d463367f15999c0cddc9eb89031e9988ab 2025-07-17T07:01:51.8615274Z * [new tag] trunk/197c1869f5467b4cfa1e197bbe394be82ba40554 -> trunk/197c1869f5467b4cfa1e197bbe394be82ba40554 2025-07-17T07:01:51.8616141Z * [new tag] trunk/1982ec2d22c5145e4ffeec08064c5ca17e969c25 -> trunk/1982ec2d22c5145e4ffeec08064c5ca17e969c25 2025-07-17T07:01:51.8617040Z * [new tag] trunk/19a01382bc623cf30cdfa7215e47a6b69d8598ff -> trunk/19a01382bc623cf30cdfa7215e47a6b69d8598ff 2025-07-17T07:01:51.8617792Z * [new tag] trunk/19ae5afdaa3a59135e6adbaea197b7a2456f1865 -> trunk/19ae5afdaa3a59135e6adbaea197b7a2456f1865 2025-07-17T07:01:51.8618541Z * [new tag] trunk/19f851ce10b16f0ed11d18d937ca7b32746153b0 -> trunk/19f851ce10b16f0ed11d18d937ca7b32746153b0 2025-07-17T07:01:51.8619452Z * [new tag] trunk/19ffb5e6f7606436249742b0f3efc0bab244dc55 -> trunk/19ffb5e6f7606436249742b0f3efc0bab244dc55 2025-07-17T07:01:51.8620799Z * [new tag] trunk/19ffdf4ea053bc5befe7eaa737fd1fafaaeb34a2 -> trunk/19ffdf4ea053bc5befe7eaa737fd1fafaaeb34a2 2025-07-17T07:01:51.8621649Z * [new tag] trunk/1a195bf7d6155b027f7ca81a97d71a3af34ce11e -> trunk/1a195bf7d6155b027f7ca81a97d71a3af34ce11e 2025-07-17T07:01:51.8622524Z * [new tag] trunk/1a568f4e5d1c0e2e8994d64f01e1e8a3104a1a48 -> trunk/1a568f4e5d1c0e2e8994d64f01e1e8a3104a1a48 2025-07-17T07:01:51.8623455Z * [new tag] trunk/1b032384b186154bb140c7368e2e700cb1419b86 -> trunk/1b032384b186154bb140c7368e2e700cb1419b86 2025-07-17T07:01:51.8624656Z * [new tag] trunk/1b389025ba0cc640e07991314bfba8b6ca385bd2 -> trunk/1b389025ba0cc640e07991314bfba8b6ca385bd2 2025-07-17T07:01:51.8625490Z * [new tag] trunk/1b3d69b59f92383633731aada8383ab88da3ed60 -> trunk/1b3d69b59f92383633731aada8383ab88da3ed60 2025-07-17T07:01:51.8626396Z * [new tag] trunk/1b50c12584909bda00009f4f0fd0d38ec792d019 -> trunk/1b50c12584909bda00009f4f0fd0d38ec792d019 2025-07-17T07:01:51.8627384Z * [new tag] trunk/1b58e7adab91fe20bbfb1568403d72869317e75c -> trunk/1b58e7adab91fe20bbfb1568403d72869317e75c 2025-07-17T07:01:51.8628295Z * [new tag] trunk/1b6772a90f5c334cab7b9d055a7f819ce4c3478c -> trunk/1b6772a90f5c334cab7b9d055a7f819ce4c3478c 2025-07-17T07:01:51.8629234Z * [new tag] trunk/1bb9b1858b84ee07ff1bc33157d4ea7a980bb6b5 -> trunk/1bb9b1858b84ee07ff1bc33157d4ea7a980bb6b5 2025-07-17T07:01:51.8630220Z * [new tag] trunk/1c6057fd179b0373686a790b0a0b7fc68fe7f27d -> trunk/1c6057fd179b0373686a790b0a0b7fc68fe7f27d 2025-07-17T07:01:51.8631159Z * [new tag] trunk/1c8844d9e7b2d72fb80b67ed51df4f6a1295b3b5 -> trunk/1c8844d9e7b2d72fb80b67ed51df4f6a1295b3b5 2025-07-17T07:01:51.8632019Z * [new tag] trunk/1c960c5638b6f20f7a1cbe1e9036c14a4845a195 -> trunk/1c960c5638b6f20f7a1cbe1e9036c14a4845a195 2025-07-17T07:01:51.8633094Z * [new tag] trunk/1cb0597a890bc50b8ed3937d6bdf5a734691bd91 -> trunk/1cb0597a890bc50b8ed3937d6bdf5a734691bd91 2025-07-17T07:01:51.8633814Z * [new tag] trunk/1cc62c2cb91e56ae50494f88f369cd6ec466a118 -> trunk/1cc62c2cb91e56ae50494f88f369cd6ec466a118 2025-07-17T07:01:51.8634718Z * [new tag] trunk/1ccc57e4288e231210c9b2c29943b2752259bf44 -> trunk/1ccc57e4288e231210c9b2c29943b2752259bf44 2025-07-17T07:01:51.8635629Z * [new tag] trunk/1cce73b5f4806f266fbbcf3383057af5f2e3a0aa -> trunk/1cce73b5f4806f266fbbcf3383057af5f2e3a0aa 2025-07-17T07:01:51.8636491Z * [new tag] trunk/1cfdcb975a3b0c685c34c6ae0e378b9bc92e0050 -> trunk/1cfdcb975a3b0c685c34c6ae0e378b9bc92e0050 2025-07-17T07:01:51.8637405Z * [new tag] trunk/1d0f45d5d15389b83312b6942ac2017c8d7ebedc -> trunk/1d0f45d5d15389b83312b6942ac2017c8d7ebedc 2025-07-17T07:01:51.8638343Z * [new tag] trunk/1d584761622ff6e5519c5e3dbbb62a21b89ffe8a -> trunk/1d584761622ff6e5519c5e3dbbb62a21b89ffe8a 2025-07-17T07:01:51.8639274Z * [new tag] trunk/1d993fa3092e4f0b5745f2470024b35cac96da14 -> trunk/1d993fa3092e4f0b5745f2470024b35cac96da14 2025-07-17T07:01:51.8640112Z * [new tag] trunk/1dc1eedd4369f6e6bb79d5315e3ffc1bdc59b709 -> trunk/1dc1eedd4369f6e6bb79d5315e3ffc1bdc59b709 2025-07-17T07:01:51.8641070Z * [new tag] trunk/1dd0b1d12ba48d7879a57391cab6213742dcadb6 -> trunk/1dd0b1d12ba48d7879a57391cab6213742dcadb6 2025-07-17T07:01:51.8641982Z * [new tag] trunk/1e373d02d57ff65c70febc4ec427bf6d1708483e -> trunk/1e373d02d57ff65c70febc4ec427bf6d1708483e 2025-07-17T07:01:51.8642925Z * [new tag] trunk/1e474cc9c8ddab22ed314ad8be641002b0161498 -> trunk/1e474cc9c8ddab22ed314ad8be641002b0161498 2025-07-17T07:01:51.8643830Z * [new tag] trunk/1e4c5b666afe6434cbe3b830aaf70864074d40f5 -> trunk/1e4c5b666afe6434cbe3b830aaf70864074d40f5 2025-07-17T07:01:51.8644672Z * [new tag] trunk/1e4d8b5a4a67220473bf0027c58baaa08a036714 -> trunk/1e4d8b5a4a67220473bf0027c58baaa08a036714 2025-07-17T07:01:51.8645606Z * [new tag] trunk/1e690b6c41677bf1fe3147e3fc011eb9805365f5 -> trunk/1e690b6c41677bf1fe3147e3fc011eb9805365f5 2025-07-17T07:01:51.8646507Z * [new tag] trunk/1e6a653234c299ccecf702233a1eeb7455b1ce77 -> trunk/1e6a653234c299ccecf702233a1eeb7455b1ce77 2025-07-17T07:01:51.8647267Z * [new tag] trunk/1e7e21ec5dd6ea28b27c57fe92bcf31ea5983913 -> trunk/1e7e21ec5dd6ea28b27c57fe92bcf31ea5983913 2025-07-17T07:01:51.8648509Z * [new tag] trunk/1e8e9f745e43fa38bbfc7b67b30bc66c0e7ebbd6 -> trunk/1e8e9f745e43fa38bbfc7b67b30bc66c0e7ebbd6 2025-07-17T07:01:51.8649361Z * [new tag] trunk/1ea9cde598ead20194dbb6c5cb26e74e36e6ad55 -> trunk/1ea9cde598ead20194dbb6c5cb26e74e36e6ad55 2025-07-17T07:01:51.8650259Z * [new tag] trunk/1ed243f01c8efb329055c6124ba0aa5f48747cfe -> trunk/1ed243f01c8efb329055c6124ba0aa5f48747cfe 2025-07-17T07:01:51.8651261Z * [new tag] trunk/1eea2c4fe35ffbdcbfccbeb7ac6c3ec02137385d -> trunk/1eea2c4fe35ffbdcbfccbeb7ac6c3ec02137385d 2025-07-17T07:01:51.8652183Z * [new tag] trunk/1f0eb79e3e91f09f99b53aa4d331eb47a1f66101 -> trunk/1f0eb79e3e91f09f99b53aa4d331eb47a1f66101 2025-07-17T07:01:51.8653052Z * [new tag] trunk/1f1f22991dcca01ed27fad44c21f0827d47bdd6b -> trunk/1f1f22991dcca01ed27fad44c21f0827d47bdd6b 2025-07-17T07:01:51.8653989Z * [new tag] trunk/1f3cc4875cb9d9b579a41b6699f97820c16c3868 -> trunk/1f3cc4875cb9d9b579a41b6699f97820c16c3868 2025-07-17T07:01:51.8654767Z * [new tag] trunk/1f57e0e04da9d334e238cec346f7ae3667bed9d1 -> trunk/1f57e0e04da9d334e238cec346f7ae3667bed9d1 2025-07-17T07:01:51.8656004Z * [new tag] trunk/1fff6356d9f99ec2980052c498dc0b70d513bc6d -> trunk/1fff6356d9f99ec2980052c498dc0b70d513bc6d 2025-07-17T07:01:51.8656814Z * [new tag] trunk/2002e3a311968d18afd0fa5d195943676f134fa2 -> trunk/2002e3a311968d18afd0fa5d195943676f134fa2 2025-07-17T07:01:51.8657782Z * [new tag] trunk/2022588295295fa1fdee7a500adad3bd0c28b9d6 -> trunk/2022588295295fa1fdee7a500adad3bd0c28b9d6 2025-07-17T07:01:51.8658694Z * [new tag] trunk/202d2ae53a493700da813ed7d6055c0b62c7fc6c -> trunk/202d2ae53a493700da813ed7d6055c0b62c7fc6c 2025-07-17T07:01:51.8659799Z * [new tag] trunk/204db27a0c4478c36dbb15fa2d4caed783a90265 -> trunk/204db27a0c4478c36dbb15fa2d4caed783a90265 2025-07-17T07:01:51.8660631Z * [new tag] trunk/205241a0d5149d05e44dc113dc0273e8eceff9f0 -> trunk/205241a0d5149d05e44dc113dc0273e8eceff9f0 2025-07-17T07:01:51.8661573Z * [new tag] trunk/208ec60e72a63f366c757a5bc895089ceb323fcc -> trunk/208ec60e72a63f366c757a5bc895089ceb323fcc 2025-07-17T07:01:51.8662533Z * [new tag] trunk/20a74c370b8427cce369b1abf0c470344d01dc8e -> trunk/20a74c370b8427cce369b1abf0c470344d01dc8e 2025-07-17T07:01:51.8663412Z * [new tag] trunk/20e40492b046b9287726d3ec656117e4dc38f0e2 -> trunk/20e40492b046b9287726d3ec656117e4dc38f0e2 2025-07-17T07:01:51.8664238Z * [new tag] trunk/210632fae1aa2cd994645a70836ff6c602e1beb9 -> trunk/210632fae1aa2cd994645a70836ff6c602e1beb9 2025-07-17T07:01:51.8665078Z * [new tag] trunk/212575f994abbc362da2517510c49f7a34e0a838 -> trunk/212575f994abbc362da2517510c49f7a34e0a838 2025-07-17T07:01:51.8665950Z * [new tag] trunk/214e2959dcdbf91a999d5c0a5d40c91e4442e8c5 -> trunk/214e2959dcdbf91a999d5c0a5d40c91e4442e8c5 2025-07-17T07:01:51.8666829Z * [new tag] trunk/2161be849702330509b452fefc2e103d3be54cd4 -> trunk/2161be849702330509b452fefc2e103d3be54cd4 2025-07-17T07:01:51.8667750Z * [new tag] trunk/216bd6091ec52865052282eced7e6d5d2a4b4fb4 -> trunk/216bd6091ec52865052282eced7e6d5d2a4b4fb4 2025-07-17T07:01:51.8668594Z * [new tag] trunk/2179afd7149c117dace9e552419082094b10a386 -> trunk/2179afd7149c117dace9e552419082094b10a386 2025-07-17T07:01:51.8669458Z * [new tag] trunk/21990fbad97acec769f737b450033774c7be8737 -> trunk/21990fbad97acec769f737b450033774c7be8737 2025-07-17T07:01:51.8670357Z * [new tag] trunk/21b5dc7a6aadb5da6c3f5be61f6204719dae00ca -> trunk/21b5dc7a6aadb5da6c3f5be61f6204719dae00ca 2025-07-17T07:01:51.8671215Z * [new tag] trunk/22edb457c9bf13e0d66631dff4f4ace5c38e0f3b -> trunk/22edb457c9bf13e0d66631dff4f4ace5c38e0f3b 2025-07-17T07:01:51.8672297Z * [new tag] trunk/22f3347fd9482bdf1006c9aa92353b739b7e868b -> trunk/22f3347fd9482bdf1006c9aa92353b739b7e868b 2025-07-17T07:01:51.8673462Z * [new tag] trunk/231eb9902ba78a4ef70203243058f3c7c0ced15d -> trunk/231eb9902ba78a4ef70203243058f3c7c0ced15d 2025-07-17T07:01:51.8674250Z * [new tag] trunk/23491519d288dedb2a54cfad5fef7fcb2ad8eade -> trunk/23491519d288dedb2a54cfad5fef7fcb2ad8eade 2025-07-17T07:01:51.8675179Z * [new tag] trunk/2380115f9738f97cf706affefd647d2cb6dfbb3f -> trunk/2380115f9738f97cf706affefd647d2cb6dfbb3f 2025-07-17T07:01:51.8676118Z * [new tag] trunk/24063ad10994e728087b3958cae9cc27dd376630 -> trunk/24063ad10994e728087b3958cae9cc27dd376630 2025-07-17T07:01:51.8676999Z * [new tag] trunk/242eb19c8383b4b197963a8a564475d52c85ac66 -> trunk/242eb19c8383b4b197963a8a564475d52c85ac66 2025-07-17T07:01:51.8678184Z * [new tag] trunk/243b12e5657a516d6e7b1a0a3f55851ce99bd4cb -> trunk/243b12e5657a516d6e7b1a0a3f55851ce99bd4cb 2025-07-17T07:01:51.8679161Z * [new tag] trunk/247113e03e3fc3e933138b52f856f33a8be33071 -> trunk/247113e03e3fc3e933138b52f856f33a8be33071 2025-07-17T07:01:51.8680059Z * [new tag] trunk/2471cc33550fc00164e2759b1d9928960e9cfaf6 -> trunk/2471cc33550fc00164e2759b1d9928960e9cfaf6 2025-07-17T07:01:51.8681059Z * [new tag] trunk/247f83e0a475bc7eea97c76719f8b5ffa87edced -> trunk/247f83e0a475bc7eea97c76719f8b5ffa87edced 2025-07-17T07:01:51.8681928Z * [new tag] trunk/2481c4b2ea5db7a6d6f60f5d27426000c2779dc8 -> trunk/2481c4b2ea5db7a6d6f60f5d27426000c2779dc8 2025-07-17T07:01:51.8682856Z * [new tag] trunk/24b49b98810bb77f3cfa4c15baa9a15c9be3db61 -> trunk/24b49b98810bb77f3cfa4c15baa9a15c9be3db61 2025-07-17T07:01:51.8683752Z * [new tag] trunk/250ae2531c55dcc50f558ec739941324e3f9a4d4 -> trunk/250ae2531c55dcc50f558ec739941324e3f9a4d4 2025-07-17T07:01:51.8684686Z * [new tag] trunk/25717da8c869a06befed60bc1a0e3dcdac7fd7d9 -> trunk/25717da8c869a06befed60bc1a0e3dcdac7fd7d9 2025-07-17T07:01:51.8685569Z * [new tag] trunk/2578796e235d1d878272084253bee3e2cc02a5b1 -> trunk/2578796e235d1d878272084253bee3e2cc02a5b1 2025-07-17T07:01:51.8686429Z * [new tag] trunk/2585960b47d6429a119665216123cb00435efde9 -> trunk/2585960b47d6429a119665216123cb00435efde9 2025-07-17T07:01:51.8687245Z * [new tag] trunk/2596e3d0617852469241be8777cf46db5c83928c -> trunk/2596e3d0617852469241be8777cf46db5c83928c 2025-07-17T07:01:51.8688386Z * [new tag] trunk/2620361d19f9c4bf37a71c8477823d605191c93a -> trunk/2620361d19f9c4bf37a71c8477823d605191c93a 2025-07-17T07:01:51.8689151Z * [new tag] trunk/2625c70aecc6eced1dbe108279feab7509733bef -> trunk/2625c70aecc6eced1dbe108279feab7509733bef 2025-07-17T07:01:51.8690024Z * [new tag] trunk/262654ee518eb314678f53baf4e133e4767eca3d -> trunk/262654ee518eb314678f53baf4e133e4767eca3d 2025-07-17T07:01:51.8690955Z * [new tag] trunk/26807dcf277feb2d99ab88d7b6da526488baea93 -> trunk/26807dcf277feb2d99ab88d7b6da526488baea93 2025-07-17T07:01:51.8691855Z * [new tag] trunk/26f066bb614c2ee5c0eff598c97855b1df011ef8 -> trunk/26f066bb614c2ee5c0eff598c97855b1df011ef8 2025-07-17T07:01:51.8692818Z * [new tag] trunk/26f7ca39724ff43b7b1e22190feeea67d2212f2d -> trunk/26f7ca39724ff43b7b1e22190feeea67d2212f2d 2025-07-17T07:01:51.8693725Z * [new tag] trunk/271ca679a8a0e40f1ecca49db76b5f8d6a9713d6 -> trunk/271ca679a8a0e40f1ecca49db76b5f8d6a9713d6 2025-07-17T07:01:51.8694449Z * [new tag] trunk/276c790010b015b79a7ca110e28f8e2111cfdb79 -> trunk/276c790010b015b79a7ca110e28f8e2111cfdb79 2025-07-17T07:01:51.8695490Z * [new tag] trunk/2796f31b5e3c90268365e961e2374df3ea93ff53 -> trunk/2796f31b5e3c90268365e961e2374df3ea93ff53 2025-07-17T07:01:51.8696433Z * [new tag] trunk/279cae52e750d5b4e0b49f3f1abca6c2027dd063 -> trunk/279cae52e750d5b4e0b49f3f1abca6c2027dd063 2025-07-17T07:01:51.8697524Z * [new tag] trunk/27c50799c1ca156166bf49ce72a5e97097a44d7d -> trunk/27c50799c1ca156166bf49ce72a5e97097a44d7d 2025-07-17T07:01:51.8698427Z * [new tag] trunk/27df0c56b7c6b75a5f77f5714aeef2ef8f1faa2c -> trunk/27df0c56b7c6b75a5f77f5714aeef2ef8f1faa2c 2025-07-17T07:01:51.8699304Z * [new tag] trunk/2815ade9a80e1f557370e86500b21cbc465a8ffe -> trunk/2815ade9a80e1f557370e86500b21cbc465a8ffe 2025-07-17T07:01:51.8700393Z * [new tag] trunk/2815eea0d0e02eaa0243fa8d1b9d8935b13b3aa5 -> trunk/2815eea0d0e02eaa0243fa8d1b9d8935b13b3aa5 2025-07-17T07:01:51.8701279Z * [new tag] trunk/2860f5c4f5328224ab7998bc90b9fb395b5b068b -> trunk/2860f5c4f5328224ab7998bc90b9fb395b5b068b 2025-07-17T07:01:51.8702408Z * [new tag] trunk/28796f71d04302029290f473a286efc2aba339c2 -> trunk/28796f71d04302029290f473a286efc2aba339c2 2025-07-17T07:01:51.8703087Z * [new tag] trunk/28aae93f24556c6aa8c972c0479b01b908b67fbe -> trunk/28aae93f24556c6aa8c972c0479b01b908b67fbe 2025-07-17T07:01:51.8704056Z * [new tag] trunk/2903e5ad3c4304b90daa6e5ed44a379225571b3a -> trunk/2903e5ad3c4304b90daa6e5ed44a379225571b3a 2025-07-17T07:01:51.8704966Z * [new tag] trunk/2908c10259bac21b00e9b36318e364801e0ae910 -> trunk/2908c10259bac21b00e9b36318e364801e0ae910 2025-07-17T07:01:51.8705952Z * [new tag] trunk/29391c7cf927857123108fa5e9dae6ed3db489f1 -> trunk/29391c7cf927857123108fa5e9dae6ed3db489f1 2025-07-17T07:01:51.8706880Z * [new tag] trunk/297805fd8f59b76a28048a79e8bced2616ed8713 -> trunk/297805fd8f59b76a28048a79e8bced2616ed8713 2025-07-17T07:01:51.8708239Z * [new tag] trunk/297daa1d30c80826b939d8f2dcd07422dec72642 -> trunk/297daa1d30c80826b939d8f2dcd07422dec72642 2025-07-17T07:01:51.8709060Z * [new tag] trunk/29867b211ab74355a0e03bdc9e204d89668c291a -> trunk/29867b211ab74355a0e03bdc9e204d89668c291a 2025-07-17T07:01:51.8709986Z * [new tag] trunk/29e6033ff38f0deb8dde1146a1612e64ed00e3b7 -> trunk/29e6033ff38f0deb8dde1146a1612e64ed00e3b7 2025-07-17T07:01:51.8718815Z * [new tag] trunk/29f76ec0f3eccf619a0aee03e3abbd4914a1b4b2 -> trunk/29f76ec0f3eccf619a0aee03e3abbd4914a1b4b2 2025-07-17T07:01:51.8719398Z * [new tag] trunk/2a3b41cbd077b229bbdd59f6f068c9b643a78b0b -> trunk/2a3b41cbd077b229bbdd59f6f068c9b643a78b0b 2025-07-17T07:01:51.8719794Z * [new tag] trunk/2a4e357192a2b01c9acaeb2168349e5396c9192a -> trunk/2a4e357192a2b01c9acaeb2168349e5396c9192a 2025-07-17T07:01:51.8720287Z * [new tag] trunk/2a8795a981c02f57a57f8699d0e85e42c53b6117 -> trunk/2a8795a981c02f57a57f8699d0e85e42c53b6117 2025-07-17T07:01:51.8720712Z * [new tag] trunk/2aade5ee9fafe52aef4e60624e6c950bc25f8a3e -> trunk/2aade5ee9fafe52aef4e60624e6c950bc25f8a3e 2025-07-17T07:01:51.8721113Z * [new tag] trunk/2ad5c25cfc603c3656e6699d6137419dbb009495 -> trunk/2ad5c25cfc603c3656e6699d6137419dbb009495 2025-07-17T07:01:51.8721516Z * [new tag] trunk/2af7c67e48cad59d648a3e85501f74eade5a3268 -> trunk/2af7c67e48cad59d648a3e85501f74eade5a3268 2025-07-17T07:01:51.8721901Z * [new tag] trunk/2b0f9b1f6172a0d5817c7ac7406200897311da5f -> trunk/2b0f9b1f6172a0d5817c7ac7406200897311da5f 2025-07-17T07:01:51.8722306Z * [new tag] trunk/2b19d85d70ecf1b7121e6d35f2adff06e1eb2e06 -> trunk/2b19d85d70ecf1b7121e6d35f2adff06e1eb2e06 2025-07-17T07:01:51.8722692Z * [new tag] trunk/2b9d638e3333e6e9ae324e1486774e83292e1883 -> trunk/2b9d638e3333e6e9ae324e1486774e83292e1883 2025-07-17T07:01:51.8723083Z * [new tag] trunk/2ba930d4ce04f417195f3061e55479a1a8b16b9d -> trunk/2ba930d4ce04f417195f3061e55479a1a8b16b9d 2025-07-17T07:01:51.8723477Z * [new tag] trunk/2bb33e7a08c4710024b3cb249a13cd2f5d0f6473 -> trunk/2bb33e7a08c4710024b3cb249a13cd2f5d0f6473 2025-07-17T07:01:51.8723876Z * [new tag] trunk/2c0d94a7debe83e96a821548a6e0cb18a0f5cfc3 -> trunk/2c0d94a7debe83e96a821548a6e0cb18a0f5cfc3 2025-07-17T07:01:51.8724278Z * [new tag] trunk/2c1a93a0ae7ebd7963a5931d7b34c885d57844d9 -> trunk/2c1a93a0ae7ebd7963a5931d7b34c885d57844d9 2025-07-17T07:01:51.8724685Z * [new tag] trunk/2c6324a1ebf3e3f6a12be6d18fc0bcdbe00f2a0a -> trunk/2c6324a1ebf3e3f6a12be6d18fc0bcdbe00f2a0a 2025-07-17T07:01:51.8725085Z * [new tag] trunk/2c68c3e8d5e9a235f5861be6486de4959f80c840 -> trunk/2c68c3e8d5e9a235f5861be6486de4959f80c840 2025-07-17T07:01:51.8725620Z * [new tag] trunk/2c76f31221e117b217b8a6a96a5405f626d2218a -> trunk/2c76f31221e117b217b8a6a96a5405f626d2218a 2025-07-17T07:01:51.8726501Z * [new tag] trunk/2cdcd16e836a144f36996104b1340b03041eb07a -> trunk/2cdcd16e836a144f36996104b1340b03041eb07a 2025-07-17T07:01:51.8727394Z * [new tag] trunk/2d3615f577894c7a117a55e85bb8371bb598ec50 -> trunk/2d3615f577894c7a117a55e85bb8371bb598ec50 2025-07-17T07:01:51.8728144Z * [new tag] trunk/2d39a48d524021995269411bd49fe792e59d9f94 -> trunk/2d39a48d524021995269411bd49fe792e59d9f94 2025-07-17T07:01:51.8729029Z * [new tag] trunk/2d7e6c6241971106a56073d7a53c7d1336b11a51 -> trunk/2d7e6c6241971106a56073d7a53c7d1336b11a51 2025-07-17T07:01:51.8729906Z * [new tag] trunk/2d832c9587fd99db295b62d0c9b459d509c19d06 -> trunk/2d832c9587fd99db295b62d0c9b459d509c19d06 2025-07-17T07:01:51.8730860Z * [new tag] trunk/2db1a54465b6121993ad7827f1327ed319c81d70 -> trunk/2db1a54465b6121993ad7827f1327ed319c81d70 2025-07-17T07:01:51.8731919Z * [new tag] trunk/2dc16274519841dc2778cba6962cb0357d1ee8dc -> trunk/2dc16274519841dc2778cba6962cb0357d1ee8dc 2025-07-17T07:01:51.8732767Z * [new tag] trunk/2e0e08588e7cb04f49ee5bbc000b18b72864dfa1 -> trunk/2e0e08588e7cb04f49ee5bbc000b18b72864dfa1 2025-07-17T07:01:51.8733634Z * [new tag] trunk/2e14069081172faf9a51bd34c1e0b85dbf39cb4b -> trunk/2e14069081172faf9a51bd34c1e0b85dbf39cb4b 2025-07-17T07:01:51.8734893Z * [new tag] trunk/2e2ea7290a1cf2da3c3efd6e6ad4836bb94beaeb -> trunk/2e2ea7290a1cf2da3c3efd6e6ad4836bb94beaeb 2025-07-17T07:01:51.8735841Z * [new tag] trunk/2e64e45b0b260e2efc2bde641ec2b3b8ed910194 -> trunk/2e64e45b0b260e2efc2bde641ec2b3b8ed910194 2025-07-17T07:01:51.8736762Z * [new tag] trunk/2e9bd03f6075b9b93b070c3770bf58f60be5508e -> trunk/2e9bd03f6075b9b93b070c3770bf58f60be5508e 2025-07-17T07:01:51.8737795Z * [new tag] trunk/2eb744c08d600e84b167dbda7daa792243a2c235 -> trunk/2eb744c08d600e84b167dbda7daa792243a2c235 2025-07-17T07:01:51.8738485Z * [new tag] trunk/2ee23175d96aa022a9c94209c14bbb7f8e064760 -> trunk/2ee23175d96aa022a9c94209c14bbb7f8e064760 2025-07-17T07:01:51.8739410Z * [new tag] trunk/2efa5eaa652f9a6505635d96178e3f119b20adf7 -> trunk/2efa5eaa652f9a6505635d96178e3f119b20adf7 2025-07-17T07:01:51.8740287Z * [new tag] trunk/2eff14c4455de22628ea599a0d4ba047a7eba6ae -> trunk/2eff14c4455de22628ea599a0d4ba047a7eba6ae 2025-07-17T07:01:51.8741255Z * [new tag] trunk/2f1c5c4131047d25f822ac278bac8c13deff4079 -> trunk/2f1c5c4131047d25f822ac278bac8c13deff4079 2025-07-17T07:01:51.8742407Z * [new tag] trunk/2f94f69b7c83370ef0cc65e3ab96bb5bf11a7b1a -> trunk/2f94f69b7c83370ef0cc65e3ab96bb5bf11a7b1a 2025-07-17T07:01:51.8743234Z * [new tag] trunk/2fde2090d001a9ae63ef41413f11897761eb4de9 -> trunk/2fde2090d001a9ae63ef41413f11897761eb4de9 2025-07-17T07:01:51.8743978Z * [new tag] trunk/2ff3280c77c705e11c5211d4be8fef9853cd0559 -> trunk/2ff3280c77c705e11c5211d4be8fef9853cd0559 2025-07-17T07:01:51.8744865Z * [new tag] trunk/3003c681ef1f8efbfa83470173c62930d195364b -> trunk/3003c681ef1f8efbfa83470173c62930d195364b 2025-07-17T07:01:51.8745773Z * [new tag] trunk/30293b8b5edca86cfc80f24874b0968620a8e72d -> trunk/30293b8b5edca86cfc80f24874b0968620a8e72d 2025-07-17T07:01:51.8746886Z * [new tag] trunk/30387ab2e485384ab2e67084a1e2c5569190ba92 -> trunk/30387ab2e485384ab2e67084a1e2c5569190ba92 2025-07-17T07:01:51.8747690Z * [new tag] trunk/3040ca6d0f8558e39919b14eebeacc34ddf980f5 -> trunk/3040ca6d0f8558e39919b14eebeacc34ddf980f5 2025-07-17T07:01:51.8748650Z * [new tag] trunk/30587195d314eb5eb02ce63f39a9be4c943629ef -> trunk/30587195d314eb5eb02ce63f39a9be4c943629ef 2025-07-17T07:01:51.8749553Z * [new tag] trunk/306dd19216b656467143483395ef582feb5d7d07 -> trunk/306dd19216b656467143483395ef582feb5d7d07 2025-07-17T07:01:51.8750415Z * [new tag] trunk/30a1cc11a47e63c4612ac862f1f2d984ab4dbf24 -> trunk/30a1cc11a47e63c4612ac862f1f2d984ab4dbf24 2025-07-17T07:01:51.8751312Z * [new tag] trunk/30d2648a4ae1585e70189e6fdca6dc9ac3d074ec -> trunk/30d2648a4ae1585e70189e6fdca6dc9ac3d074ec 2025-07-17T07:01:51.8752263Z * [new tag] trunk/30d3cf62fb813819364007e26b3eda84f7cddf3b -> trunk/30d3cf62fb813819364007e26b3eda84f7cddf3b 2025-07-17T07:01:51.8753240Z * [new tag] trunk/3106a33e410db9d1c2b0e47f9e329611d344a827 -> trunk/3106a33e410db9d1c2b0e47f9e329611d344a827 2025-07-17T07:01:51.8754086Z * [new tag] trunk/310e8361c565ca1602e719e4c812dc3931ec84d7 -> trunk/310e8361c565ca1602e719e4c812dc3931ec84d7 2025-07-17T07:01:51.8754981Z * [new tag] trunk/31326a9ad7db53e5bf4e03f8e52b75da944d0298 -> trunk/31326a9ad7db53e5bf4e03f8e52b75da944d0298 2025-07-17T07:01:51.8755845Z * [new tag] trunk/313a6a8ef94d689331b2bd8161f95c23d42eb22d -> trunk/313a6a8ef94d689331b2bd8161f95c23d42eb22d 2025-07-17T07:01:51.8756784Z * [new tag] trunk/3159ee2ad3e586573edb977c4c1745a26d9f1007 -> trunk/3159ee2ad3e586573edb977c4c1745a26d9f1007 2025-07-17T07:01:51.8757658Z * [new tag] trunk/31659964a5b546a84a4c18bb9a0828a0f63b8ccb -> trunk/31659964a5b546a84a4c18bb9a0828a0f63b8ccb 2025-07-17T07:01:51.8758505Z * [new tag] trunk/3173616532ecf0d5c0b78595d6054f1b4bd2bd75 -> trunk/3173616532ecf0d5c0b78595d6054f1b4bd2bd75 2025-07-17T07:01:51.8759406Z * [new tag] trunk/317520bf6ed5c1f2ef324f841e8ec0d8d80cb58a -> trunk/317520bf6ed5c1f2ef324f841e8ec0d8d80cb58a 2025-07-17T07:01:51.8760313Z * [new tag] trunk/317af4c87b7b8b03b8a08a4ead84d4065dd920e0 -> trunk/317af4c87b7b8b03b8a08a4ead84d4065dd920e0 2025-07-17T07:01:51.8761062Z * [new tag] trunk/31e127459789f9d97b3b35001c50e76f1b580cd5 -> trunk/31e127459789f9d97b3b35001c50e76f1b580cd5 2025-07-17T07:01:51.8762073Z * [new tag] trunk/3232b57cd87fbd15c990fbf87d181716a1993a55 -> trunk/3232b57cd87fbd15c990fbf87d181716a1993a55 2025-07-17T07:01:51.8763001Z * [new tag] trunk/326e751d07b92f788bece086227da9288206a27a -> trunk/326e751d07b92f788bece086227da9288206a27a 2025-07-17T07:01:51.8763911Z * [new tag] trunk/32983ea698842a2ca331eabb06acce9c99acb082 -> trunk/32983ea698842a2ca331eabb06acce9c99acb082 2025-07-17T07:01:51.8765038Z * [new tag] trunk/32c1611263ecc8832a8d7494480654ad851911c0 -> trunk/32c1611263ecc8832a8d7494480654ad851911c0 2025-07-17T07:01:51.8765952Z * [new tag] trunk/32eee8ed225d9f10fbbcb38c24b8b44c24c0c97c -> trunk/32eee8ed225d9f10fbbcb38c24b8b44c24c0c97c 2025-07-17T07:01:51.8767335Z * [new tag] trunk/3321acc92e24859dbe2ac6499067d1afde5622c3 -> trunk/3321acc92e24859dbe2ac6499067d1afde5622c3 2025-07-17T07:01:51.8768134Z * [new tag] trunk/333e0e61472c71cb2ad51a59cc5c56f6a47bc747 -> trunk/333e0e61472c71cb2ad51a59cc5c56f6a47bc747 2025-07-17T07:01:51.8769277Z * [new tag] trunk/3341c131b767a4036c152624c1e43baaf24cadf9 -> trunk/3341c131b767a4036c152624c1e43baaf24cadf9 2025-07-17T07:01:51.8770109Z * [new tag] trunk/336bff6d58ceb50b12d9d67764fd9f238bc0adb5 -> trunk/336bff6d58ceb50b12d9d67764fd9f238bc0adb5 2025-07-17T07:01:51.8771030Z * [new tag] trunk/336f1e2d35dfc63925754e884543491cc3f98f8c -> trunk/336f1e2d35dfc63925754e884543491cc3f98f8c 2025-07-17T07:01:51.8772047Z * [new tag] trunk/338a8c7853ab9b3e67dbcb402d30fec066bdd856 -> trunk/338a8c7853ab9b3e67dbcb402d30fec066bdd856 2025-07-17T07:01:51.8773176Z * [new tag] trunk/3404c1f0cfe4c8fd41d718d38316c24a3c0474b0 -> trunk/3404c1f0cfe4c8fd41d718d38316c24a3c0474b0 2025-07-17T07:01:51.8773946Z * [new tag] trunk/344731fb257440ab04f2d634929821be9be3fa80 -> trunk/344731fb257440ab04f2d634929821be9be3fa80 2025-07-17T07:01:51.8776223Z * [new tag] trunk/347ace4c7ac2dbb14799089c30bd01a9ac312791 -> trunk/347ace4c7ac2dbb14799089c30bd01a9ac312791 2025-07-17T07:01:51.8777183Z * [new tag] trunk/348e2a76dfe9c8a1995ab6ce4902dbeba935c984 -> trunk/348e2a76dfe9c8a1995ab6ce4902dbeba935c984 2025-07-17T07:01:51.8777947Z * [new tag] trunk/348fd45065620f20080299774f37f45233ef8f6b -> trunk/348fd45065620f20080299774f37f45233ef8f6b 2025-07-17T07:01:51.8778878Z * [new tag] trunk/3490a4f906aeaa867a390c2e97b357f17b01fcf2 -> trunk/3490a4f906aeaa867a390c2e97b357f17b01fcf2 2025-07-17T07:01:51.8779734Z * [new tag] trunk/34c8033fd3dea404db1706e264761912d095f723 -> trunk/34c8033fd3dea404db1706e264761912d095f723 2025-07-17T07:01:51.8780694Z * [new tag] trunk/34d8e64ef64d88324092a2028884c54c13e086b3 -> trunk/34d8e64ef64d88324092a2028884c54c13e086b3 2025-07-17T07:01:51.8781605Z * [new tag] trunk/35321b2ad6e86a58ca5d5be90ad8edce5945e16c -> trunk/35321b2ad6e86a58ca5d5be90ad8edce5945e16c 2025-07-17T07:01:51.8782492Z * [new tag] trunk/3580b8dde44d8bf4f229537ae9897ddd5e70f5db -> trunk/3580b8dde44d8bf4f229537ae9897ddd5e70f5db 2025-07-17T07:01:51.8783626Z * [new tag] trunk/3584e84c2434a2681b7288bcbd3bdb163c793df5 -> trunk/3584e84c2434a2681b7288bcbd3bdb163c793df5 2025-07-17T07:01:51.8784448Z * [new tag] trunk/3596c0c77f28c74e6cfb50769402ed52dbe10c67 -> trunk/3596c0c77f28c74e6cfb50769402ed52dbe10c67 2025-07-17T07:01:51.8785335Z * [new tag] trunk/359e8f5d691cdbbc54beafea25c06fc0ce321ad6 -> trunk/359e8f5d691cdbbc54beafea25c06fc0ce321ad6 2025-07-17T07:01:51.8786233Z * [new tag] trunk/35d03398e511fa0921c9db928c661dc9531ff2fc -> trunk/35d03398e511fa0921c9db928c661dc9531ff2fc 2025-07-17T07:01:51.8787082Z * [new tag] trunk/35e44067c4d9cc9be2652c0b9098885c5a321029 -> trunk/35e44067c4d9cc9be2652c0b9098885c5a321029 2025-07-17T07:01:51.8788042Z * [new tag] trunk/35e8f2593cdb72036c88a4b8d01487747204ba03 -> trunk/35e8f2593cdb72036c88a4b8d01487747204ba03 2025-07-17T07:01:51.8788890Z * [new tag] trunk/35ecd7c2d44a4e370e21ddab76b6c28266188846 -> trunk/35ecd7c2d44a4e370e21ddab76b6c28266188846 2025-07-17T07:01:51.8789761Z * [new tag] trunk/3608737347bcbb1ec1e23d85a29811abfd75c7a1 -> trunk/3608737347bcbb1ec1e23d85a29811abfd75c7a1 2025-07-17T07:01:51.8790657Z * [new tag] trunk/3644b41a7ccda62c066d525d956a08c9eac686c0 -> trunk/3644b41a7ccda62c066d525d956a08c9eac686c0 2025-07-17T07:01:51.8791551Z * [new tag] trunk/365ce465f393a6426b4ab3148da9a92445bf61d3 -> trunk/365ce465f393a6426b4ab3148da9a92445bf61d3 2025-07-17T07:01:51.8792427Z * [new tag] trunk/3684be056d9af667400ba071a116be8b1112bba8 -> trunk/3684be056d9af667400ba071a116be8b1112bba8 2025-07-17T07:01:51.8793340Z * [new tag] trunk/36a722e20d081c1a5a6df417d0f8333f6c082476 -> trunk/36a722e20d081c1a5a6df417d0f8333f6c082476 2025-07-17T07:01:51.8794297Z * [new tag] trunk/36bf81e36396c5f08eb8cab7f7858555282ca6c2 -> trunk/36bf81e36396c5f08eb8cab7f7858555282ca6c2 2025-07-17T07:01:51.8795182Z * [new tag] trunk/36dd598bdac5c665e46f05d00a38d6863a99615f -> trunk/36dd598bdac5c665e46f05d00a38d6863a99615f 2025-07-17T07:01:51.8795989Z * [new tag] trunk/36f7a027b595083f3a3761b407120b3f1f4e8634 -> trunk/36f7a027b595083f3a3761b407120b3f1f4e8634 2025-07-17T07:01:51.8796863Z * [new tag] trunk/36fd1ac9324429c095f8fbc5f6d2bd4b71f18d61 -> trunk/36fd1ac9324429c095f8fbc5f6d2bd4b71f18d61 2025-07-17T07:01:51.8797783Z * [new tag] trunk/370fc49dde9e8635957d9a910d43154085264225 -> trunk/370fc49dde9e8635957d9a910d43154085264225 2025-07-17T07:01:51.8798696Z * [new tag] trunk/376c16703c7f55a7eab9d5751bfc0e91af26a735 -> trunk/376c16703c7f55a7eab9d5751bfc0e91af26a735 2025-07-17T07:01:51.8799625Z * [new tag] trunk/378c121d5e1c8d2c24124f0c0b6cf08a1341e9d9 -> trunk/378c121d5e1c8d2c24124f0c0b6cf08a1341e9d9 2025-07-17T07:01:51.8800580Z * [new tag] trunk/37ccc532f75d8537c5b5d74b1c92a7f20df96353 -> trunk/37ccc532f75d8537c5b5d74b1c92a7f20df96353 2025-07-17T07:01:51.8801497Z * [new tag] trunk/380e30a723c1fb3530cad12ac44706412df7aa71 -> trunk/380e30a723c1fb3530cad12ac44706412df7aa71 2025-07-17T07:01:51.8802791Z * [new tag] trunk/3819584f12e2a46463a9799612ace1f59d76b9cd -> trunk/3819584f12e2a46463a9799612ace1f59d76b9cd 2025-07-17T07:01:51.8803654Z * [new tag] trunk/382598ef872b2afb9a03f8d88277a6c2edeb507f -> trunk/382598ef872b2afb9a03f8d88277a6c2edeb507f 2025-07-17T07:01:51.8804613Z * [new tag] trunk/382c6190c1329e96e71eef21a19737a3eda0040b -> trunk/382c6190c1329e96e71eef21a19737a3eda0040b 2025-07-17T07:01:51.8805419Z * [new tag] trunk/38371f693b07a485705119407da2e5dc64cec4eb -> trunk/38371f693b07a485705119407da2e5dc64cec4eb 2025-07-17T07:01:51.8806379Z * [new tag] trunk/38410cf9b57079f3360c1e79601973a01cb2588c -> trunk/38410cf9b57079f3360c1e79601973a01cb2588c 2025-07-17T07:01:51.8807154Z * [new tag] trunk/3863bbb55b38985c7d64c8a0be7beb2005a9cc07 -> trunk/3863bbb55b38985c7d64c8a0be7beb2005a9cc07 2025-07-17T07:01:51.8808052Z * [new tag] trunk/386aa7200324519ea9a8eff5eb1b3c0517756d24 -> trunk/386aa7200324519ea9a8eff5eb1b3c0517756d24 2025-07-17T07:01:51.8809059Z * [new tag] trunk/386bc9e2e990a6ac29bb90ba97b71c5b85e11080 -> trunk/386bc9e2e990a6ac29bb90ba97b71c5b85e11080 2025-07-17T07:01:51.8809974Z * [new tag] trunk/38757d94f1b3d65295b5ca2d7527ea0d582a5d3b -> trunk/38757d94f1b3d65295b5ca2d7527ea0d582a5d3b 2025-07-17T07:01:51.8810900Z * [new tag] trunk/38bfd462b8fb035de08a8c0d2b7b13eba78ee870 -> trunk/38bfd462b8fb035de08a8c0d2b7b13eba78ee870 2025-07-17T07:01:51.8811929Z * [new tag] trunk/38c4d05535f4e031a2ad5f39a4a415cc3a919cfb -> trunk/38c4d05535f4e031a2ad5f39a4a415cc3a919cfb 2025-07-17T07:01:51.8812792Z * [new tag] trunk/38e1e5d54ce42d42f8920ff46d9102800b94482d -> trunk/38e1e5d54ce42d42f8920ff46d9102800b94482d 2025-07-17T07:01:51.8813767Z * [new tag] trunk/38e5e81e55fc5d85d6cf8a83c96c88578995e3fe -> trunk/38e5e81e55fc5d85d6cf8a83c96c88578995e3fe 2025-07-17T07:01:51.8814728Z * [new tag] trunk/391473cca0b0fa9667ad54c972e7b63bc589cd6d -> trunk/391473cca0b0fa9667ad54c972e7b63bc589cd6d 2025-07-17T07:01:51.8816289Z * [new tag] trunk/39270430c9e4dc7010a9754f522774d17ae9d578 -> trunk/39270430c9e4dc7010a9754f522774d17ae9d578 2025-07-17T07:01:51.8817087Z * [new tag] trunk/39456edbbad97a09e6507792e1df3ee1f2a16f98 -> trunk/39456edbbad97a09e6507792e1df3ee1f2a16f98 2025-07-17T07:01:51.8817934Z * [new tag] trunk/398fca9dcfcb9f90eae9258d728fab71f5b59db2 -> trunk/398fca9dcfcb9f90eae9258d728fab71f5b59db2 2025-07-17T07:01:51.8818819Z * [new tag] trunk/39a8f66d5939e892bcb07ef97462af47d3201491 -> trunk/39a8f66d5939e892bcb07ef97462af47d3201491 2025-07-17T07:01:51.8819595Z * [new tag] trunk/39b71d11fc2dd9b4da6d23a34eb29aefbb1df672 -> trunk/39b71d11fc2dd9b4da6d23a34eb29aefbb1df672 2025-07-17T07:01:51.8820447Z * [new tag] trunk/39c605e8b3c13e7f6b5b1bbbe3f4060e24b2a3e4 -> trunk/39c605e8b3c13e7f6b5b1bbbe3f4060e24b2a3e4 2025-07-17T07:01:51.8821395Z * [new tag] trunk/3a43dba21ff5d01bb8b259af3839c90e447c6ec0 -> trunk/3a43dba21ff5d01bb8b259af3839c90e447c6ec0 2025-07-17T07:01:51.8822243Z * [new tag] trunk/3a5677a380c79810cc370dce6ef79b0871ddcf8c -> trunk/3a5677a380c79810cc370dce6ef79b0871ddcf8c 2025-07-17T07:01:51.8823001Z * [new tag] trunk/3a7ff829c597ef26a271d52ae2a3c8354b2f447e -> trunk/3a7ff829c597ef26a271d52ae2a3c8354b2f447e 2025-07-17T07:01:51.8823912Z * [new tag] trunk/3b4b5f8d474a9c664f622012c6a61414ea7799a9 -> trunk/3b4b5f8d474a9c664f622012c6a61414ea7799a9 2025-07-17T07:01:51.8824832Z * [new tag] trunk/3b6569b1ef4b9ff25f5b75fe0a216d6d084d573f -> trunk/3b6569b1ef4b9ff25f5b75fe0a216d6d084d573f 2025-07-17T07:01:51.8825798Z * [new tag] trunk/3b7c5e6fa5c0be64ddde3a8edc5bcdc10390f1e3 -> trunk/3b7c5e6fa5c0be64ddde3a8edc5bcdc10390f1e3 2025-07-17T07:01:51.8826526Z * [new tag] trunk/3bc6bdc8660c052d932f550d5734da6f801c2630 -> trunk/3bc6bdc8660c052d932f550d5734da6f801c2630 2025-07-17T07:01:51.8828214Z * [new tag] trunk/3bdd5ae334b85a114a2b62fa17dcf204413eda32 -> trunk/3bdd5ae334b85a114a2b62fa17dcf204413eda32 2025-07-17T07:01:51.8828996Z * [new tag] trunk/3beb915004f4e26b1e7c5e7692e6e8ca9b75de46 -> trunk/3beb915004f4e26b1e7c5e7692e6e8ca9b75de46 2025-07-17T07:01:51.8829983Z * [new tag] trunk/3bec588bf5c4eda9a4d42ae4c25e6f87af7f078c -> trunk/3bec588bf5c4eda9a4d42ae4c25e6f87af7f078c 2025-07-17T07:01:51.8830848Z * [new tag] trunk/3c2324c64ac6b7497d630788a66c82705bbb044e -> trunk/3c2324c64ac6b7497d630788a66c82705bbb044e 2025-07-17T07:01:51.8831780Z * [new tag] trunk/3c7079959c8e61d7acb4f704a0ecf74c61425c2e -> trunk/3c7079959c8e61d7acb4f704a0ecf74c61425c2e 2025-07-17T07:01:51.8832718Z * [new tag] trunk/3c72b9fd8feed4588a040bc681ffe83cc7acd26d -> trunk/3c72b9fd8feed4588a040bc681ffe83cc7acd26d 2025-07-17T07:01:51.8833584Z * [new tag] trunk/3cb11877aa30c04be7ffa9b4ca1722f1270a5828 -> trunk/3cb11877aa30c04be7ffa9b4ca1722f1270a5828 2025-07-17T07:01:51.8834363Z * [new tag] trunk/3cbae6dde8f2a9c3b4a3e4e079e97307c3aa52d8 -> trunk/3cbae6dde8f2a9c3b4a3e4e079e97307c3aa52d8 2025-07-17T07:01:51.8835250Z * [new tag] trunk/3d06ff82a84a118f0ed246864d4fc01ac4726328 -> trunk/3d06ff82a84a118f0ed246864d4fc01ac4726328 2025-07-17T07:01:51.8836151Z * [new tag] trunk/3d595fd5595f38bb5ed3d390dc50e1715e173ad6 -> trunk/3d595fd5595f38bb5ed3d390dc50e1715e173ad6 2025-07-17T07:01:51.8837427Z * [new tag] trunk/3d82a1dfb59fa5e248f7499a7ecdf784d4f61c0e -> trunk/3d82a1dfb59fa5e248f7499a7ecdf784d4f61c0e 2025-07-17T07:01:51.8838190Z * [new tag] trunk/3dabc351bb5581f69825eee6b24fbac9f9260241 -> trunk/3dabc351bb5581f69825eee6b24fbac9f9260241 2025-07-17T07:01:51.8839158Z * [new tag] trunk/3dd872e6d53560933d8d7fc11357617746d37168 -> trunk/3dd872e6d53560933d8d7fc11357617746d37168 2025-07-17T07:01:51.8840050Z * [new tag] trunk/3dda80e990121eaf156014fffe6e2a4602c8b195 -> trunk/3dda80e990121eaf156014fffe6e2a4602c8b195 2025-07-17T07:01:51.8840928Z * [new tag] trunk/3df6360e8c956edd25453b21f6b7f56e0366dcb4 -> trunk/3df6360e8c956edd25453b21f6b7f56e0366dcb4 2025-07-17T07:01:51.8841867Z * [new tag] trunk/3e131f7779af03b462f0598afb2569092b57c840 -> trunk/3e131f7779af03b462f0598afb2569092b57c840 2025-07-17T07:01:51.8842861Z * [new tag] trunk/3e38feb05fffdf5b181a1f4c7a6f43b00ef1c526 -> trunk/3e38feb05fffdf5b181a1f4c7a6f43b00ef1c526 2025-07-17T07:01:51.8843654Z * [new tag] trunk/3e56a9cdfb98a9b06568ee54e3157c800d98a17e -> trunk/3e56a9cdfb98a9b06568ee54e3157c800d98a17e 2025-07-17T07:01:51.8844515Z * [new tag] trunk/3eb7084f7a3657f4ba6626aca63721a0020f1bd7 -> trunk/3eb7084f7a3657f4ba6626aca63721a0020f1bd7 2025-07-17T07:01:51.8845389Z * [new tag] trunk/3ed4384f5b4bb7ae7d12298632a258385a51446e -> trunk/3ed4384f5b4bb7ae7d12298632a258385a51446e 2025-07-17T07:01:51.8846297Z * [new tag] trunk/3ee75b7eacef6758db602e87287ef9574609b327 -> trunk/3ee75b7eacef6758db602e87287ef9574609b327 2025-07-17T07:01:51.8847189Z * [new tag] trunk/3ee8828c87ce6186607c3d3ab3852518fca49228 -> trunk/3ee8828c87ce6186607c3d3ab3852518fca49228 2025-07-17T07:01:51.8848081Z * [new tag] trunk/3efb22e09111b92bedb01b2a8385c789fe69090a -> trunk/3efb22e09111b92bedb01b2a8385c789fe69090a 2025-07-17T07:01:51.8848990Z * [new tag] trunk/3effe0c293219b00a0eae7e139fe2d9aed84bc03 -> trunk/3effe0c293219b00a0eae7e139fe2d9aed84bc03 2025-07-17T07:01:51.8849864Z * [new tag] trunk/3f29642ecf039129032cc61c986d7b62807163c0 -> trunk/3f29642ecf039129032cc61c986d7b62807163c0 2025-07-17T07:01:51.8850787Z * [new tag] trunk/3f569f9af77d51d0328f16434e64252756681daa -> trunk/3f569f9af77d51d0328f16434e64252756681daa 2025-07-17T07:01:51.8851674Z * [new tag] trunk/3f65e38b73cb8f0d0b4fb2e9a6671085d371ec40 -> trunk/3f65e38b73cb8f0d0b4fb2e9a6671085d371ec40 2025-07-17T07:01:51.8852640Z * [new tag] trunk/3f69e3b3a07efd5e9aa86d37773bf2a24dc0ea70 -> trunk/3f69e3b3a07efd5e9aa86d37773bf2a24dc0ea70 2025-07-17T07:01:51.8853402Z * [new tag] trunk/3f83e3eeca0645f4b2cd16fa7d5a591e9cf810d4 -> trunk/3f83e3eeca0645f4b2cd16fa7d5a591e9cf810d4 2025-07-17T07:01:51.8854511Z * [new tag] trunk/3f920f3d8f5bd15d2222758f21f9a5d36e4dad1f -> trunk/3f920f3d8f5bd15d2222758f21f9a5d36e4dad1f 2025-07-17T07:01:51.8855357Z * [new tag] trunk/3fd84a8592a2a87d04f0e3f236a92605e2be12df -> trunk/3fd84a8592a2a87d04f0e3f236a92605e2be12df 2025-07-17T07:01:51.8856304Z * [new tag] trunk/400f439670ff8859c17a870eb83dc5cae5a9c2e4 -> trunk/400f439670ff8859c17a870eb83dc5cae5a9c2e4 2025-07-17T07:01:51.8857180Z * [new tag] trunk/402ae09e41005ebff686e97e2f120a6b79e2afb7 -> trunk/402ae09e41005ebff686e97e2f120a6b79e2afb7 2025-07-17T07:01:51.8858252Z * [new tag] trunk/404008e3efdabeaf5b140a3aff77131461c33a0a -> trunk/404008e3efdabeaf5b140a3aff77131461c33a0a 2025-07-17T07:01:51.8859110Z * [new tag] trunk/4048a144abf82041f9b6653d9d4aa600a6a5409a -> trunk/4048a144abf82041f9b6653d9d4aa600a6a5409a 2025-07-17T07:01:51.8860322Z * [new tag] trunk/408d9884b07cf7268961bae7138a6436916d4a43 -> trunk/408d9884b07cf7268961bae7138a6436916d4a43 2025-07-17T07:01:51.8861341Z * [new tag] trunk/40a785103cf94a1dbc3e0e43d1ed6c41fb60bedb -> trunk/40a785103cf94a1dbc3e0e43d1ed6c41fb60bedb 2025-07-17T07:01:51.8862159Z * [new tag] trunk/40d02eb481670325ba80dcb42b40c3ebb347f1c7 -> trunk/40d02eb481670325ba80dcb42b40c3ebb347f1c7 2025-07-17T07:01:51.8863128Z * [new tag] trunk/40e39ae21f15f200828ab32826bed1a4b62532ba -> trunk/40e39ae21f15f200828ab32826bed1a4b62532ba 2025-07-17T07:01:51.8864047Z * [new tag] trunk/40fefe2871a5561b67ec268bab19db60e733f2d4 -> trunk/40fefe2871a5561b67ec268bab19db60e733f2d4 2025-07-17T07:01:51.8864822Z * [new tag] trunk/414ad470450c654d97e73bef704a7b596b5b4cbc -> trunk/414ad470450c654d97e73bef704a7b596b5b4cbc 2025-07-17T07:01:51.8865925Z * [new tag] trunk/415dfabe9b569b71098a2f874f3fc67ad2a4fc2e -> trunk/415dfabe9b569b71098a2f874f3fc67ad2a4fc2e 2025-07-17T07:01:51.8866747Z * [new tag] trunk/4162c0f70297818abd70f2fe7424dcd3cc9b2543 -> trunk/4162c0f70297818abd70f2fe7424dcd3cc9b2543 2025-07-17T07:01:51.8867678Z * [new tag] trunk/41910d7a94d2f3ffe06f7c0c30971c71b9c3b09e -> trunk/41910d7a94d2f3ffe06f7c0c30971c71b9c3b09e 2025-07-17T07:01:51.8868810Z * [new tag] trunk/41971335c98b0881e0784085096eceace575d563 -> trunk/41971335c98b0881e0784085096eceace575d563 2025-07-17T07:01:51.8869649Z * [new tag] trunk/41e8b826d07bbf707bc2c64a78b4dc15e8f3c358 -> trunk/41e8b826d07bbf707bc2c64a78b4dc15e8f3c358 2025-07-17T07:01:51.8870542Z * [new tag] trunk/41f6acef83d280a18909d58f8442e145c9e7ea6f -> trunk/41f6acef83d280a18909d58f8442e145c9e7ea6f 2025-07-17T07:01:51.8871494Z * [new tag] trunk/42015db6a9602f40250f9afc18a3fbad4ca4ef39 -> trunk/42015db6a9602f40250f9afc18a3fbad4ca4ef39 2025-07-17T07:01:51.8874829Z * [new tag] trunk/4237ee3c33b0edb01db96d8c1ecf8f5d2cf184bc -> trunk/4237ee3c33b0edb01db96d8c1ecf8f5d2cf184bc 2025-07-17T07:01:51.8875727Z * [new tag] trunk/4283d96bcdf9aaa4289985267186d74ba6534ee5 -> trunk/4283d96bcdf9aaa4289985267186d74ba6534ee5 2025-07-17T07:01:51.8876608Z * [new tag] trunk/42b48ee67229286127390000f103a11dfc8901f5 -> trunk/42b48ee67229286127390000f103a11dfc8901f5 2025-07-17T07:01:51.8877584Z * [new tag] trunk/42ff6a4a5c4e0d77bd18fcc5426622f1b8f20add -> trunk/42ff6a4a5c4e0d77bd18fcc5426622f1b8f20add 2025-07-17T07:01:51.8878489Z * [new tag] trunk/430cc1c636380a7d50652df646274f7008a76747 -> trunk/430cc1c636380a7d50652df646274f7008a76747 2025-07-17T07:01:51.8879391Z * [new tag] trunk/4311aea5e7568c9956a9f0d694f1980c387b6a37 -> trunk/4311aea5e7568c9956a9f0d694f1980c387b6a37 2025-07-17T07:01:51.8880340Z * [new tag] trunk/433a2471023f77a4e62e880ef59f22caf939c227 -> trunk/433a2471023f77a4e62e880ef59f22caf939c227 2025-07-17T07:01:51.8881265Z * [new tag] trunk/43523bf1682c2926a84a1f65f00fabd3d34db4f2 -> trunk/43523bf1682c2926a84a1f65f00fabd3d34db4f2 2025-07-17T07:01:51.8882049Z * [new tag] trunk/43a09189c68fe02bd9d8433c4a144ffc9bbf895c -> trunk/43a09189c68fe02bd9d8433c4a144ffc9bbf895c 2025-07-17T07:01:51.8882964Z * [new tag] trunk/43f72163275ce1503b1de1480642c61d20e6158d -> trunk/43f72163275ce1503b1de1480642c61d20e6158d 2025-07-17T07:01:51.8883939Z * [new tag] trunk/442aca44d603ae6c2b7d2aa2190cc91f970c4202 -> trunk/442aca44d603ae6c2b7d2aa2190cc91f970c4202 2025-07-17T07:01:51.8884919Z * [new tag] trunk/44303caabfa0bdc5688d693d491ba7c3fdb40f3a -> trunk/44303caabfa0bdc5688d693d491ba7c3fdb40f3a 2025-07-17T07:01:51.8885838Z * [new tag] trunk/443b5b43c3d40b89bbb5db70f3f05a44b2a4ec66 -> trunk/443b5b43c3d40b89bbb5db70f3f05a44b2a4ec66 2025-07-17T07:01:51.8886784Z * [new tag] trunk/4486a6dbfd65ef490cfe73e0630929e85f61ee16 -> trunk/4486a6dbfd65ef490cfe73e0630929e85f61ee16 2025-07-17T07:01:51.8887535Z * [new tag] trunk/4491326fb0c0e67eca1598ae33c41cdfced2cd33 -> trunk/4491326fb0c0e67eca1598ae33c41cdfced2cd33 2025-07-17T07:01:51.8888513Z * [new tag] trunk/44a5f93462bd4aa4f36ae2573685ed7ba458c4c9 -> trunk/44a5f93462bd4aa4f36ae2573685ed7ba458c4c9 2025-07-17T07:01:51.8889815Z * [new tag] trunk/44d0800d60e78fef8ab332e307c3134e3c276ba4 -> trunk/44d0800d60e78fef8ab332e307c3134e3c276ba4 2025-07-17T07:01:51.8890683Z * [new tag] trunk/44df7cf28dd37a4d900eb6b2f78dabf72b209d9b -> trunk/44df7cf28dd37a4d900eb6b2f78dabf72b209d9b 2025-07-17T07:01:51.8891543Z * [new tag] trunk/44f5b9312290866584088e2e42228c484f669faf -> trunk/44f5b9312290866584088e2e42228c484f669faf 2025-07-17T07:01:51.8892329Z * [new tag] trunk/4500a4aa50141ed30e093ef8491b30d1d1287348 -> trunk/4500a4aa50141ed30e093ef8491b30d1d1287348 2025-07-17T07:01:51.8893214Z * [new tag] trunk/451b525bf0cb8840adbde1b6280c7b199f733ced -> trunk/451b525bf0cb8840adbde1b6280c7b199f733ced 2025-07-17T07:01:51.8894131Z * [new tag] trunk/45382b284d03015f3e6eb83a959f0e54a3ebd688 -> trunk/45382b284d03015f3e6eb83a959f0e54a3ebd688 2025-07-17T07:01:51.8895108Z * [new tag] trunk/453bc9fbdfb97fef925477cbbf4948e93fd22756 -> trunk/453bc9fbdfb97fef925477cbbf4948e93fd22756 2025-07-17T07:01:51.8896061Z * [new tag] trunk/45596ec58f5bc5489911ee932c2a55a7ff40d0a0 -> trunk/45596ec58f5bc5489911ee932c2a55a7ff40d0a0 2025-07-17T07:01:51.8896970Z * [new tag] trunk/455dfd258980294f0745bd90aee12a323e37224d -> trunk/455dfd258980294f0745bd90aee12a323e37224d 2025-07-17T07:01:51.8897853Z * [new tag] trunk/456b7451c78096fd512c60e26cc618386a251bc7 -> trunk/456b7451c78096fd512c60e26cc618386a251bc7 2025-07-17T07:01:51.8898736Z * [new tag] trunk/456f40cb09c6e5e0cd268d1601b89fc5ac1987ea -> trunk/456f40cb09c6e5e0cd268d1601b89fc5ac1987ea 2025-07-17T07:01:51.8899632Z * [new tag] trunk/4574b39aa45f0250cba04aa3cb053a686c4888b2 -> trunk/4574b39aa45f0250cba04aa3cb053a686c4888b2 2025-07-17T07:01:51.8900753Z * [new tag] trunk/457dd79927db6ae7f1c53f36768b7ac93b61e507 -> trunk/457dd79927db6ae7f1c53f36768b7ac93b61e507 2025-07-17T07:01:51.8901513Z * [new tag] trunk/4585c33e74079af8e9067bf39970b93c3f13629f -> trunk/4585c33e74079af8e9067bf39970b93c3f13629f 2025-07-17T07:01:51.8902437Z * [new tag] trunk/458cc7213baf01ab742a2250c7a31f7796fcb1ef -> trunk/458cc7213baf01ab742a2250c7a31f7796fcb1ef 2025-07-17T07:01:51.8903362Z * [new tag] trunk/45c5a232373cac1b1524259f20758bfb56dde5f2 -> trunk/45c5a232373cac1b1524259f20758bfb56dde5f2 2025-07-17T07:01:51.8904278Z * [new tag] trunk/4609699bfd440d1fe603c5ad9a942e45410094c1 -> trunk/4609699bfd440d1fe603c5ad9a942e45410094c1 2025-07-17T07:01:51.8905177Z * [new tag] trunk/4628f1b7a9313df98346d582d9a789661a823e27 -> trunk/4628f1b7a9313df98346d582d9a789661a823e27 2025-07-17T07:01:51.8906296Z * [new tag] trunk/463fe36532bc165b46c12cef4948f039434e0924 -> trunk/463fe36532bc165b46c12cef4948f039434e0924 2025-07-17T07:01:51.8907079Z * [new tag] trunk/4657a84bc55b6ce12f21706de2b90e1d43784f57 -> trunk/4657a84bc55b6ce12f21706de2b90e1d43784f57 2025-07-17T07:01:51.8907963Z * [new tag] trunk/46915b13614dbac90724d0f1802b8e0db037c9e4 -> trunk/46915b13614dbac90724d0f1802b8e0db037c9e4 2025-07-17T07:01:51.8908877Z * [new tag] trunk/473208cb18d543e8f968918a6b3c9defa8a4ae10 -> trunk/473208cb18d543e8f968918a6b3c9defa8a4ae10 2025-07-17T07:01:51.8909771Z * [new tag] trunk/476874b37fff42a46d25dfac720ef4c71ec74fe0 -> trunk/476874b37fff42a46d25dfac720ef4c71ec74fe0 2025-07-17T07:01:51.8911101Z * [new tag] trunk/4781b0ee6057fdf2a82d2f9ed30299f219267a71 -> trunk/4781b0ee6057fdf2a82d2f9ed30299f219267a71 2025-07-17T07:01:51.8911949Z * [new tag] trunk/4781d72faa6b72bf96fde9bedfca06a5285eebfb -> trunk/4781d72faa6b72bf96fde9bedfca06a5285eebfb 2025-07-17T07:01:51.8912924Z * [new tag] trunk/47c8810b5275179833d6b33ca3d70922f485272c -> trunk/47c8810b5275179833d6b33ca3d70922f485272c 2025-07-17T07:01:51.8915008Z * [new tag] trunk/47f10d0ad0dda281c886ff08ac2f938207027316 -> trunk/47f10d0ad0dda281c886ff08ac2f938207027316 2025-07-17T07:01:51.8915854Z * [new tag] trunk/4805a6ead6f1e7f32351056e2602be4e908f69b7 -> trunk/4805a6ead6f1e7f32351056e2602be4e908f69b7 2025-07-17T07:01:51.8916734Z * [new tag] trunk/48315181c75e43cab5957197d42e053d66b3fe1c -> trunk/48315181c75e43cab5957197d42e053d66b3fe1c 2025-07-17T07:01:51.8917870Z * [new tag] trunk/4851863e3f98e1ffba1d5801f3533031ef4f905d -> trunk/4851863e3f98e1ffba1d5801f3533031ef4f905d 2025-07-17T07:01:51.8918693Z * [new tag] trunk/48560eef80e97e855cbb8e2814acefe8f5cc6fbd -> trunk/48560eef80e97e855cbb8e2814acefe8f5cc6fbd 2025-07-17T07:01:51.8919593Z * [new tag] trunk/4886ba64dceb24b8a0444e574abb907a18fdeee9 -> trunk/4886ba64dceb24b8a0444e574abb907a18fdeee9 2025-07-17T07:01:51.8920457Z * [new tag] trunk/48921721d8ed32a2c7f80605b434f9fcb2f948b4 -> trunk/48921721d8ed32a2c7f80605b434f9fcb2f948b4 2025-07-17T07:01:51.8921423Z * [new tag] trunk/48de3da2539cecaee14af8e3841c133c9c0c0f1c -> trunk/48de3da2539cecaee14af8e3841c133c9c0c0f1c 2025-07-17T07:01:51.8922188Z * [new tag] trunk/48e7b62d3abda4a3eac2f538c9f35e56093c4ece -> trunk/48e7b62d3abda4a3eac2f538c9f35e56093c4ece 2025-07-17T07:01:51.8923103Z * [new tag] trunk/4918502d2e685270cea9f1fd733a3414ac1ca5d7 -> trunk/4918502d2e685270cea9f1fd733a3414ac1ca5d7 2025-07-17T07:01:51.8924009Z * [new tag] trunk/493bd625e252dea02e871346beaa49745b4b2663 -> trunk/493bd625e252dea02e871346beaa49745b4b2663 2025-07-17T07:01:51.8924911Z * [new tag] trunk/493f42a5417a8ac33d1070478d1869ea4f207b5b -> trunk/493f42a5417a8ac33d1070478d1869ea4f207b5b 2025-07-17T07:01:51.8925829Z * [new tag] trunk/495c317005ad656dee752b6ad7bd5541b04e388e -> trunk/495c317005ad656dee752b6ad7bd5541b04e388e 2025-07-17T07:01:51.8926723Z * [new tag] trunk/49888e6be0dcc00ba546746425893bb17c287248 -> trunk/49888e6be0dcc00ba546746425893bb17c287248 2025-07-17T07:01:51.8927492Z * [new tag] trunk/49ee1e7106db00778345efd7c70cc12d2ca6a91a -> trunk/49ee1e7106db00778345efd7c70cc12d2ca6a91a 2025-07-17T07:01:51.8928266Z * [new tag] trunk/4a26bb8a12ba5cb437ed5b7f035b5b533e07549e -> trunk/4a26bb8a12ba5cb437ed5b7f035b5b533e07549e 2025-07-17T07:01:51.8929254Z * [new tag] trunk/4a4cac0cefea3661cc69cfdafdba64832ee0841a -> trunk/4a4cac0cefea3661cc69cfdafdba64832ee0841a 2025-07-17T07:01:51.8930167Z * [new tag] trunk/4a80ddfbe70bf6b75acc3177e5d2095b285da841 -> trunk/4a80ddfbe70bf6b75acc3177e5d2095b285da841 2025-07-17T07:01:51.8931168Z * [new tag] trunk/4a8f5e752beb5a6809ba866c83f32dd464a47bfd -> trunk/4a8f5e752beb5a6809ba866c83f32dd464a47bfd 2025-07-17T07:01:51.8932045Z * [new tag] trunk/4a954fc1857ff41be604e8cae68908d41124b419 -> trunk/4a954fc1857ff41be604e8cae68908d41124b419 2025-07-17T07:01:51.8932961Z * [new tag] trunk/4ab4d29cbee1f90daf14444d5ca3a53653c7784d -> trunk/4ab4d29cbee1f90daf14444d5ca3a53653c7784d 2025-07-17T07:01:51.8933883Z * [new tag] trunk/4b11428cb5b3d97f3068a2dc4c55cee6ddd41979 -> trunk/4b11428cb5b3d97f3068a2dc4c55cee6ddd41979 2025-07-17T07:01:51.8934822Z * [new tag] trunk/4b4c2a7b1dfd88313801878c5b4e3855fe5232df -> trunk/4b4c2a7b1dfd88313801878c5b4e3855fe5232df 2025-07-17T07:01:51.8935783Z * [new tag] trunk/4b55871e06d6bad54eac45e45a9af615d758a39f -> trunk/4b55871e06d6bad54eac45e45a9af615d758a39f 2025-07-17T07:01:51.8936716Z * [new tag] trunk/4b6cbf528b8f7b95e02e3a0233945e0bb8b00f44 -> trunk/4b6cbf528b8f7b95e02e3a0233945e0bb8b00f44 2025-07-17T07:01:51.8937973Z * [new tag] trunk/4b9a6f7211123511e856ac8c8524bc332a741241 -> trunk/4b9a6f7211123511e856ac8c8524bc332a741241 2025-07-17T07:01:51.8938747Z * [new tag] trunk/4bb936d8b77c959efbff0bdb68f63e5a5faf60a9 -> trunk/4bb936d8b77c959efbff0bdb68f63e5a5faf60a9 2025-07-17T07:01:51.8939641Z * [new tag] trunk/4bc3e4b497948af0a48d08b427339efc8960fedb -> trunk/4bc3e4b497948af0a48d08b427339efc8960fedb 2025-07-17T07:01:51.8940507Z * [new tag] trunk/4bd18e31e5a38d0e84ce915b1fa124058c6373fa -> trunk/4bd18e31e5a38d0e84ce915b1fa124058c6373fa 2025-07-17T07:01:51.8941406Z * [new tag] trunk/4c0091fda65b714fa73671a15e379f814af153e0 -> trunk/4c0091fda65b714fa73671a15e379f814af153e0 2025-07-17T07:01:51.8942585Z * [new tag] trunk/4c0aa37dda605e7cf3372c71940d7e3a93ecef8d -> trunk/4c0aa37dda605e7cf3372c71940d7e3a93ecef8d 2025-07-17T07:01:51.8943367Z * [new tag] trunk/4c3da611c2a80fa5073b164974d7edc0577b9ca7 -> trunk/4c3da611c2a80fa5073b164974d7edc0577b9ca7 2025-07-17T07:01:51.8944289Z * [new tag] trunk/4c59edf0c5838087cbe09c67c6e0e776467a5f2d -> trunk/4c59edf0c5838087cbe09c67c6e0e776467a5f2d 2025-07-17T07:01:51.8945035Z * [new tag] trunk/4c8eb65efb147cd263fc02f5588683f530363a0f -> trunk/4c8eb65efb147cd263fc02f5588683f530363a0f 2025-07-17T07:01:51.8945963Z * [new tag] trunk/4cbbc8b4583e9dac4af7e8d1d8535546de21178c -> trunk/4cbbc8b4583e9dac4af7e8d1d8535546de21178c 2025-07-17T07:01:51.8946861Z * [new tag] trunk/4cc13c4af6dbd5aa36545ef577d0bee517cf8c57 -> trunk/4cc13c4af6dbd5aa36545ef577d0bee517cf8c57 2025-07-17T07:01:51.8947626Z * [new tag] trunk/4cd6e96bf0d13f168772f6ad44267b1ac7632a97 -> trunk/4cd6e96bf0d13f168772f6ad44267b1ac7632a97 2025-07-17T07:01:51.8948553Z * [new tag] trunk/4cdbdcdbcf2b7f7a52a5c61cb35064f7b4aa2e44 -> trunk/4cdbdcdbcf2b7f7a52a5c61cb35064f7b4aa2e44 2025-07-17T07:01:51.8949941Z * [new tag] trunk/4ce6e6ec8890a3f6ee604c9efb3ff153825ce575 -> trunk/4ce6e6ec8890a3f6ee604c9efb3ff153825ce575 2025-07-17T07:01:51.8950810Z * [new tag] trunk/4cfc0a320897b6ec75b85cfc07bc29009a824177 -> trunk/4cfc0a320897b6ec75b85cfc07bc29009a824177 2025-07-17T07:01:51.8951676Z * [new tag] trunk/4d055982e38f59fdb2a4c9d8855e58548bc42c12 -> trunk/4d055982e38f59fdb2a4c9d8855e58548bc42c12 2025-07-17T07:01:51.8952668Z * [new tag] trunk/4d3ecefda5a41df678fd68b020c521db95e9fbde -> trunk/4d3ecefda5a41df678fd68b020c521db95e9fbde 2025-07-17T07:01:51.8953534Z * [new tag] trunk/4d5d627e5ff3310318f9df80348ee419324228df -> trunk/4d5d627e5ff3310318f9df80348ee419324228df 2025-07-17T07:01:51.8954418Z * [new tag] trunk/4d9d884c3f5adc89b59d25d5a080498b76f9bb39 -> trunk/4d9d884c3f5adc89b59d25d5a080498b76f9bb39 2025-07-17T07:01:51.8957181Z * [new tag] trunk/4da98351b9e231bbbe83b5c590cf7d2ea382333a -> trunk/4da98351b9e231bbbe83b5c590cf7d2ea382333a 2025-07-17T07:01:51.8957592Z * [new tag] trunk/4dce5b71a0751aa4287c3a80adf07afc773ec4db -> trunk/4dce5b71a0751aa4287c3a80adf07afc773ec4db 2025-07-17T07:01:51.8957993Z * [new tag] trunk/4e13eca713c60ca63c1116823b99d2461a7422ef -> trunk/4e13eca713c60ca63c1116823b99d2461a7422ef 2025-07-17T07:01:51.8958529Z * [new tag] trunk/4e19477196547eb2e8157d6d132689373ffcf0fa -> trunk/4e19477196547eb2e8157d6d132689373ffcf0fa 2025-07-17T07:01:51.8959472Z * [new tag] trunk/4e8dd11be17a974e0ee5503b3061d4d47467844f -> trunk/4e8dd11be17a974e0ee5503b3061d4d47467844f 2025-07-17T07:01:51.8960317Z * [new tag] trunk/4ebd2690654ffe487c43941ebaea41b99914d8b7 -> trunk/4ebd2690654ffe487c43941ebaea41b99914d8b7 2025-07-17T07:01:51.8961413Z * [new tag] trunk/4ed1b03f7275075b1783d1deab946ced4ceba4d6 -> trunk/4ed1b03f7275075b1783d1deab946ced4ceba4d6 2025-07-17T07:01:51.8962206Z * [new tag] trunk/4ee4863232b9e07728d85254768bcba3aadc9b9a -> trunk/4ee4863232b9e07728d85254768bcba3aadc9b9a 2025-07-17T07:01:51.8963301Z * [new tag] trunk/4f36743f5eef2d9c40357eb5d8d8b1aeeacfbb2a -> trunk/4f36743f5eef2d9c40357eb5d8d8b1aeeacfbb2a 2025-07-17T07:01:51.8964090Z * [new tag] trunk/4f5b34427b57e8b876d12e6ce551f04a7c31cacf -> trunk/4f5b34427b57e8b876d12e6ce551f04a7c31cacf 2025-07-17T07:01:51.8965006Z * [new tag] trunk/4f5be5661240427fbbfbe1d137dce53ca32846b5 -> trunk/4f5be5661240427fbbfbe1d137dce53ca32846b5 2025-07-17T07:01:51.8965913Z * [new tag] trunk/4f70fbbd16d1f0d62af082246a95e56cffccc860 -> trunk/4f70fbbd16d1f0d62af082246a95e56cffccc860 2025-07-17T07:01:51.8966985Z * [new tag] trunk/4ff0e033c16aae064f05ee9300053cd304891673 -> trunk/4ff0e033c16aae064f05ee9300053cd304891673 2025-07-17T07:01:51.8967680Z * [new tag] trunk/4ff9b7fa3116b1c429e577830ac6e816734ad029 -> trunk/4ff9b7fa3116b1c429e577830ac6e816734ad029 2025-07-17T07:01:51.8968545Z * [new tag] trunk/502486d9466e81cda76c91d33eb869235480bee9 -> trunk/502486d9466e81cda76c91d33eb869235480bee9 2025-07-17T07:01:51.8969341Z * [new tag] trunk/503362d019b3782581492af7767945dbd75ca1c9 -> trunk/503362d019b3782581492af7767945dbd75ca1c9 2025-07-17T07:01:51.8970102Z * [new tag] trunk/508cdc4fc9f7a91b5fd3f20de38d673daebf1af3 -> trunk/508cdc4fc9f7a91b5fd3f20de38d673daebf1af3 2025-07-17T07:01:51.8971051Z * [new tag] trunk/50940270ae179134cd4f9072f04ffdd55daf808e -> trunk/50940270ae179134cd4f9072f04ffdd55daf808e 2025-07-17T07:01:51.8972094Z * [new tag] trunk/50b2069b61942e923528c94ccbbc8ab5e92c381e -> trunk/50b2069b61942e923528c94ccbbc8ab5e92c381e 2025-07-17T07:01:51.8973310Z * [new tag] trunk/50d8168c8b62990fda86398c9dee1dc8cbd6908d -> trunk/50d8168c8b62990fda86398c9dee1dc8cbd6908d 2025-07-17T07:01:51.8974240Z * [new tag] trunk/510c398a4f1dff9217938159d605290149358332 -> trunk/510c398a4f1dff9217938159d605290149358332 2025-07-17T07:01:51.8975218Z * [new tag] trunk/5116293f7eb587ada6076f3c3ea1711a0ec7ab4a -> trunk/5116293f7eb587ada6076f3c3ea1711a0ec7ab4a 2025-07-17T07:01:51.8976396Z * [new tag] trunk/5118a8f8a5a7906e26bcbd35370de3416b5cdab0 -> trunk/5118a8f8a5a7906e26bcbd35370de3416b5cdab0 2025-07-17T07:01:51.8977324Z * [new tag] trunk/51560797ce70aea353585d8381ee88c2e6c81075 -> trunk/51560797ce70aea353585d8381ee88c2e6c81075 2025-07-17T07:01:51.8978297Z * [new tag] trunk/517d2995e09603017f07f065c1ae5b8d25962cd2 -> trunk/517d2995e09603017f07f065c1ae5b8d25962cd2 2025-07-17T07:01:51.8979155Z * [new tag] trunk/51853b358e698f6a42c1eef045ab8fa766129ee1 -> trunk/51853b358e698f6a42c1eef045ab8fa766129ee1 2025-07-17T07:01:51.8979947Z * [new tag] trunk/51a708ffc679b13f99e4c7cf19bc00082a3266a6 -> trunk/51a708ffc679b13f99e4c7cf19bc00082a3266a6 2025-07-17T07:01:51.8981038Z * [new tag] trunk/51eb8e8f84bb9aa901cff17dd649e18b17a8908c -> trunk/51eb8e8f84bb9aa901cff17dd649e18b17a8908c 2025-07-17T07:01:51.8982324Z * [new tag] trunk/52214485747ceb0ea47ce5a09babb0b82b3282b8 -> trunk/52214485747ceb0ea47ce5a09babb0b82b3282b8 2025-07-17T07:01:51.8983172Z * [new tag] trunk/522a18bd6c094c766c0de9ef539682d1a3f04a15 -> trunk/522a18bd6c094c766c0de9ef539682d1a3f04a15 2025-07-17T07:01:51.8983975Z * [new tag] trunk/523b637cbeb69665072a2cf489ec1c5313b57670 -> trunk/523b637cbeb69665072a2cf489ec1c5313b57670 2025-07-17T07:01:51.8984933Z * [new tag] trunk/524e8270955788c53473497fd2cc16c5aa0e4c67 -> trunk/524e8270955788c53473497fd2cc16c5aa0e4c67 2025-07-17T07:01:51.8986988Z * [new tag] trunk/5264f8cd8d08272003298cdefe6bd60b1b8c80b4 -> trunk/5264f8cd8d08272003298cdefe6bd60b1b8c80b4 2025-07-17T07:01:51.8987838Z * [new tag] trunk/52772765e034622d1a86476e4bb19c28e3945f74 -> trunk/52772765e034622d1a86476e4bb19c28e3945f74 2025-07-17T07:01:51.8988835Z * [new tag] trunk/5285d1024376396a00fe750fe3d628c65dc26254 -> trunk/5285d1024376396a00fe750fe3d628c65dc26254 2025-07-17T07:01:51.8989646Z * [new tag] trunk/529e0357c6c4e74f8cd32c29198c5f1c9f6e329d -> trunk/529e0357c6c4e74f8cd32c29198c5f1c9f6e329d 2025-07-17T07:01:51.8990540Z * [new tag] trunk/52e4e41cbc36a5cf44395ff84ca2d069263560de -> trunk/52e4e41cbc36a5cf44395ff84ca2d069263560de 2025-07-17T07:01:51.8991440Z * [new tag] trunk/52edfb2cbcdded865645cc82bb1bb501fdcbdb52 -> trunk/52edfb2cbcdded865645cc82bb1bb501fdcbdb52 2025-07-17T07:01:51.8992605Z * [new tag] trunk/52f873adc23e7069ce95933d97ee514022cfbf7d -> trunk/52f873adc23e7069ce95933d97ee514022cfbf7d 2025-07-17T07:01:51.8993398Z * [new tag] trunk/53057fc16af2d381a61fe891b52ac8731ee9dfae -> trunk/53057fc16af2d381a61fe891b52ac8731ee9dfae 2025-07-17T07:01:51.8994322Z * [new tag] trunk/534c454e77ac0eefd52d63c60d42911e7f9617ea -> trunk/534c454e77ac0eefd52d63c60d42911e7f9617ea 2025-07-17T07:01:51.8995210Z * [new tag] trunk/53ab73090eb66d96e45ec134e41cf67266208954 -> trunk/53ab73090eb66d96e45ec134e41cf67266208954 2025-07-17T07:01:51.8996150Z * [new tag] trunk/53cd18f6b31bc2ea62985fda87e96aa17cd11bc1 -> trunk/53cd18f6b31bc2ea62985fda87e96aa17cd11bc1 2025-07-17T07:01:51.8997006Z * [new tag] trunk/53d06e18d9b165cb8aa0a5a3cbb6837ac3000c59 -> trunk/53d06e18d9b165cb8aa0a5a3cbb6837ac3000c59 2025-07-17T07:01:51.8998257Z * [new tag] trunk/53e0b9c3936176521ed8d71c00abd5b7499057c2 -> trunk/53e0b9c3936176521ed8d71c00abd5b7499057c2 2025-07-17T07:01:51.8999063Z * [new tag] trunk/541297daae63f74a90bac52c5db3540bf84bc971 -> trunk/541297daae63f74a90bac52c5db3540bf84bc971 2025-07-17T07:01:51.9000031Z * [new tag] trunk/5430990bd7d050f249b1cdf1f7d1016c4b17488d -> trunk/5430990bd7d050f249b1cdf1f7d1016c4b17488d 2025-07-17T07:01:51.9000917Z * [new tag] trunk/5435e7539930be7bd038683ce096038b30c5bb5f -> trunk/5435e7539930be7bd038683ce096038b30c5bb5f 2025-07-17T07:01:51.9002034Z * [new tag] trunk/545fbd58dc8b4f688f6b259cb671b3ec16a46810 -> trunk/545fbd58dc8b4f688f6b259cb671b3ec16a46810 2025-07-17T07:01:51.9002820Z * [new tag] trunk/54701a0c943245402fceeab0e55d7aa394303c20 -> trunk/54701a0c943245402fceeab0e55d7aa394303c20 2025-07-17T07:01:51.9003658Z * [new tag] trunk/5484890539823d9867c74209588abe095c9232a1 -> trunk/5484890539823d9867c74209588abe095c9232a1 2025-07-17T07:01:51.9004622Z * [new tag] trunk/548c9d8281d9b3d6d12e90c40b3387faf0e24c61 -> trunk/548c9d8281d9b3d6d12e90c40b3387faf0e24c61 2025-07-17T07:01:51.9005503Z * [new tag] trunk/54976bca103fcf2b5037cc0cd1b37c4639fcf779 -> trunk/54976bca103fcf2b5037cc0cd1b37c4639fcf779 2025-07-17T07:01:51.9006643Z * [new tag] trunk/54998c2daaf5b8919cf82367492dd3c5177ab935 -> trunk/54998c2daaf5b8919cf82367492dd3c5177ab935 2025-07-17T07:01:51.9007407Z * [new tag] trunk/54a4d34d100f4ebe45f486451967daba896e839c -> trunk/54a4d34d100f4ebe45f486451967daba896e839c 2025-07-17T07:01:51.9008275Z * [new tag] trunk/54a7e5b5983d237b324b50703bcb0919a6c4c296 -> trunk/54a7e5b5983d237b324b50703bcb0919a6c4c296 2025-07-17T07:01:51.9009253Z * [new tag] trunk/54b8087f638de57c1c93c4624d03f2f2c512b90e -> trunk/54b8087f638de57c1c93c4624d03f2f2c512b90e 2025-07-17T07:01:51.9010399Z * [new tag] trunk/55108074c0795be3b617d3b13b06794f63e1f8ca -> trunk/55108074c0795be3b617d3b13b06794f63e1f8ca 2025-07-17T07:01:51.9011665Z * [new tag] trunk/554b5680405e6197a985040ffe88157beb637450 -> trunk/554b5680405e6197a985040ffe88157beb637450 2025-07-17T07:01:51.9012568Z * [new tag] trunk/555f3562541992b66a550eca8e8740884b1247f8 -> trunk/555f3562541992b66a550eca8e8740884b1247f8 2025-07-17T07:01:51.9013522Z * [new tag] trunk/5596cefba623fcc0739f1e9568131a85973cf1f9 -> trunk/5596cefba623fcc0739f1e9568131a85973cf1f9 2025-07-17T07:01:51.9014558Z * [new tag] trunk/55d888a616be3c94d8e4073b4d1580541692997d -> trunk/55d888a616be3c94d8e4073b4d1580541692997d 2025-07-17T07:01:51.9015471Z * [new tag] trunk/55dae0bf7a4b501be91d0e022e675337dfe288ff -> trunk/55dae0bf7a4b501be91d0e022e675337dfe288ff 2025-07-17T07:01:51.9016416Z * [new tag] trunk/55ef7b15e0b2de903bfc26adfb0788ecfbcb4ed4 -> trunk/55ef7b15e0b2de903bfc26adfb0788ecfbcb4ed4 2025-07-17T07:01:51.9017297Z * [new tag] trunk/5606c516fd87e5c3594177e4ca64c3cac7fdafd5 -> trunk/5606c516fd87e5c3594177e4ca64c3cac7fdafd5 2025-07-17T07:01:51.9018063Z * [new tag] trunk/5633283574c458bd6a3cbb6a0a890f0cb9c8b2b5 -> trunk/5633283574c458bd6a3cbb6a0a890f0cb9c8b2b5 2025-07-17T07:01:51.9019000Z * [new tag] trunk/563fd95563c5edd732ae260b3bd3d0c38822ab57 -> trunk/563fd95563c5edd732ae260b3bd3d0c38822ab57 2025-07-17T07:01:51.9020150Z * [new tag] trunk/565fd079099d33a81c11d7b36581f09441ba6efa -> trunk/565fd079099d33a81c11d7b36581f09441ba6efa 2025-07-17T07:01:51.9020820Z * [new tag] trunk/568ca89bac9a80a66d664593a26ae69ac604796e -> trunk/568ca89bac9a80a66d664593a26ae69ac604796e 2025-07-17T07:01:51.9021720Z * [new tag] trunk/5692cbb818f1af9fcce9234e202d232afa5f3fa3 -> trunk/5692cbb818f1af9fcce9234e202d232afa5f3fa3 2025-07-17T07:01:51.9022694Z * [new tag] trunk/56b03df6ac5b4185a2b7b92f253565500a5b51ca -> trunk/56b03df6ac5b4185a2b7b92f253565500a5b51ca 2025-07-17T07:01:51.9023629Z * [new tag] trunk/56b3bf0c74f5837fd4fa6293bc515d353cb10295 -> trunk/56b3bf0c74f5837fd4fa6293bc515d353cb10295 2025-07-17T07:01:51.9024583Z * [new tag] trunk/56c69bedcc7e2211e5a3d6249e51b1674be5d10e -> trunk/56c69bedcc7e2211e5a3d6249e51b1674be5d10e 2025-07-17T07:01:51.9025445Z * [new tag] trunk/5763ec5f8d11df5eea962bedc74563394c0e273f -> trunk/5763ec5f8d11df5eea962bedc74563394c0e273f 2025-07-17T07:01:51.9026361Z * [new tag] trunk/577baa411675ed241c7d5cf79f25c13e29dac583 -> trunk/577baa411675ed241c7d5cf79f25c13e29dac583 2025-07-17T07:01:51.9027263Z * [new tag] trunk/57e4d7b5ccb8e35b434b62cc1f9aafb7d4d82b55 -> trunk/57e4d7b5ccb8e35b434b62cc1f9aafb7d4d82b55 2025-07-17T07:01:51.9028198Z * [new tag] trunk/584a0510b30b2472e54197d6b67b6f5f5e8ac807 -> trunk/584a0510b30b2472e54197d6b67b6f5f5e8ac807 2025-07-17T07:01:51.9029043Z * [new tag] trunk/588b5fb94bc6d2195ba5a4287b7feebe736da7db -> trunk/588b5fb94bc6d2195ba5a4287b7feebe736da7db 2025-07-17T07:01:51.9029968Z * [new tag] trunk/58e5d20c57e6baafd82b3b49c0e52931f5ae5d51 -> trunk/58e5d20c57e6baafd82b3b49c0e52931f5ae5d51 2025-07-17T07:01:51.9030856Z * [new tag] trunk/590607c5992d200b86361c5b68d53c93d8099193 -> trunk/590607c5992d200b86361c5b68d53c93d8099193 2025-07-17T07:01:51.9031838Z * [new tag] trunk/590fe4d2d7565f2045ef1ad4f4aad1f3b3de7aa3 -> trunk/590fe4d2d7565f2045ef1ad4f4aad1f3b3de7aa3 2025-07-17T07:01:51.9032764Z * [new tag] trunk/5951fcd50acc51bb91beae8488758f35219da849 -> trunk/5951fcd50acc51bb91beae8488758f35219da849 2025-07-17T07:01:51.9033552Z * [new tag] trunk/596b418391aa5d520e69310250c1f86c6c0a1107 -> trunk/596b418391aa5d520e69310250c1f86c6c0a1107 2025-07-17T07:01:51.9034463Z * [new tag] trunk/59c3cac4547aafd2f718b7c64053098cc5886878 -> trunk/59c3cac4547aafd2f718b7c64053098cc5886878 2025-07-17T07:01:51.9035678Z * [new tag] trunk/59eb61b2d1e4b64debbefa036acd0d8c7d55f0a3 -> trunk/59eb61b2d1e4b64debbefa036acd0d8c7d55f0a3 2025-07-17T07:01:51.9036535Z * [new tag] trunk/59f9b25f3cfc635053843372ea29ff4bf754da3f -> trunk/59f9b25f3cfc635053843372ea29ff4bf754da3f 2025-07-17T07:01:51.9037698Z * [new tag] trunk/5a2db5152d23f76dbb45d20008d9af68e761e8d1 -> trunk/5a2db5152d23f76dbb45d20008d9af68e761e8d1 2025-07-17T07:01:51.9038550Z * [new tag] trunk/5a533f74a160f6a570c2b9770ffdd89874ba59e2 -> trunk/5a533f74a160f6a570c2b9770ffdd89874ba59e2 2025-07-17T07:01:51.9039416Z * [new tag] trunk/5a54db14e3843cfa87fd8d27487dbf2f2dfb6c47 -> trunk/5a54db14e3843cfa87fd8d27487dbf2f2dfb6c47 2025-07-17T07:01:51.9040357Z * [new tag] trunk/5a5a05a6a3be376130848e235df73b752eef0230 -> trunk/5a5a05a6a3be376130848e235df73b752eef0230 2025-07-17T07:01:51.9041272Z * [new tag] trunk/5ab257c74c8e3ffe99380790de2134ba8013555e -> trunk/5ab257c74c8e3ffe99380790de2134ba8013555e 2025-07-17T07:01:51.9042215Z * [new tag] trunk/5ab6a3fb6fd37c542060c606edd4b95c7e3cae82 -> trunk/5ab6a3fb6fd37c542060c606edd4b95c7e3cae82 2025-07-17T07:01:51.9043125Z * [new tag] trunk/5ad2bee2c8a7defd2580bb138145a49c37146fcc -> trunk/5ad2bee2c8a7defd2580bb138145a49c37146fcc 2025-07-17T07:01:51.9044053Z * [new tag] trunk/5aee022d8b2bc9d31ddaf877315ffb8ad9d62985 -> trunk/5aee022d8b2bc9d31ddaf877315ffb8ad9d62985 2025-07-17T07:01:51.9044818Z * [new tag] trunk/5b10b0a96f9abf8c2751db324f0773aa433ec783 -> trunk/5b10b0a96f9abf8c2751db324f0773aa433ec783 2025-07-17T07:01:51.9045802Z * [new tag] trunk/5b4e0255d7ed756c312fb74ffcf17fe62c903bd7 -> trunk/5b4e0255d7ed756c312fb74ffcf17fe62c903bd7 2025-07-17T07:01:51.9046733Z * [new tag] trunk/5b656289064311e8bb08e1148db7d9b41f9d7ac8 -> trunk/5b656289064311e8bb08e1148db7d9b41f9d7ac8 2025-07-17T07:01:51.9049191Z * [new tag] trunk/5b9db4335e61c1c903cb0769282cbea588e49036 -> trunk/5b9db4335e61c1c903cb0769282cbea588e49036 2025-07-17T07:01:51.9049979Z * [new tag] trunk/5bd7804be2186f1756fba75ecc900d3b19016ad5 -> trunk/5bd7804be2186f1756fba75ecc900d3b19016ad5 2025-07-17T07:01:51.9050898Z * [new tag] trunk/5c79a55e7e58c6382c7ce02da1cd07d358239d94 -> trunk/5c79a55e7e58c6382c7ce02da1cd07d358239d94 2025-07-17T07:01:51.9051873Z * [new tag] trunk/5c7e1d39ab81647300d60e7cc22b24acf2641457 -> trunk/5c7e1d39ab81647300d60e7cc22b24acf2641457 2025-07-17T07:01:51.9052845Z * [new tag] trunk/5cc4e856fda4accb2e9291527693a91dc2a18d89 -> trunk/5cc4e856fda4accb2e9291527693a91dc2a18d89 2025-07-17T07:01:51.9054092Z * [new tag] trunk/5cfe4377d6f3b5845ba1f67122e5fbda8d642ecd -> trunk/5cfe4377d6f3b5845ba1f67122e5fbda8d642ecd 2025-07-17T07:01:51.9054949Z * [new tag] trunk/5d5a5b3501dfb0759ed36d0a88b65cdcd87c1e27 -> trunk/5d5a5b3501dfb0759ed36d0a88b65cdcd87c1e27 2025-07-17T07:01:51.9055999Z * [new tag] trunk/5d8d126249f83a9581f6b086f0891753bbb7175e -> trunk/5d8d126249f83a9581f6b086f0891753bbb7175e 2025-07-17T07:01:51.9057649Z * [new tag] trunk/5db9a2b54ae69917753be5b2eef0e15ad3cfd19b -> trunk/5db9a2b54ae69917753be5b2eef0e15ad3cfd19b 2025-07-17T07:01:51.9058516Z * [new tag] trunk/5dc75f72d4ede12067ddca459e02c8ee8a95e02c -> trunk/5dc75f72d4ede12067ddca459e02c8ee8a95e02c 2025-07-17T07:01:51.9059404Z * [new tag] trunk/5dd07c70e53a86b73f49711b8186d86dc4f1b32a -> trunk/5dd07c70e53a86b73f49711b8186d86dc4f1b32a 2025-07-17T07:01:51.9060285Z * [new tag] trunk/5dd9652389ed7959a842323e4ce063f553710e47 -> trunk/5dd9652389ed7959a842323e4ce063f553710e47 2025-07-17T07:01:51.9061213Z * [new tag] trunk/5df3bf13ec4e436abefe9d3822230727c04c2ab7 -> trunk/5df3bf13ec4e436abefe9d3822230727c04c2ab7 2025-07-17T07:01:51.9062323Z * [new tag] trunk/5dfd8a9c7a464bb42e81b8594eefd2fa865e5423 -> trunk/5dfd8a9c7a464bb42e81b8594eefd2fa865e5423 2025-07-17T07:01:51.9063170Z * [new tag] trunk/5dfe1787b5ed372fe1e9f0d73fab4e53a61bd17a -> trunk/5dfe1787b5ed372fe1e9f0d73fab4e53a61bd17a 2025-07-17T07:01:51.9063914Z * [new tag] trunk/5e18bc333144473f1f10bc8a5ba05dba7950fb8a -> trunk/5e18bc333144473f1f10bc8a5ba05dba7950fb8a 2025-07-17T07:01:51.9064906Z * [new tag] trunk/5e636d664ae0ccfdc1f0c5b060eb50fabf56e91b -> trunk/5e636d664ae0ccfdc1f0c5b060eb50fabf56e91b 2025-07-17T07:01:51.9065878Z * [new tag] trunk/5e93abe3c0106cf6fc694a38433909f20ee6a92c -> trunk/5e93abe3c0106cf6fc694a38433909f20ee6a92c 2025-07-17T07:01:51.9066769Z * [new tag] trunk/5eb5c3700bf51015e6a46f9f20c0e0e20cf55ab6 -> trunk/5eb5c3700bf51015e6a46f9f20c0e0e20cf55ab6 2025-07-17T07:01:51.9067646Z * [new tag] trunk/5f1225ef487a4c3954499a8ae5b5a55751e3ef2b -> trunk/5f1225ef487a4c3954499a8ae5b5a55751e3ef2b 2025-07-17T07:01:51.9068461Z * [new tag] trunk/5fb07acbc32874a932cd26087cf752b2f4cc72df -> trunk/5fb07acbc32874a932cd26087cf752b2f4cc72df 2025-07-17T07:01:51.9070148Z * [new tag] trunk/5fbaa041e75af11c7ff892e544707820232bd750 -> trunk/5fbaa041e75af11c7ff892e544707820232bd750 2025-07-17T07:01:51.9070917Z * [new tag] trunk/602044068359a3be08b2030a1bb1efb4dc107767 -> trunk/602044068359a3be08b2030a1bb1efb4dc107767 2025-07-17T07:01:51.9071924Z * [new tag] trunk/603a54a9b33e1aabe1407721d7935b881a160968 -> trunk/603a54a9b33e1aabe1407721d7935b881a160968 2025-07-17T07:01:51.9073007Z * [new tag] trunk/606d73bde495b055984206986fb7dd8918570e89 -> trunk/606d73bde495b055984206986fb7dd8918570e89 2025-07-17T07:01:51.9074375Z * [new tag] trunk/6098209bfffa453ec2cea08b0c9991b3b7e2bfeb -> trunk/6098209bfffa453ec2cea08b0c9991b3b7e2bfeb 2025-07-17T07:01:51.9075166Z * [new tag] trunk/60abb0d3273749cb2a7d583c7c2863bd2819e87e -> trunk/60abb0d3273749cb2a7d583c7c2863bd2819e87e 2025-07-17T07:01:51.9076056Z * [new tag] trunk/60b41de0ca0e5240eccf15c26a2b55b4a34d6f47 -> trunk/60b41de0ca0e5240eccf15c26a2b55b4a34d6f47 2025-07-17T07:01:51.9076958Z * [new tag] trunk/60e66d11ab3d62273cafa7f6b104db7e20f75dfd -> trunk/60e66d11ab3d62273cafa7f6b104db7e20f75dfd 2025-07-17T07:01:51.9077847Z * [new tag] trunk/61712e6f2ba58cce354a742d918934ec7293ee43 -> trunk/61712e6f2ba58cce354a742d918934ec7293ee43 2025-07-17T07:01:51.9078911Z * [new tag] trunk/617e3f69f8479197be57a28cc31e001c3feec407 -> trunk/617e3f69f8479197be57a28cc31e001c3feec407 2025-07-17T07:01:51.9079767Z * [new tag] trunk/61a7b09ef39907b6c4b47445f965c4c77cc6c2ed -> trunk/61a7b09ef39907b6c4b47445f965c4c77cc6c2ed 2025-07-17T07:01:51.9080667Z * [new tag] trunk/61b271e0f3f93209325dea9dccb1e97e7bc16b41 -> trunk/61b271e0f3f93209325dea9dccb1e97e7bc16b41 2025-07-17T07:01:51.9081518Z * [new tag] trunk/61e13782ddddf9e957c984ef11d7bb7643b871e7 -> trunk/61e13782ddddf9e957c984ef11d7bb7643b871e7 2025-07-17T07:01:51.9082655Z * [new tag] trunk/61eaaa21a42398941ea3fb01585a0926e9544831 -> trunk/61eaaa21a42398941ea3fb01585a0926e9544831 2025-07-17T07:01:51.9083441Z * [new tag] trunk/61f6aa36b9f9a168bd3c710c449f0a78c0301972 -> trunk/61f6aa36b9f9a168bd3c710c449f0a78c0301972 2025-07-17T07:01:51.9084344Z * [new tag] trunk/6200584193b770411b7f91880bbff6f746acfcb0 -> trunk/6200584193b770411b7f91880bbff6f746acfcb0 2025-07-17T07:01:51.9085314Z * [new tag] trunk/620415e018cd67e45d5c0a87964aff8751c9bf71 -> trunk/620415e018cd67e45d5c0a87964aff8751c9bf71 2025-07-17T07:01:51.9086249Z * [new tag] trunk/6215e90b7b9af8275c5dbfaa5fd58d7ec08b6764 -> trunk/6215e90b7b9af8275c5dbfaa5fd58d7ec08b6764 2025-07-17T07:01:51.9087163Z * [new tag] trunk/62272d5b24e7f505a02175de3c56ecc287557d2a -> trunk/62272d5b24e7f505a02175de3c56ecc287557d2a 2025-07-17T07:01:51.9088064Z * [new tag] trunk/627ba411366bcc15019c49756d3f22fd3914bd50 -> trunk/627ba411366bcc15019c49756d3f22fd3914bd50 2025-07-17T07:01:51.9088932Z * [new tag] trunk/62fa3f5aebfe763f5de27e687e4c81df7c7080e3 -> trunk/62fa3f5aebfe763f5de27e687e4c81df7c7080e3 2025-07-17T07:01:51.9089842Z * [new tag] trunk/6303cc41b71c02af9c855f3cc85b1146a97fc7a8 -> trunk/6303cc41b71c02af9c855f3cc85b1146a97fc7a8 2025-07-17T07:01:51.9090860Z * [new tag] trunk/63360e64da814de8ce271f1e4b6e2380a03b585e -> trunk/63360e64da814de8ce271f1e4b6e2380a03b585e 2025-07-17T07:01:51.9091687Z * [new tag] trunk/63a96eaeb84f5af6e83c7360c09f5540a44d19ca -> trunk/63a96eaeb84f5af6e83c7360c09f5540a44d19ca 2025-07-17T07:01:51.9092563Z * [new tag] trunk/63e87d6d05dc05ed7cc642c0cf8fd1cd2e3b93a1 -> trunk/63e87d6d05dc05ed7cc642c0cf8fd1cd2e3b93a1 2025-07-17T07:01:51.9093437Z * [new tag] trunk/6401d1d53d13cb2d564d48a30a8cf4f952ff773d -> trunk/6401d1d53d13cb2d564d48a30a8cf4f952ff773d 2025-07-17T07:01:51.9094557Z * [new tag] trunk/640703d95f210876f20f8f16c868442dad17b477 -> trunk/640703d95f210876f20f8f16c868442dad17b477 2025-07-17T07:01:51.9095405Z * [new tag] trunk/640f5a70905b01405f1ce397d25308d3e6a1d421 -> trunk/640f5a70905b01405f1ce397d25308d3e6a1d421 2025-07-17T07:01:51.9096374Z * [new tag] trunk/6442ae9256a6936bc8efe31fc5326d5c1c1f660b -> trunk/6442ae9256a6936bc8efe31fc5326d5c1c1f660b 2025-07-17T07:01:51.9097252Z * [new tag] trunk/64436c38c956b252d8c9f6f68e94fb46c34efdac -> trunk/64436c38c956b252d8c9f6f68e94fb46c34efdac 2025-07-17T07:01:51.9098036Z * [new tag] trunk/644cc58dfffe1b5bd15688495551b49462c163f6 -> trunk/644cc58dfffe1b5bd15688495551b49462c163f6 2025-07-17T07:01:51.9098921Z * [new tag] trunk/64bb6317a57d14c37339d86cded7c6b860c7d638 -> trunk/64bb6317a57d14c37339d86cded7c6b860c7d638 2025-07-17T07:01:51.9099811Z * [new tag] trunk/64f2ec77f869a7d495694519fb482e9ecaaa6da1 -> trunk/64f2ec77f869a7d495694519fb482e9ecaaa6da1 2025-07-17T07:01:51.9100960Z * [new tag] trunk/651b4a68f2a60d55d266e40776709247ef347d68 -> trunk/651b4a68f2a60d55d266e40776709247ef347d68 2025-07-17T07:01:51.9101759Z * [new tag] trunk/653c52fe52254e6783d75a73c3a8abbfd9cd2b3c -> trunk/653c52fe52254e6783d75a73c3a8abbfd9cd2b3c 2025-07-17T07:01:51.9102638Z * [new tag] trunk/655b3b14ffba4ae73e26a63b4289329e8d160a6f -> trunk/655b3b14ffba4ae73e26a63b4289329e8d160a6f 2025-07-17T07:01:51.9103384Z * [new tag] trunk/65b9c13cce43a7a8666ca1ef12b554a5dcfd2492 -> trunk/65b9c13cce43a7a8666ca1ef12b554a5dcfd2492 2025-07-17T07:01:51.9104302Z * [new tag] trunk/65fcca4f8c97de82d35d51ad9b790d10433e9b91 -> trunk/65fcca4f8c97de82d35d51ad9b790d10433e9b91 2025-07-17T07:01:51.9105261Z * [new tag] trunk/660695f11dd73efbe2694be0ed5826e4624f22e4 -> trunk/660695f11dd73efbe2694be0ed5826e4624f22e4 2025-07-17T07:01:51.9106055Z * [new tag] trunk/660dbea909b98f19dc0239a7ae565f8a52cfe846 -> trunk/660dbea909b98f19dc0239a7ae565f8a52cfe846 2025-07-17T07:01:51.9106992Z * [new tag] trunk/6629eaf0c658b523eed47a98c0a3e6b44fd89c6b -> trunk/6629eaf0c658b523eed47a98c0a3e6b44fd89c6b 2025-07-17T07:01:51.9107871Z * [new tag] trunk/662c1cfed2bf3262709f1150fcb11e96bc29e142 -> trunk/662c1cfed2bf3262709f1150fcb11e96bc29e142 2025-07-17T07:01:51.9108767Z * [new tag] trunk/670dab6c630552b32189911f22896ec453e55ab7 -> trunk/670dab6c630552b32189911f22896ec453e55ab7 2025-07-17T07:01:51.9109625Z * [new tag] trunk/671a9d175b7e15b4947c97f50b931dba1cc35a63 -> trunk/671a9d175b7e15b4947c97f50b931dba1cc35a63 2025-07-17T07:01:51.9110523Z * [new tag] trunk/672ac2ec86d8ffe080399363df96597437e51115 -> trunk/672ac2ec86d8ffe080399363df96597437e51115 2025-07-17T07:01:51.9111412Z * [new tag] trunk/67ee0c6725e8dd2d0372ff902775b7040631aaf7 -> trunk/67ee0c6725e8dd2d0372ff902775b7040631aaf7 2025-07-17T07:01:51.9112311Z * [new tag] trunk/67f8270516ef877aec85801e4a8d0b533687e938 -> trunk/67f8270516ef877aec85801e4a8d0b533687e938 2025-07-17T07:01:51.9113083Z * [new tag] trunk/6835ba1b3409d9764c6e704cdc0311d53ea0841a -> trunk/6835ba1b3409d9764c6e704cdc0311d53ea0841a 2025-07-17T07:01:51.9114095Z * [new tag] trunk/68996dc18306ac0320ac5e6979f1a7a7a226b3cc -> trunk/68996dc18306ac0320ac5e6979f1a7a7a226b3cc 2025-07-17T07:01:51.9114972Z * [new tag] trunk/68f36683f0c0dfe7befeba2b65dee30faf88f7cc -> trunk/68f36683f0c0dfe7befeba2b65dee30faf88f7cc 2025-07-17T07:01:51.9115858Z * [new tag] trunk/6918758f554790e1155fe9f3ee4120692347680a -> trunk/6918758f554790e1155fe9f3ee4120692347680a 2025-07-17T07:01:51.9116840Z * [new tag] trunk/693116f765ede6632d3dd8ef3ffe01cc40b960b9 -> trunk/693116f765ede6632d3dd8ef3ffe01cc40b960b9 2025-07-17T07:01:51.9117686Z * [new tag] trunk/694028f50269c9f34721db813e9ee1a8221c99b2 -> trunk/694028f50269c9f34721db813e9ee1a8221c99b2 2025-07-17T07:01:51.9118837Z * [new tag] trunk/6959b5febe090375e261e678902681533d2e2528 -> trunk/6959b5febe090375e261e678902681533d2e2528 2025-07-17T07:01:51.9119655Z * [new tag] trunk/69f2e09cc241176d6128a8f92f9490422c5b81b3 -> trunk/69f2e09cc241176d6128a8f92f9490422c5b81b3 2025-07-17T07:01:51.9120603Z * [new tag] trunk/6a3d00aa3b1e54feeb040891bfe2c59c36b9443c -> trunk/6a3d00aa3b1e54feeb040891bfe2c59c36b9443c 2025-07-17T07:01:51.9121503Z * [new tag] trunk/6abe450a6fda580714a276ed2dd695ab99074b93 -> trunk/6abe450a6fda580714a276ed2dd695ab99074b93 2025-07-17T07:01:51.9122439Z * [new tag] trunk/6b05842e4785f52ab89a8fa2a88f9e6f2316cbbd -> trunk/6b05842e4785f52ab89a8fa2a88f9e6f2316cbbd 2025-07-17T07:01:51.9123353Z * [new tag] trunk/6b1211df294e57d59c1e1717b1fedc671ec5bd5a -> trunk/6b1211df294e57d59c1e1717b1fedc671ec5bd5a 2025-07-17T07:01:51.9124215Z * [new tag] trunk/6b2bef10afae4acb18f230a496392b673c954ce7 -> trunk/6b2bef10afae4acb18f230a496392b673c954ce7 2025-07-17T07:01:51.9125095Z * [new tag] trunk/6b3eef6d316ce33ef0f0416c2f3f6a827ac4fe30 -> trunk/6b3eef6d316ce33ef0f0416c2f3f6a827ac4fe30 2025-07-17T07:01:51.9125958Z * [new tag] trunk/6b45af38a5991134f45d9750b79c767688ba3761 -> trunk/6b45af38a5991134f45d9750b79c767688ba3761 2025-07-17T07:01:51.9126913Z * [new tag] trunk/6b7767fc8d759017b5fc2a7002051cf5c05615a9 -> trunk/6b7767fc8d759017b5fc2a7002051cf5c05615a9 2025-07-17T07:01:51.9127819Z * [new tag] trunk/6b84cb29f97957032c5aa91b4e2a6bfa166db04d -> trunk/6b84cb29f97957032c5aa91b4e2a6bfa166db04d 2025-07-17T07:01:51.9128742Z * [new tag] trunk/6bc263809de610e2fcdf594c122a0f99c6c7d96f -> trunk/6bc263809de610e2fcdf594c122a0f99c6c7d96f 2025-07-17T07:01:51.9129609Z * [new tag] trunk/6c008e2fb5a94342c5c4047d034c61bb067fa2b7 -> trunk/6c008e2fb5a94342c5c4047d034c61bb067fa2b7 2025-07-17T07:01:51.9130532Z * [new tag] trunk/6c05f2fca049344fbbb55eed7a6c866f34c8a477 -> trunk/6c05f2fca049344fbbb55eed7a6c866f34c8a477 2025-07-17T07:01:51.9131415Z * [new tag] trunk/6c0b42fd2f2c070f4c7ef9a3914698726f61ef3a -> trunk/6c0b42fd2f2c070f4c7ef9a3914698726f61ef3a 2025-07-17T07:01:51.9132831Z * [new tag] trunk/6c24c6633a00608d8c20805a77fae55f6b8fe9a6 -> trunk/6c24c6633a00608d8c20805a77fae55f6b8fe9a6 2025-07-17T07:01:51.9133833Z * [new tag] trunk/6c42afe1964496e1b02be1442470f254e35eb199 -> trunk/6c42afe1964496e1b02be1442470f254e35eb199 2025-07-17T07:01:51.9134724Z * [new tag] trunk/6c5227ba00a2904365af566c24b4681cd01a041c -> trunk/6c5227ba00a2904365af566c24b4681cd01a041c 2025-07-17T07:01:51.9136198Z * [new tag] trunk/6c795306378c47341d58109da03371bba2bec46e -> trunk/6c795306378c47341d58109da03371bba2bec46e 2025-07-17T07:01:51.9137162Z * [new tag] trunk/6cc490d40b16b3be7ecc8943fe1bc594246caab3 -> trunk/6cc490d40b16b3be7ecc8943fe1bc594246caab3 2025-07-17T07:01:51.9138060Z * [new tag] trunk/6d02321472ee0761092166dd273eb3ec386cf0c0 -> trunk/6d02321472ee0761092166dd273eb3ec386cf0c0 2025-07-17T07:01:51.9138977Z * [new tag] trunk/6d2155db498375317e2101068a220439dba80939 -> trunk/6d2155db498375317e2101068a220439dba80939 2025-07-17T07:01:51.9139821Z * [new tag] trunk/6d3a4356f61b28a14abd95f641e2615deb186365 -> trunk/6d3a4356f61b28a14abd95f641e2615deb186365 2025-07-17T07:01:51.9140580Z * [new tag] trunk/6d5c789ad5f5d162426a5a10c84813ec923dd8ee -> trunk/6d5c789ad5f5d162426a5a10c84813ec923dd8ee 2025-07-17T07:01:51.9141647Z * [new tag] trunk/6dc2b22269f82a1feec06b5e3e79b184704cc0ba -> trunk/6dc2b22269f82a1feec06b5e3e79b184704cc0ba 2025-07-17T07:01:51.9142501Z * [new tag] trunk/6de41ce0f899604c3f8b33e1f8d37eb89b3a963e -> trunk/6de41ce0f899604c3f8b33e1f8d37eb89b3a963e 2025-07-17T07:01:51.9143377Z * [new tag] trunk/6defd5084e3fd330561095252c30ea50efa8e0ca -> trunk/6defd5084e3fd330561095252c30ea50efa8e0ca 2025-07-17T07:01:51.9144493Z * [new tag] trunk/6dfada220e4f46c28df3a6bdf57260ca0717c4bd -> trunk/6dfada220e4f46c28df3a6bdf57260ca0717c4bd 2025-07-17T07:01:51.9145389Z * [new tag] trunk/6e17315cd33892919dc6deb6a9ecb568dda6195e -> trunk/6e17315cd33892919dc6deb6a9ecb568dda6195e 2025-07-17T07:01:51.9146512Z * [new tag] trunk/6e185c53124e1b5a0fe391959060c1249178bcb6 -> trunk/6e185c53124e1b5a0fe391959060c1249178bcb6 2025-07-17T07:01:51.9147335Z * [new tag] trunk/6e2992a9984f2c3f6469564008c7e45869b84678 -> trunk/6e2992a9984f2c3f6469564008c7e45869b84678 2025-07-17T07:01:51.9148264Z * [new tag] trunk/6ea91f067256447cda6fae533f806c1f8baafbe2 -> trunk/6ea91f067256447cda6fae533f806c1f8baafbe2 2025-07-17T07:01:51.9149177Z * [new tag] trunk/6eb6f198e1a28e904ce00a132c43aa2536419664 -> trunk/6eb6f198e1a28e904ce00a132c43aa2536419664 2025-07-17T07:01:51.9150044Z * [new tag] trunk/6ebe9a4f47e9cd1c9ccd467bcdfdea9445fd98d6 -> trunk/6ebe9a4f47e9cd1c9ccd467bcdfdea9445fd98d6 2025-07-17T07:01:51.9150978Z * [new tag] trunk/6ed85bfe6a1704a87a492a3381414186eb439f5e -> trunk/6ed85bfe6a1704a87a492a3381414186eb439f5e 2025-07-17T07:01:51.9151877Z * [new tag] trunk/6ef70edd9a9acee76a607bdd22bac400c8e29434 -> trunk/6ef70edd9a9acee76a607bdd22bac400c8e29434 2025-07-17T07:01:51.9152800Z * [new tag] trunk/6f05d58f2b0bc4779fbed9db5b0f80af1c686dcd -> trunk/6f05d58f2b0bc4779fbed9db5b0f80af1c686dcd 2025-07-17T07:01:51.9153702Z * [new tag] trunk/6f23f53599629a47d6e097b2a027048658a142d4 -> trunk/6f23f53599629a47d6e097b2a027048658a142d4 2025-07-17T07:01:51.9154644Z * [new tag] trunk/6f60cfe9b1445271f8b83f58b4c4f12578d837d3 -> trunk/6f60cfe9b1445271f8b83f58b4c4f12578d837d3 2025-07-17T07:01:51.9155538Z * [new tag] trunk/6fb62931593fc10252e4994cd1a0595ebf8e990f -> trunk/6fb62931593fc10252e4994cd1a0595ebf8e990f 2025-07-17T07:01:51.9156653Z * [new tag] trunk/6fe7456aa1a2d025d1d06e15ba3896e6adba94b8 -> trunk/6fe7456aa1a2d025d1d06e15ba3896e6adba94b8 2025-07-17T07:01:51.9157540Z * [new tag] trunk/6ffa03ef9e18299f41da076f421abbd32e51e175 -> trunk/6ffa03ef9e18299f41da076f421abbd32e51e175 2025-07-17T07:01:51.9158437Z * [new tag] trunk/702a304b0785d2dfe3d9bd4eed535c38e969818d -> trunk/702a304b0785d2dfe3d9bd4eed535c38e969818d 2025-07-17T07:01:51.9159319Z * [new tag] trunk/706bc41c4c3caa2654a926382c2ef0ef4d0bc55e -> trunk/706bc41c4c3caa2654a926382c2ef0ef4d0bc55e 2025-07-17T07:01:51.9160135Z * [new tag] trunk/706e236b080cadd5b8a016e4bf972cdb0886d57b -> trunk/706e236b080cadd5b8a016e4bf972cdb0886d57b 2025-07-17T07:01:51.9161069Z * [new tag] trunk/7070ab318061d44f195a83b6dc11ef9299603dfe -> trunk/7070ab318061d44f195a83b6dc11ef9299603dfe 2025-07-17T07:01:51.9162012Z * [new tag] trunk/7081b8233a64c350c64e9f00c9b9d00e52020241 -> trunk/7081b8233a64c350c64e9f00c9b9d00e52020241 2025-07-17T07:01:51.9163002Z * [new tag] trunk/70b68caf58815419924ddeda231cbf6535181c53 -> trunk/70b68caf58815419924ddeda231cbf6535181c53 2025-07-17T07:01:51.9164062Z * [new tag] trunk/70bb34929aabc9c11d9ec9bbb53a4f9fc32e5e19 -> trunk/70bb34929aabc9c11d9ec9bbb53a4f9fc32e5e19 2025-07-17T07:01:51.9164898Z * [new tag] trunk/710b92cf3b577036d551708b351b4043817233f5 -> trunk/710b92cf3b577036d551708b351b4043817233f5 2025-07-17T07:01:51.9165820Z * [new tag] trunk/717a099d427d97a62c26fee58f9da9a0893d4233 -> trunk/717a099d427d97a62c26fee58f9da9a0893d4233 2025-07-17T07:01:51.9166757Z * [new tag] trunk/71a650ad565fe75ca752acbc8b40fb9ce677f40f -> trunk/71a650ad565fe75ca752acbc8b40fb9ce677f40f 2025-07-17T07:01:51.9167593Z * [new tag] trunk/720c2c46b181ad446bf970d70a27fd0ce149114d -> trunk/720c2c46b181ad446bf970d70a27fd0ce149114d 2025-07-17T07:01:51.9168483Z * [new tag] trunk/721d2580dbf9a4922adc1c7d1cc8237126d3cdd6 -> trunk/721d2580dbf9a4922adc1c7d1cc8237126d3cdd6 2025-07-17T07:01:51.9169397Z * [new tag] trunk/72453a66769294e93a183dfb5bdefa23de873d89 -> trunk/72453a66769294e93a183dfb5bdefa23de873d89 2025-07-17T07:01:51.9170307Z * [new tag] trunk/725c3272848c408d0fa2cba4de76affe90f793b5 -> trunk/725c3272848c408d0fa2cba4de76affe90f793b5 2025-07-17T07:01:51.9171212Z * [new tag] trunk/7275f280454f790414b24147a2ba7f94d0eabcf6 -> trunk/7275f280454f790414b24147a2ba7f94d0eabcf6 2025-07-17T07:01:51.9172210Z * [new tag] trunk/728cf6721e2996490922d0eacb23081953e45fc7 -> trunk/728cf6721e2996490922d0eacb23081953e45fc7 2025-07-17T07:01:51.9173196Z * [new tag] trunk/72c8751b61e0b2c7b88003a41ed737a65768063c -> trunk/72c8751b61e0b2c7b88003a41ed737a65768063c 2025-07-17T07:01:51.9174288Z * [new tag] trunk/731351bb4ac572fb47d46c70b7425c209c81570a -> trunk/731351bb4ac572fb47d46c70b7425c209c81570a 2025-07-17T07:01:51.9175120Z * [new tag] trunk/73220d52fd67b5f4f5b15e0e0433e09733c93f31 -> trunk/73220d52fd67b5f4f5b15e0e0433e09733c93f31 2025-07-17T07:01:51.9176280Z * [new tag] trunk/73772919d2db560c9cc8ed617c94c77450542a94 -> trunk/73772919d2db560c9cc8ed617c94c77450542a94 2025-07-17T07:01:51.9176944Z * [new tag] trunk/7381c777245a836f88e9622005c6ea16009a3a0d -> trunk/7381c777245a836f88e9622005c6ea16009a3a0d 2025-07-17T07:01:51.9177859Z * [new tag] trunk/7392470da4386e654f303eb526dfba7e7777b06b -> trunk/7392470da4386e654f303eb526dfba7e7777b06b 2025-07-17T07:01:51.9178791Z * [new tag] trunk/7444debaca7de16b2cf71d0cd82dc8998c3a73c4 -> trunk/7444debaca7de16b2cf71d0cd82dc8998c3a73c4 2025-07-17T07:01:51.9179746Z * [new tag] trunk/7485ef078f182af981b5f17d06602de68816492c -> trunk/7485ef078f182af981b5f17d06602de68816492c 2025-07-17T07:01:51.9180836Z * [new tag] trunk/749757ac1b66485459e751b0e58b5a7c64d5f561 -> trunk/749757ac1b66485459e751b0e58b5a7c64d5f561 2025-07-17T07:01:51.9181648Z * [new tag] trunk/74ebd8d14e0d612ed35557ab254a90facf73b029 -> trunk/74ebd8d14e0d612ed35557ab254a90facf73b029 2025-07-17T07:01:51.9182576Z * [new tag] trunk/7521cd91118c1c1a55b8f7146d7e22bf60fe06bf -> trunk/7521cd91118c1c1a55b8f7146d7e22bf60fe06bf 2025-07-17T07:01:51.9183506Z * [new tag] trunk/752f202ef3445d2e28e2abeb6feed56eee9da5a9 -> trunk/752f202ef3445d2e28e2abeb6feed56eee9da5a9 2025-07-17T07:01:51.9184428Z * [new tag] trunk/7531bd64911774fc2656f290601834d5d02d3925 -> trunk/7531bd64911774fc2656f290601834d5d02d3925 2025-07-17T07:01:51.9185304Z * [new tag] trunk/754699610b0abec2fe3f5a73269b1dd09a330445 -> trunk/754699610b0abec2fe3f5a73269b1dd09a330445 2025-07-17T07:01:51.9186250Z * [new tag] trunk/754c04aa062d8f3c0449aec4bbcaab00bfca4bf2 -> trunk/754c04aa062d8f3c0449aec4bbcaab00bfca4bf2 2025-07-17T07:01:51.9187119Z * [new tag] trunk/75824035d392595de3bd0b8588e63c2fc6d09139 -> trunk/75824035d392595de3bd0b8588e63c2fc6d09139 2025-07-17T07:01:51.9187904Z * [new tag] trunk/7597988f1b5a41c0b91d379e0ce51111fd7cc95a -> trunk/7597988f1b5a41c0b91d379e0ce51111fd7cc95a 2025-07-17T07:01:51.9188984Z * [new tag] trunk/7599bebead35dd21bad3d3e85f62f400ad2c3139 -> trunk/7599bebead35dd21bad3d3e85f62f400ad2c3139 2025-07-17T07:01:51.9189811Z * [new tag] trunk/75a7d9e86842b57f9001a0fa6fd716927614dbf9 -> trunk/75a7d9e86842b57f9001a0fa6fd716927614dbf9 2025-07-17T07:01:51.9190729Z * [new tag] trunk/75f258dd1feeb90b8084849155e049fb57ba3521 -> trunk/75f258dd1feeb90b8084849155e049fb57ba3521 2025-07-17T07:01:51.9191618Z * [new tag] trunk/75f3e5a88df60caef27fd9c9df3fd51161378fcc -> trunk/75f3e5a88df60caef27fd9c9df3fd51161378fcc 2025-07-17T07:01:51.9193003Z * [new tag] trunk/75f489d37f048ec8a9106163ca16201156f05499 -> trunk/75f489d37f048ec8a9106163ca16201156f05499 2025-07-17T07:01:51.9193835Z * [new tag] trunk/764c02b78b935ba61c46277cb7409e6419460058 -> trunk/764c02b78b935ba61c46277cb7409e6419460058 2025-07-17T07:01:51.9194744Z * [new tag] trunk/76644c9ff58285c95764ffd27d629df219aab140 -> trunk/76644c9ff58285c95764ffd27d629df219aab140 2025-07-17T07:01:51.9195666Z * [new tag] trunk/769d754ab2469813a3b790ec58c25c466099dd3d -> trunk/769d754ab2469813a3b790ec58c25c466099dd3d 2025-07-17T07:01:51.9196999Z * [new tag] trunk/76ca23c41c7edcf7b4c60ff6162eedcdf06ef359 -> trunk/76ca23c41c7edcf7b4c60ff6162eedcdf06ef359 2025-07-17T07:01:51.9197762Z * [new tag] trunk/76d07e919f66c24a55746bbc060c14b3df02ffa8 -> trunk/76d07e919f66c24a55746bbc060c14b3df02ffa8 2025-07-17T07:01:51.9198678Z * [new tag] trunk/76fe88fa56c8aac7377127fb5553de94e16e9070 -> trunk/76fe88fa56c8aac7377127fb5553de94e16e9070 2025-07-17T07:01:51.9199580Z * [new tag] trunk/770285522805745959221b902f23776a13d56df8 -> trunk/770285522805745959221b902f23776a13d56df8 2025-07-17T07:01:51.9200497Z * [new tag] trunk/7709ff55123dbdcaaa44004738fb767324f05dbd -> trunk/7709ff55123dbdcaaa44004738fb767324f05dbd 2025-07-17T07:01:51.9201423Z * [new tag] trunk/771be857043bf794cd219a9b925e308e31f12314 -> trunk/771be857043bf794cd219a9b925e308e31f12314 2025-07-17T07:01:51.9203214Z * [new tag] trunk/772d5904152abc9702bf49037e46ab6203b83f55 -> trunk/772d5904152abc9702bf49037e46ab6203b83f55 2025-07-17T07:01:51.9204055Z * [new tag] trunk/77518d1a13cc105637814bc157387478384dc897 -> trunk/77518d1a13cc105637814bc157387478384dc897 2025-07-17T07:01:51.9205079Z * [new tag] trunk/77676753ecabf6a6645bdd3abfe01939e5751e76 -> trunk/77676753ecabf6a6645bdd3abfe01939e5751e76 2025-07-17T07:01:51.9206237Z * [new tag] trunk/777eca9f16aeecd7c362a235cf25e6b8e6eda57f -> trunk/777eca9f16aeecd7c362a235cf25e6b8e6eda57f 2025-07-17T07:01:51.9207073Z * [new tag] trunk/77ac3a096532ee08211dae38c2a9336c970d1785 -> trunk/77ac3a096532ee08211dae38c2a9336c970d1785 2025-07-17T07:01:51.9207992Z * [new tag] trunk/77f884c2ec62df9df930ae86e9b8437364900346 -> trunk/77f884c2ec62df9df930ae86e9b8437364900346 2025-07-17T07:01:51.9208869Z * [new tag] trunk/783a4c1f5002bf1749833821c450360a417d40c7 -> trunk/783a4c1f5002bf1749833821c450360a417d40c7 2025-07-17T07:01:51.9209697Z * [new tag] trunk/78684e27ace1ebd51ccc93d4b6b8a6a3ec3b4c14 -> trunk/78684e27ace1ebd51ccc93d4b6b8a6a3ec3b4c14 2025-07-17T07:01:51.9210657Z * [new tag] trunk/78ee2ee90eed957aec3dc80423b108b16938a8ae -> trunk/78ee2ee90eed957aec3dc80423b108b16938a8ae 2025-07-17T07:01:51.9211539Z * [new tag] trunk/7918978653ff4f21c284b34809acf88784deb0de -> trunk/7918978653ff4f21c284b34809acf88784deb0de 2025-07-17T07:01:51.9212455Z * [new tag] trunk/794b95d54bb2c49d704169ddb777acfd8da8cfad -> trunk/794b95d54bb2c49d704169ddb777acfd8da8cfad 2025-07-17T07:01:51.9213331Z * [new tag] trunk/794ef6c9b8f173a0cbab5288efb2d68aef9c90b9 -> trunk/794ef6c9b8f173a0cbab5288efb2d68aef9c90b9 2025-07-17T07:01:51.9214188Z * [new tag] trunk/795a6a0affd349adfb4e3df298b604b74f27b44e -> trunk/795a6a0affd349adfb4e3df298b604b74f27b44e 2025-07-17T07:01:51.9214905Z * [new tag] trunk/7982b8c703e24a36c0e0a01b8cf22cd272369b28 -> trunk/7982b8c703e24a36c0e0a01b8cf22cd272369b28 2025-07-17T07:01:51.9215933Z * [new tag] trunk/7986c0dba6e1044d90b7f607f9cca15922339bb4 -> trunk/7986c0dba6e1044d90b7f607f9cca15922339bb4 2025-07-17T07:01:51.9216895Z * [new tag] trunk/799443605bffbd0d80fb3f2edbfe5517e0e2b4a3 -> trunk/799443605bffbd0d80fb3f2edbfe5517e0e2b4a3 2025-07-17T07:01:51.9217865Z * [new tag] trunk/7999735d23aeca844d4a7b23de6ac2370767099a -> trunk/7999735d23aeca844d4a7b23de6ac2370767099a 2025-07-17T07:01:51.9218805Z * [new tag] trunk/79ab84e9b8fe561a55931b2108af45993a670276 -> trunk/79ab84e9b8fe561a55931b2108af45993a670276 2025-07-17T07:01:51.9219730Z * [new tag] trunk/79aef141695f2daea4a9aeb0f385726c5794a242 -> trunk/79aef141695f2daea4a9aeb0f385726c5794a242 2025-07-17T07:01:51.9220656Z * [new tag] trunk/79bdafe5b61f6f073dcd276e135076ff3277a951 -> trunk/79bdafe5b61f6f073dcd276e135076ff3277a951 2025-07-17T07:01:51.9221629Z * [new tag] trunk/79d7c754ab8ae0e5c3a614521632d2cfbfa0fdba -> trunk/79d7c754ab8ae0e5c3a614521632d2cfbfa0fdba 2025-07-17T07:01:51.9222548Z * [new tag] trunk/7a03b0d2ca3946b770bc817571fc36bd5c608b38 -> trunk/7a03b0d2ca3946b770bc817571fc36bd5c608b38 2025-07-17T07:01:51.9223506Z * [new tag] trunk/7a08755c5f3630150c50d09e16c0abf9501dea1e -> trunk/7a08755c5f3630150c50d09e16c0abf9501dea1e 2025-07-17T07:01:51.9224779Z * [new tag] trunk/7a41f207943cc6c88160d7a531fc57bdbe149253 -> trunk/7a41f207943cc6c88160d7a531fc57bdbe149253 2025-07-17T07:01:51.9225658Z * [new tag] trunk/7a48cc699006879a6ec185ac5bd4eaad0c0b7e36 -> trunk/7a48cc699006879a6ec185ac5bd4eaad0c0b7e36 2025-07-17T07:01:51.9226552Z * [new tag] trunk/7a92b5119654c07d15f5c0818e6ae804b01e836c -> trunk/7a92b5119654c07d15f5c0818e6ae804b01e836c 2025-07-17T07:01:51.9227447Z * [new tag] trunk/7ae7c1414354ad225c4a36b3228e30a2baf94923 -> trunk/7ae7c1414354ad225c4a36b3228e30a2baf94923 2025-07-17T07:01:51.9228428Z * [new tag] trunk/7afb834f939eccbb3262e646f0922eed070074a7 -> trunk/7afb834f939eccbb3262e646f0922eed070074a7 2025-07-17T07:01:51.9229399Z * [new tag] trunk/7b0118884e0855e4918af53d44c3fbfc185a1ee9 -> trunk/7b0118884e0855e4918af53d44c3fbfc185a1ee9 2025-07-17T07:01:51.9230327Z * [new tag] trunk/7b392bac13a8007d1e0c6c789aa9bc515119faba -> trunk/7b392bac13a8007d1e0c6c789aa9bc515119faba 2025-07-17T07:01:51.9231430Z * [new tag] trunk/7b7cd56f5e2a965ae8a48c69441dd4f58bf68ceb -> trunk/7b7cd56f5e2a965ae8a48c69441dd4f58bf68ceb 2025-07-17T07:01:51.9232306Z * [new tag] trunk/7be862ab8f2b9040b34c58e1b77ce74c2512f062 -> trunk/7be862ab8f2b9040b34c58e1b77ce74c2512f062 2025-07-17T07:01:51.9233259Z * [new tag] trunk/7c1f62782875a4ac70b96aed0e6c9525b6e4eaf3 -> trunk/7c1f62782875a4ac70b96aed0e6c9525b6e4eaf3 2025-07-17T07:01:51.9234107Z * [new tag] trunk/7c51619e7fbd346e2299255c468cc43bd69425d7 -> trunk/7c51619e7fbd346e2299255c468cc43bd69425d7 2025-07-17T07:01:51.9235246Z * [new tag] trunk/7caf6c801ddfaf556a3ca191173b50002c4261f4 -> trunk/7caf6c801ddfaf556a3ca191173b50002c4261f4 2025-07-17T07:01:51.9236147Z * [new tag] trunk/7cda4017ddda554752e89069ae205be5e8388f59 -> trunk/7cda4017ddda554752e89069ae205be5e8388f59 2025-07-17T07:01:51.9237305Z * [new tag] trunk/7cf31b4a426f3791af30159cea420687f347cd7a -> trunk/7cf31b4a426f3791af30159cea420687f347cd7a 2025-07-17T07:01:51.9238194Z * [new tag] trunk/7cf38d2a0521c5ca292a720bce00617be749a0e7 -> trunk/7cf38d2a0521c5ca292a720bce00617be749a0e7 2025-07-17T07:01:51.9239273Z * [new tag] trunk/7cfd054075b0713642fab8dc4313fc1e5d992599 -> trunk/7cfd054075b0713642fab8dc4313fc1e5d992599 2025-07-17T07:01:51.9240201Z * [new tag] trunk/7d1b3f599d6968a3efe70b7401310e3127949f13 -> trunk/7d1b3f599d6968a3efe70b7401310e3127949f13 2025-07-17T07:01:51.9241398Z * [new tag] trunk/7d4228dbfd13d1ac8fac2c78c042dbb8314f042d -> trunk/7d4228dbfd13d1ac8fac2c78c042dbb8314f042d 2025-07-17T07:01:51.9242299Z * [new tag] trunk/7dcc77e422dcf97ce35991a138ab635a5cb88731 -> trunk/7dcc77e422dcf97ce35991a138ab635a5cb88731 2025-07-17T07:01:51.9243166Z * [new tag] trunk/7e433d5f423248914c5e9838d3ea145db7964923 -> trunk/7e433d5f423248914c5e9838d3ea145db7964923 2025-07-17T07:01:51.9244132Z * [new tag] trunk/7e4c097b0752ae79a8b5dd1de21a51aaafba2ef9 -> trunk/7e4c097b0752ae79a8b5dd1de21a51aaafba2ef9 2025-07-17T07:01:51.9245055Z * [new tag] trunk/7e54c02a35b905e758497b856a1953eb009ba836 -> trunk/7e54c02a35b905e758497b856a1953eb009ba836 2025-07-17T07:01:51.9247165Z * [new tag] trunk/7e83d5084588bdb51151f869b53c92c07bc9f544 -> trunk/7e83d5084588bdb51151f869b53c92c07bc9f544 2025-07-17T07:01:51.9248098Z * [new tag] trunk/7f0cddfb55d426b414cf7b4482c6a371618b349c -> trunk/7f0cddfb55d426b414cf7b4482c6a371618b349c 2025-07-17T07:01:51.9248903Z * [new tag] trunk/7f14b42adf70196d82340c59a9981ffcadf0c53c -> trunk/7f14b42adf70196d82340c59a9981ffcadf0c53c 2025-07-17T07:01:51.9249843Z * [new tag] trunk/7f6e7103a3aefd2d96b3cc6702be931a66b44977 -> trunk/7f6e7103a3aefd2d96b3cc6702be931a66b44977 2025-07-17T07:01:51.9250743Z * [new tag] trunk/7f9fc7e67ce9853a1bb4b16c901c708f78c1c5cd -> trunk/7f9fc7e67ce9853a1bb4b16c901c708f78c1c5cd 2025-07-17T07:01:51.9251652Z * [new tag] trunk/7fcad0231cb0ae9ba6a0004098b5ffd82c62858f -> trunk/7fcad0231cb0ae9ba6a0004098b5ffd82c62858f 2025-07-17T07:01:51.9252511Z * [new tag] trunk/805297981ae4e2aa08d133161477a477f5da274f -> trunk/805297981ae4e2aa08d133161477a477f5da274f 2025-07-17T07:01:51.9253485Z * [new tag] trunk/8088958793841191eba1faf98849904ee769bbfd -> trunk/8088958793841191eba1faf98849904ee769bbfd 2025-07-17T07:01:51.9254625Z * [new tag] trunk/80bcaa4195944d0f00f16abcbb702bde4d582d14 -> trunk/80bcaa4195944d0f00f16abcbb702bde4d582d14 2025-07-17T07:01:51.9256128Z * [new tag] trunk/80d89974c173c9d7a2c59fe9dfce2fe1301e2833 -> trunk/80d89974c173c9d7a2c59fe9dfce2fe1301e2833 2025-07-17T07:01:51.9256982Z * [new tag] trunk/8134684d44f3f9d09e775f3adc513bdef72245c9 -> trunk/8134684d44f3f9d09e775f3adc513bdef72245c9 2025-07-17T07:01:51.9257869Z * [new tag] trunk/8142a0286016e63a0e91b5667e1fb1a5e868ffd7 -> trunk/8142a0286016e63a0e91b5667e1fb1a5e868ffd7 2025-07-17T07:01:51.9258790Z * [new tag] trunk/8147c4a9044913e62ba3aab7f1eaaafead4f5930 -> trunk/8147c4a9044913e62ba3aab7f1eaaafead4f5930 2025-07-17T07:01:51.9259678Z * [new tag] trunk/8153340d1073c190ecf221e51abbd983286782a8 -> trunk/8153340d1073c190ecf221e51abbd983286782a8 2025-07-17T07:01:51.9260648Z * [new tag] trunk/815545f2dd6ade563cb1263f8bb7813f355edb2e -> trunk/815545f2dd6ade563cb1263f8bb7813f355edb2e 2025-07-17T07:01:51.9261521Z * [new tag] trunk/81759afed4fa4fa3a03f9ff671abe270679cc006 -> trunk/81759afed4fa4fa3a03f9ff671abe270679cc006 2025-07-17T07:01:51.9262395Z * [new tag] trunk/8186af5a26bc6a628f283c4791c8d68ef6fa1336 -> trunk/8186af5a26bc6a628f283c4791c8d68ef6fa1336 2025-07-17T07:01:51.9263307Z * [new tag] trunk/81b0b308cafae44339b8a123e8b1bec3bf9eb8ad -> trunk/81b0b308cafae44339b8a123e8b1bec3bf9eb8ad 2025-07-17T07:01:51.9264143Z * [new tag] trunk/81bf278537c081279a574157e1359ec7f0b65daf -> trunk/81bf278537c081279a574157e1359ec7f0b65daf 2025-07-17T07:01:51.9265047Z * [new tag] trunk/81c7445eb97f910ed89da9b11140d914651a1675 -> trunk/81c7445eb97f910ed89da9b11140d914651a1675 2025-07-17T07:01:51.9266000Z * [new tag] trunk/82672206b724a767dcb8c2541074449100de701b -> trunk/82672206b724a767dcb8c2541074449100de701b 2025-07-17T07:01:51.9266850Z * [new tag] trunk/826f12b829070e3d5bfd050f001b61aaf78e5447 -> trunk/826f12b829070e3d5bfd050f001b61aaf78e5447 2025-07-17T07:01:51.9267779Z * [new tag] trunk/82765dad16d7cda1ea382497dbf8c1ab2d07bfdd -> trunk/82765dad16d7cda1ea382497dbf8c1ab2d07bfdd 2025-07-17T07:01:51.9268867Z * [new tag] trunk/82a1ee1135b054d371d10081883b848ac7b7419f -> trunk/82a1ee1135b054d371d10081883b848ac7b7419f 2025-07-17T07:01:51.9269690Z * [new tag] trunk/82e6475d920991ef3be5d5637a72bf49313cc604 -> trunk/82e6475d920991ef3be5d5637a72bf49313cc604 2025-07-17T07:01:51.9270524Z * [new tag] trunk/82eefaedd98b63de8a87e34275af781f8eb177e1 -> trunk/82eefaedd98b63de8a87e34275af781f8eb177e1 2025-07-17T07:01:51.9271369Z * [new tag] trunk/82fb904140d258604a185154287b9c6fde047fc5 -> trunk/82fb904140d258604a185154287b9c6fde047fc5 2025-07-17T07:01:51.9272387Z * [new tag] trunk/830a335a7da5fec00395d440ba568749cb4e2e9e -> trunk/830a335a7da5fec00395d440ba568749cb4e2e9e 2025-07-17T07:01:51.9277266Z * [new tag] trunk/831c9010c7d7460fd78bc59b3fa28a65a0339e5d -> trunk/831c9010c7d7460fd78bc59b3fa28a65a0339e5d 2025-07-17T07:01:51.9278130Z * [new tag] trunk/8347268edcf3cc4f638474d860450bb0cd7a9908 -> trunk/8347268edcf3cc4f638474d860450bb0cd7a9908 2025-07-17T07:01:51.9279022Z * [new tag] trunk/836bb1941b593063f002394c1de3ec382c2ce50e -> trunk/836bb1941b593063f002394c1de3ec382c2ce50e 2025-07-17T07:01:51.9279921Z * [new tag] trunk/83700b448874cac8e89356ab06292e4289577269 -> trunk/83700b448874cac8e89356ab06292e4289577269 2025-07-17T07:01:51.9280862Z * [new tag] trunk/8372d0986a495d41f41c5d7684f1428ff847c699 -> trunk/8372d0986a495d41f41c5d7684f1428ff847c699 2025-07-17T07:01:51.9282206Z * [new tag] trunk/838798425731e6c90df4a885e90ec03f18eea10b -> trunk/838798425731e6c90df4a885e90ec03f18eea10b 2025-07-17T07:01:51.9282886Z * [new tag] trunk/83d22256f84232c5440b25a08459c649a32b9a4f -> trunk/83d22256f84232c5440b25a08459c649a32b9a4f 2025-07-17T07:01:51.9283783Z * [new tag] trunk/84085229765698166f07c9220d5544023ab80d47 -> trunk/84085229765698166f07c9220d5544023ab80d47 2025-07-17T07:01:51.9284698Z * [new tag] trunk/8485f1950710d383fd8d0d772fc978933c6fc175 -> trunk/8485f1950710d383fd8d0d772fc978933c6fc175 2025-07-17T07:01:51.9285592Z * [new tag] trunk/84b77ec1280a56bece8408a2e0e179b9eb95e1e2 -> trunk/84b77ec1280a56bece8408a2e0e179b9eb95e1e2 2025-07-17T07:01:51.9286481Z * [new tag] trunk/84c14361c28e1b553ea405ef5020cf0a90468850 -> trunk/84c14361c28e1b553ea405ef5020cf0a90468850 2025-07-17T07:01:51.9287416Z * [new tag] trunk/84c588e5eada9e7921608065edc444a15c22cb1c -> trunk/84c588e5eada9e7921608065edc444a15c22cb1c 2025-07-17T07:01:51.9288323Z * [new tag] trunk/84dec060b79078e68dcb6be1e7f308dad05d00e2 -> trunk/84dec060b79078e68dcb6be1e7f308dad05d00e2 2025-07-17T07:01:51.9289271Z * [new tag] trunk/85111cd165f108ffabb4a90083d59d7a867ebd9f -> trunk/85111cd165f108ffabb4a90083d59d7a867ebd9f 2025-07-17T07:01:51.9290132Z * [new tag] trunk/851a6fa82df251fbc368f0284d941ce78f68e7b1 -> trunk/851a6fa82df251fbc368f0284d941ce78f68e7b1 2025-07-17T07:01:51.9290997Z * [new tag] trunk/85320336799e38411d15c0e159b41248cda01218 -> trunk/85320336799e38411d15c0e159b41248cda01218 2025-07-17T07:01:51.9292194Z * [new tag] trunk/8554c8007ddaa8029e7e01bb1af12f358bf597c2 -> trunk/8554c8007ddaa8029e7e01bb1af12f358bf597c2 2025-07-17T07:01:51.9293073Z * [new tag] trunk/85857181ebca86e9c709e9922a9d9ef41a9c4ef9 -> trunk/85857181ebca86e9c709e9922a9d9ef41a9c4ef9 2025-07-17T07:01:51.9294047Z * [new tag] trunk/85df746892d9b0e87e7a5dfa78ef81a84aec6de0 -> trunk/85df746892d9b0e87e7a5dfa78ef81a84aec6de0 2025-07-17T07:01:51.9294861Z * [new tag] trunk/86251eff4069c468efebdb481dd18fe8d42856f0 -> trunk/86251eff4069c468efebdb481dd18fe8d42856f0 2025-07-17T07:01:51.9295806Z * [new tag] trunk/863327ae496471654344e1e04ccaa713a44a135d -> trunk/863327ae496471654344e1e04ccaa713a44a135d 2025-07-17T07:01:51.9296710Z * [new tag] trunk/86670b39fa3df63a652a9a06b59b73f92d70c392 -> trunk/86670b39fa3df63a652a9a06b59b73f92d70c392 2025-07-17T07:01:51.9298022Z * [new tag] trunk/86996c15dc4294c400c3a552d1a9e8e90aa6c7f6 -> trunk/86996c15dc4294c400c3a552d1a9e8e90aa6c7f6 2025-07-17T07:01:51.9298824Z * [new tag] trunk/86ced144534c2c78460a2d0f1d9ed2ba048f2514 -> trunk/86ced144534c2c78460a2d0f1d9ed2ba048f2514 2025-07-17T07:01:51.9299732Z * [new tag] trunk/86d8af6a6cc648134289de89d393d0dce5b3a5f4 -> trunk/86d8af6a6cc648134289de89d393d0dce5b3a5f4 2025-07-17T07:01:51.9300873Z * [new tag] trunk/86eaf452c330bd871262b7590ff8bd1bf432e2d7 -> trunk/86eaf452c330bd871262b7590ff8bd1bf432e2d7 2025-07-17T07:01:51.9301722Z * [new tag] trunk/87d615efab194482a00d241e4d9aebc513635cf5 -> trunk/87d615efab194482a00d241e4d9aebc513635cf5 2025-07-17T07:01:51.9302625Z * [new tag] trunk/8823138e47a3200c313f6bf2d21eb689d8150f39 -> trunk/8823138e47a3200c313f6bf2d21eb689d8150f39 2025-07-17T07:01:51.9303486Z * [new tag] trunk/8892b782a85b9de9f51334e65e009b3d59899b02 -> trunk/8892b782a85b9de9f51334e65e009b3d59899b02 2025-07-17T07:01:51.9304441Z * [new tag] trunk/88b9c285e0eb68134c5f834ceccdb99bc1d255d0 -> trunk/88b9c285e0eb68134c5f834ceccdb99bc1d255d0 2025-07-17T07:01:51.9305293Z * [new tag] trunk/88c6199db09372b6e2d55a5349ab545527842727 -> trunk/88c6199db09372b6e2d55a5349ab545527842727 2025-07-17T07:01:51.9306223Z * [new tag] trunk/88cd9f34b00c22e4edefa223490fcdd6c4ff272a -> trunk/88cd9f34b00c22e4edefa223490fcdd6c4ff272a 2025-07-17T07:01:51.9307051Z * [new tag] trunk/898179331e82e5ee1ff64737f508fd6a6da711f9 -> trunk/898179331e82e5ee1ff64737f508fd6a6da711f9 2025-07-17T07:01:51.9308412Z * [new tag] trunk/899d3d3e9eec4756a2e759f0203334dbe08f664e -> trunk/899d3d3e9eec4756a2e759f0203334dbe08f664e 2025-07-17T07:01:51.9309399Z * [new tag] trunk/8a2255130025abc80e03adaeb0b90d6463f2f916 -> trunk/8a2255130025abc80e03adaeb0b90d6463f2f916 2025-07-17T07:01:51.9310190Z * [new tag] trunk/8a396c56350ade97c18a6e13b3d97939f1eea075 -> trunk/8a396c56350ade97c18a6e13b3d97939f1eea075 2025-07-17T07:01:51.9311141Z * [new tag] trunk/8a47f9d03be0e0118a43443ff5f60db7be85102b -> trunk/8a47f9d03be0e0118a43443ff5f60db7be85102b 2025-07-17T07:01:51.9312020Z * [new tag] trunk/8a88c6e85abe345d95d970d424a0295cbc89b69c -> trunk/8a88c6e85abe345d95d970d424a0295cbc89b69c 2025-07-17T07:01:51.9312935Z * [new tag] trunk/8a8fac11318778aa5e4479b369b37fdb9e10ec17 -> trunk/8a8fac11318778aa5e4479b369b37fdb9e10ec17 2025-07-17T07:01:51.9313910Z * [new tag] trunk/8ad6197b466e91eb5fbbde2c76688d40fa472ac3 -> trunk/8ad6197b466e91eb5fbbde2c76688d40fa472ac3 2025-07-17T07:01:51.9314829Z * [new tag] trunk/8b0e0e4f2301f4aeb9fccf52cd2c6d8ac13da131 -> trunk/8b0e0e4f2301f4aeb9fccf52cd2c6d8ac13da131 2025-07-17T07:01:51.9315730Z * [new tag] trunk/8b68e5b1bb4a0014a64eab25c54b716968e109a9 -> trunk/8b68e5b1bb4a0014a64eab25c54b716968e109a9 2025-07-17T07:01:51.9316600Z * [new tag] trunk/8b8684466a11ae6ac27c85dbc6a02091dadac749 -> trunk/8b8684466a11ae6ac27c85dbc6a02091dadac749 2025-07-17T07:01:51.9317527Z * [new tag] trunk/8b97e4dd8cbe23101801043fe343d7350e527540 -> trunk/8b97e4dd8cbe23101801043fe343d7350e527540 2025-07-17T07:01:51.9318489Z * [new tag] trunk/8bda95228fbefa6ce204bf4da8b632d1516431bb -> trunk/8bda95228fbefa6ce204bf4da8b632d1516431bb 2025-07-17T07:01:51.9319974Z * [new tag] trunk/8c0df6fe1769c813f57da766528ff3cc4e56943e -> trunk/8c0df6fe1769c813f57da766528ff3cc4e56943e 2025-07-17T07:01:51.9320872Z * [new tag] trunk/8c2e45008282cf5202b72a0ecb0c2951438abeea -> trunk/8c2e45008282cf5202b72a0ecb0c2951438abeea 2025-07-17T07:01:51.9321968Z * [new tag] trunk/8c3f206457a1b9d75bc95a6c30a101135fcee329 -> trunk/8c3f206457a1b9d75bc95a6c30a101135fcee329 2025-07-17T07:01:51.9322862Z * [new tag] trunk/8c5b070d1f7cf4a9b58b09a28a0aeb5fdca28679 -> trunk/8c5b070d1f7cf4a9b58b09a28a0aeb5fdca28679 2025-07-17T07:01:51.9323769Z * [new tag] trunk/8c928372b3560bb512e2cf98c94efd923caf5fa7 -> trunk/8c928372b3560bb512e2cf98c94efd923caf5fa7 2025-07-17T07:01:51.9324670Z * [new tag] trunk/8cb0c4a4da0e017834d63f3d7dc2ab29e0ad6424 -> trunk/8cb0c4a4da0e017834d63f3d7dc2ab29e0ad6424 2025-07-17T07:01:51.9325549Z * [new tag] trunk/8d070187e34d5474ae88cefe56754f729138756b -> trunk/8d070187e34d5474ae88cefe56754f729138756b 2025-07-17T07:01:51.9326370Z * [new tag] trunk/8d7ee0f4fb7e41e8a49a5a24b6686b2324c493ba -> trunk/8d7ee0f4fb7e41e8a49a5a24b6686b2324c493ba 2025-07-17T07:01:51.9327294Z * [new tag] trunk/8da774d81feace041ff39de4ba2ddd43086370d3 -> trunk/8da774d81feace041ff39de4ba2ddd43086370d3 2025-07-17T07:01:51.9328170Z * [new tag] trunk/8dff457f42c9a20f9936d22773239df33cb48c9d -> trunk/8dff457f42c9a20f9936d22773239df33cb48c9d 2025-07-17T07:01:51.9329056Z * [new tag] trunk/8e02cd9c5ab151faad80e18e648c82a204fc3735 -> trunk/8e02cd9c5ab151faad80e18e648c82a204fc3735 2025-07-17T07:01:51.9330153Z * [new tag] trunk/8e8bbfc80340f0bf0a31a03d61b9b4dd72a40469 -> trunk/8e8bbfc80340f0bf0a31a03d61b9b4dd72a40469 2025-07-17T07:01:51.9330943Z * [new tag] trunk/8e8d4b13b0f6358e249a1e6fea9831b91b41b6d0 -> trunk/8e8d4b13b0f6358e249a1e6fea9831b91b41b6d0 2025-07-17T07:01:51.9331871Z * [new tag] trunk/8eaa9f2701277f328d9d6aea1bfe7cba20792f7c -> trunk/8eaa9f2701277f328d9d6aea1bfe7cba20792f7c 2025-07-17T07:01:51.9332798Z * [new tag] trunk/8eb3c5b7a18205974fff0353b97e599ea3d0e2a4 -> trunk/8eb3c5b7a18205974fff0353b97e599ea3d0e2a4 2025-07-17T07:01:51.9333712Z * [new tag] trunk/8f02161d1012143263fdbca47ee62983448e2c7e -> trunk/8f02161d1012143263fdbca47ee62983448e2c7e 2025-07-17T07:01:51.9334697Z * [new tag] trunk/8f0998aafe5e9594ade50cd263d5c63a5aad9333 -> trunk/8f0998aafe5e9594ade50cd263d5c63a5aad9333 2025-07-17T07:01:51.9335651Z * [new tag] trunk/8f5f01bf19e01f4b74add7f3d558966152a398c7 -> trunk/8f5f01bf19e01f4b74add7f3d558966152a398c7 2025-07-17T07:01:51.9336640Z * [new tag] trunk/8f9a191db6efcf44510db87c8f6f6e96c67e9b11 -> trunk/8f9a191db6efcf44510db87c8f6f6e96c67e9b11 2025-07-17T07:01:51.9337525Z * [new tag] trunk/8fcda2c60d974e4970ce939bc828bece9903c304 -> trunk/8fcda2c60d974e4970ce939bc828bece9903c304 2025-07-17T07:01:51.9338329Z * [new tag] trunk/900fba4c073b121b6c9ce581ea27e25c13a354e5 -> trunk/900fba4c073b121b6c9ce581ea27e25c13a354e5 2025-07-17T07:01:51.9339210Z * [new tag] trunk/9056279f8159b052599a31b591a78da1acc4224c -> trunk/9056279f8159b052599a31b591a78da1acc4224c 2025-07-17T07:01:51.9340311Z * [new tag] trunk/905b0846903a0193ee651a44762e72c881f83950 -> trunk/905b0846903a0193ee651a44762e72c881f83950 2025-07-17T07:01:51.9341171Z * [new tag] trunk/905b194a2e3d1d1c9ec136dfc1b6be7e62ff4b2c -> trunk/905b194a2e3d1d1c9ec136dfc1b6be7e62ff4b2c 2025-07-17T07:01:51.9342040Z * [new tag] trunk/90618581e971d28ac6950305d72521af05ed3a42 -> trunk/90618581e971d28ac6950305d72521af05ed3a42 2025-07-17T07:01:51.9342930Z * [new tag] trunk/907aea032d2f709f26dec78b762481c4604cc186 -> trunk/907aea032d2f709f26dec78b762481c4604cc186 2025-07-17T07:01:51.9343872Z * [new tag] trunk/907d0931cc8868164c2890b0452eda5d4da49278 -> trunk/907d0931cc8868164c2890b0452eda5d4da49278 2025-07-17T07:01:51.9345022Z * [new tag] trunk/90b973a2e228f2a5e2c158e5f5cf8f59d9118fc8 -> trunk/90b973a2e228f2a5e2c158e5f5cf8f59d9118fc8 2025-07-17T07:01:51.9345796Z * [new tag] trunk/91ee9ee82d40bcc36afbe9fed8dfc2f8c19c7316 -> trunk/91ee9ee82d40bcc36afbe9fed8dfc2f8c19c7316 2025-07-17T07:01:51.9346700Z * [new tag] trunk/9222552572890fd7ff1d75884cc6a99a55ab90bc -> trunk/9222552572890fd7ff1d75884cc6a99a55ab90bc 2025-07-17T07:01:51.9347613Z * [new tag] trunk/92388bb2ab14dae7b63c2088de4d6add413d1c27 -> trunk/92388bb2ab14dae7b63c2088de4d6add413d1c27 2025-07-17T07:01:51.9348771Z * [new tag] trunk/92409b6c89fbfbd3caa79c81b1e3d9e7917d3bc7 -> trunk/92409b6c89fbfbd3caa79c81b1e3d9e7917d3bc7 2025-07-17T07:01:51.9349544Z * [new tag] trunk/924fc52e1842de756a7632e7b2777aeed77d60e5 -> trunk/924fc52e1842de756a7632e7b2777aeed77d60e5 2025-07-17T07:01:51.9350445Z * [new tag] trunk/925fbfca27580f2ec8d5f9bfd549b1fe2e665617 -> trunk/925fbfca27580f2ec8d5f9bfd549b1fe2e665617 2025-07-17T07:01:51.9351340Z * [new tag] trunk/92b7ed6d07fcd6af31cddc7b4276e626b023bd0b -> trunk/92b7ed6d07fcd6af31cddc7b4276e626b023bd0b 2025-07-17T07:01:51.9352437Z * [new tag] trunk/92c79f36dbbdb01e0aab6efb8f52df828e1046d5 -> trunk/92c79f36dbbdb01e0aab6efb8f52df828e1046d5 2025-07-17T07:01:51.9353135Z * [new tag] trunk/92ee5bd9f6bef38b3e3e81a37f4d5374395aeb40 -> trunk/92ee5bd9f6bef38b3e3e81a37f4d5374395aeb40 2025-07-17T07:01:51.9354117Z * [new tag] trunk/92f41ccc2651ab284118ec7087977ab455027eb0 -> trunk/92f41ccc2651ab284118ec7087977ab455027eb0 2025-07-17T07:01:51.9355022Z * [new tag] trunk/930b575389f9233efddf70ea7b7804ed06af80d5 -> trunk/930b575389f9233efddf70ea7b7804ed06af80d5 2025-07-17T07:01:51.9356004Z * [new tag] trunk/9328a7fb589f6372ea478e47cdae8d124030f383 -> trunk/9328a7fb589f6372ea478e47cdae8d124030f383 2025-07-17T07:01:51.9356904Z * [new tag] trunk/9338d85d4594f981c198bcbb7edfccf6b92643be -> trunk/9338d85d4594f981c198bcbb7edfccf6b92643be 2025-07-17T07:01:51.9357661Z * [new tag] trunk/9345279c6ebdbad95b7b53bc2cb6f63a4e57b2cc -> trunk/9345279c6ebdbad95b7b53bc2cb6f63a4e57b2cc 2025-07-17T07:01:51.9358384Z * [new tag] trunk/937529f0b31788726e53890f5601886c64dc9eec -> trunk/937529f0b31788726e53890f5601886c64dc9eec 2025-07-17T07:01:51.9359321Z * [new tag] trunk/938515fa75f4e9d28c62a2aaa3e0e5428c867c36 -> trunk/938515fa75f4e9d28c62a2aaa3e0e5428c867c36 2025-07-17T07:01:51.9360279Z * [new tag] trunk/93854e83b7bfde94090662e9b372d8bf44ccf5d4 -> trunk/93854e83b7bfde94090662e9b372d8bf44ccf5d4 2025-07-17T07:01:51.9361222Z * [new tag] trunk/944a140e90389eced1ec38e14cb4345811ed0b1a -> trunk/944a140e90389eced1ec38e14cb4345811ed0b1a 2025-07-17T07:01:51.9362074Z * [new tag] trunk/9462106b7e41d9a24308255e15613b464ab086ce -> trunk/9462106b7e41d9a24308255e15613b464ab086ce 2025-07-17T07:01:51.9363007Z * [new tag] trunk/94716db22214912896cf680dc3eb88574f611a42 -> trunk/94716db22214912896cf680dc3eb88574f611a42 2025-07-17T07:01:51.9363918Z * [new tag] trunk/94763f5ca75330206777f3c1b36ff124706bd9d9 -> trunk/94763f5ca75330206777f3c1b36ff124706bd9d9 2025-07-17T07:01:51.9364855Z * [new tag] trunk/94995eba07763890b86465d53c4647c089f48d0a -> trunk/94995eba07763890b86465d53c4647c089f48d0a 2025-07-17T07:01:51.9365740Z * [new tag] trunk/94ae61533719defecb08d11f63c5d9fe9a826c00 -> trunk/94ae61533719defecb08d11f63c5d9fe9a826c00 2025-07-17T07:01:51.9366692Z * [new tag] trunk/94c746bb43484787a3f5bbdc2f72bd4fb02f2964 -> trunk/94c746bb43484787a3f5bbdc2f72bd4fb02f2964 2025-07-17T07:01:51.9367568Z * [new tag] trunk/94da4523ec4362dabbf9f08f9f2efce79ab70b73 -> trunk/94da4523ec4362dabbf9f08f9f2efce79ab70b73 2025-07-17T07:01:51.9368530Z * [new tag] trunk/94f8679019ea4b1272f1ad58ad7cad87147cf5a7 -> trunk/94f8679019ea4b1272f1ad58ad7cad87147cf5a7 2025-07-17T07:01:51.9369406Z * [new tag] trunk/9508d73307b4bc1fe453677526a096e5e10a7575 -> trunk/9508d73307b4bc1fe453677526a096e5e10a7575 2025-07-17T07:01:51.9370269Z * [new tag] trunk/9513b9d03fa8950ba5d2b59cc0b1a1aab3a41c06 -> trunk/9513b9d03fa8950ba5d2b59cc0b1a1aab3a41c06 2025-07-17T07:01:51.9371182Z * [new tag] trunk/95448b2ce61c3995ecdae0bfec655a5ef81a117d -> trunk/95448b2ce61c3995ecdae0bfec655a5ef81a117d 2025-07-17T07:01:51.9372340Z * [new tag] trunk/954ce949500746763a487a8ed9800035e7cd87d4 -> trunk/954ce949500746763a487a8ed9800035e7cd87d4 2025-07-17T07:01:51.9373335Z * [new tag] trunk/95a7d1912a86d857a9f4adb55b73b57518ce1e7b -> trunk/95a7d1912a86d857a9f4adb55b73b57518ce1e7b 2025-07-17T07:01:51.9374194Z * [new tag] trunk/95bc3da9f8555897d5d2b90f75385e3b2ff7bc43 -> trunk/95bc3da9f8555897d5d2b90f75385e3b2ff7bc43 2025-07-17T07:01:51.9375056Z * [new tag] trunk/95cb42c45d17f532222611e8028c9307622cc3c9 -> trunk/95cb42c45d17f532222611e8028c9307622cc3c9 2025-07-17T07:01:51.9375973Z * [new tag] trunk/9620994067b18e846a097d1e99af85ec2426ef0a -> trunk/9620994067b18e846a097d1e99af85ec2426ef0a 2025-07-17T07:01:51.9376873Z * [new tag] trunk/9636e2cfd3e995ef977f670ad47e8e895296d992 -> trunk/9636e2cfd3e995ef977f670ad47e8e895296d992 2025-07-17T07:01:51.9377765Z * [new tag] trunk/9642c7568967ab424c5d0e04ef2cd1e82a54b5f8 -> trunk/9642c7568967ab424c5d0e04ef2cd1e82a54b5f8 2025-07-17T07:01:51.9379131Z * [new tag] trunk/9656251bb1bee12c0e2f21828dc14a4c3c06afdd -> trunk/9656251bb1bee12c0e2f21828dc14a4c3c06afdd 2025-07-17T07:01:51.9379957Z * [new tag] trunk/9665702c64af633ab23499228d552a49660a9fa2 -> trunk/9665702c64af633ab23499228d552a49660a9fa2 2025-07-17T07:01:51.9380870Z * [new tag] trunk/96897e721b76f3b72aa406696165711d74f8d260 -> trunk/96897e721b76f3b72aa406696165711d74f8d260 2025-07-17T07:01:51.9381817Z * [new tag] trunk/968f90ce7344223c788eeba59200cc22b9f94dcd -> trunk/968f90ce7344223c788eeba59200cc22b9f94dcd 2025-07-17T07:01:51.9382704Z * [new tag] trunk/96d082d06bda98addd4ad7903d315477404dc272 -> trunk/96d082d06bda98addd4ad7903d315477404dc272 2025-07-17T07:01:51.9383517Z * [new tag] trunk/96df86641048a282a4622ac93d19e4c7b6c38b8e -> trunk/96df86641048a282a4622ac93d19e4c7b6c38b8e 2025-07-17T07:01:51.9384477Z * [new tag] trunk/96e4c95cd8d03037765ffd4b8fcdaa06c4b2c51c -> trunk/96e4c95cd8d03037765ffd4b8fcdaa06c4b2c51c 2025-07-17T07:01:51.9385399Z * [new tag] trunk/9768d393fa62df8a508136f5b6634bf955d8365d -> trunk/9768d393fa62df8a508136f5b6634bf955d8365d 2025-07-17T07:01:51.9386312Z * [new tag] trunk/977abe786d907c1ff76528a550e3d53c9f3b1044 -> trunk/977abe786d907c1ff76528a550e3d53c9f3b1044 2025-07-17T07:01:51.9387224Z * [new tag] trunk/981c99fdffd9a4f510e4b89245d16aa427ee0978 -> trunk/981c99fdffd9a4f510e4b89245d16aa427ee0978 2025-07-17T07:01:51.9388101Z * [new tag] trunk/987314aa96fdf8aa051e3643b26f4209b7fe166d -> trunk/987314aa96fdf8aa051e3643b26f4209b7fe166d 2025-07-17T07:01:51.9388996Z * [new tag] trunk/98a34e8d4b4d73504afbf49f70284221e6303314 -> trunk/98a34e8d4b4d73504afbf49f70284221e6303314 2025-07-17T07:01:51.9389912Z * [new tag] trunk/98bb0c0e7888a84de1d22c4952de69a5d5b1a0b3 -> trunk/98bb0c0e7888a84de1d22c4952de69a5d5b1a0b3 2025-07-17T07:01:51.9390904Z * [new tag] trunk/9944cd0949e96d656746de6df0cb3a5a954597e4 -> trunk/9944cd0949e96d656746de6df0cb3a5a954597e4 2025-07-17T07:01:51.9391815Z * [new tag] trunk/996206e66fcafff25d0af5177497e8f792000869 -> trunk/996206e66fcafff25d0af5177497e8f792000869 2025-07-17T07:01:51.9392780Z * [new tag] trunk/9963845a4e55a3e8d35ea49deb8f8c1f6c53cb74 -> trunk/9963845a4e55a3e8d35ea49deb8f8c1f6c53cb74 2025-07-17T07:01:51.9393609Z * [new tag] trunk/9968c854b6a38857d05fb1da7bea797fbf23c880 -> trunk/9968c854b6a38857d05fb1da7bea797fbf23c880 2025-07-17T07:01:51.9394487Z * [new tag] trunk/9968edd00256fdb47e2e0129df918c5b23c06419 -> trunk/9968edd00256fdb47e2e0129df918c5b23c06419 2025-07-17T07:01:51.9395385Z * [new tag] trunk/99c1a6bdd98ecb1f9b2fb8310bcd891c8c6adf57 -> trunk/99c1a6bdd98ecb1f9b2fb8310bcd891c8c6adf57 2025-07-17T07:01:51.9396356Z * [new tag] trunk/99e99d5bfe57eddfce23f7dc9ede016cb1393c52 -> trunk/99e99d5bfe57eddfce23f7dc9ede016cb1393c52 2025-07-17T07:01:51.9397536Z * [new tag] trunk/9a2c669425379eb264f896390b8fcd8d3f2ce959 -> trunk/9a2c669425379eb264f896390b8fcd8d3f2ce959 2025-07-17T07:01:51.9398353Z * [new tag] trunk/9a42f01586267b52fe303c0c1b7a71d50cc4178e -> trunk/9a42f01586267b52fe303c0c1b7a71d50cc4178e 2025-07-17T07:01:51.9399251Z * [new tag] trunk/9a4c08ddfc9b43c07cd16355277d359dfcef50d6 -> trunk/9a4c08ddfc9b43c07cd16355277d359dfcef50d6 2025-07-17T07:01:51.9400164Z * [new tag] trunk/9a5278225fc5e7b46d54a65ae1a3f049ee49824f -> trunk/9a5278225fc5e7b46d54a65ae1a3f049ee49824f 2025-07-17T07:01:51.9400912Z * [new tag] trunk/9a5c59368d46d3793e09b72a2782ef4d12e74392 -> trunk/9a5c59368d46d3793e09b72a2782ef4d12e74392 2025-07-17T07:01:51.9401816Z * [new tag] trunk/9aaa184105b2f436b5834187c4c004c02e438491 -> trunk/9aaa184105b2f436b5834187c4c004c02e438491 2025-07-17T07:01:51.9402743Z * [new tag] trunk/9afee0fa969fd4ebab86a2cbad3217c8bade156e -> trunk/9afee0fa969fd4ebab86a2cbad3217c8bade156e 2025-07-17T07:01:51.9403615Z * [new tag] trunk/9b0013c6bb98d7161e921d03be76c81bbc0eebef -> trunk/9b0013c6bb98d7161e921d03be76c81bbc0eebef 2025-07-17T07:01:51.9404753Z * [new tag] trunk/9b122aab5dcd419b96d4a1137c45e99bde94f14e -> trunk/9b122aab5dcd419b96d4a1137c45e99bde94f14e 2025-07-17T07:01:51.9405540Z * [new tag] trunk/9b498d3bb28b8e3411ce464dd2755c5b96d92c8f -> trunk/9b498d3bb28b8e3411ce464dd2755c5b96d92c8f 2025-07-17T07:01:51.9406463Z * [new tag] trunk/9b4a748e29a720d0fade7e1298a68cc36cfd5f5e -> trunk/9b4a748e29a720d0fade7e1298a68cc36cfd5f5e 2025-07-17T07:01:51.9407337Z * [new tag] trunk/9b4db093cb63555654c868ba9e3ab68ef4c722e5 -> trunk/9b4db093cb63555654c868ba9e3ab68ef4c722e5 2025-07-17T07:01:51.9408283Z * [new tag] trunk/9bae2fcf990cd51891218895ac1b3cfe70ffc2c3 -> trunk/9bae2fcf990cd51891218895ac1b3cfe70ffc2c3 2025-07-17T07:01:51.9409145Z * [new tag] trunk/9bd0830ed8d3e753afc3e9fff2553f4c5871f6f9 -> trunk/9bd0830ed8d3e753afc3e9fff2553f4c5871f6f9 2025-07-17T07:01:51.9410049Z * [new tag] trunk/9bd42c15707a4b410ee005d5916e882a7db432bb -> trunk/9bd42c15707a4b410ee005d5916e882a7db432bb 2025-07-17T07:01:51.9411291Z * [new tag] trunk/9bdf87e8918b9a3f78d7bcb8a770c19f7c82ac15 -> trunk/9bdf87e8918b9a3f78d7bcb8a770c19f7c82ac15 2025-07-17T07:01:51.9412113Z * [new tag] trunk/9be5860bc34b643abd02ac5b5a89d820efcdfbc9 -> trunk/9be5860bc34b643abd02ac5b5a89d820efcdfbc9 2025-07-17T07:01:51.9413527Z * [new tag] trunk/9bf41633d76f7816fdcc0b7ae22a42dc91821004 -> trunk/9bf41633d76f7816fdcc0b7ae22a42dc91821004 2025-07-17T07:01:51.9414319Z * [new tag] trunk/9bf6593e96b711641606e6008a4936173fd3b458 -> trunk/9bf6593e96b711641606e6008a4936173fd3b458 2025-07-17T07:01:51.9415405Z * [new tag] trunk/9bfefda296d2df31ce9bb90410d91d8f18127545 -> trunk/9bfefda296d2df31ce9bb90410d91d8f18127545 2025-07-17T07:01:51.9416324Z * [new tag] trunk/9c189ed29a2cfa08ff357cb5042ff8c34f07d668 -> trunk/9c189ed29a2cfa08ff357cb5042ff8c34f07d668 2025-07-17T07:01:51.9417201Z * [new tag] trunk/9c39bc24807a5843f8affdf56bd71836760dc554 -> trunk/9c39bc24807a5843f8affdf56bd71836760dc554 2025-07-17T07:01:51.9418246Z * [new tag] trunk/9ca080db87f66becb2bb2c61598c0beabae73d5f -> trunk/9ca080db87f66becb2bb2c61598c0beabae73d5f 2025-07-17T07:01:51.9419070Z * [new tag] trunk/9cced33c7c9cdfc68e9ed85c4b45bdfd05dac92d -> trunk/9cced33c7c9cdfc68e9ed85c4b45bdfd05dac92d 2025-07-17T07:01:51.9420064Z * [new tag] trunk/9cd521de4dad5fc6bca94e253a9334b9a521acb0 -> trunk/9cd521de4dad5fc6bca94e253a9334b9a521acb0 2025-07-17T07:01:51.9420930Z * [new tag] trunk/9d175bc7e615b062d8ea57df6ca17edb8a6b951f -> trunk/9d175bc7e615b062d8ea57df6ca17edb8a6b951f 2025-07-17T07:01:51.9421867Z * [new tag] trunk/9d184bda2f190a3ba72a4a0d95e1fde26d6bfc08 -> trunk/9d184bda2f190a3ba72a4a0d95e1fde26d6bfc08 2025-07-17T07:01:51.9422741Z * [new tag] trunk/9d2d2270037c5a014767cdfa531863da8062bf9d -> trunk/9d2d2270037c5a014767cdfa531863da8062bf9d 2025-07-17T07:01:51.9423897Z * [new tag] trunk/9d59b516e9b3026948918e3ff8c2ef55a33d13ad -> trunk/9d59b516e9b3026948918e3ff8c2ef55a33d13ad 2025-07-17T07:01:51.9424679Z * [new tag] trunk/9d677389cb5eb75d423860c55519b522961a9195 -> trunk/9d677389cb5eb75d423860c55519b522961a9195 2025-07-17T07:01:51.9425651Z * [new tag] trunk/9de23d0c29dfac8dc0f6f234bdbcd85a6375fa81 -> trunk/9de23d0c29dfac8dc0f6f234bdbcd85a6375fa81 2025-07-17T07:01:51.9426506Z * [new tag] trunk/9df0176408518b30ac172837bd697c9d19b19a98 -> trunk/9df0176408518b30ac172837bd697c9d19b19a98 2025-07-17T07:01:51.9427456Z * [new tag] trunk/9df2e8020f190bdad02258aff2b2f88f7db3dcab -> trunk/9df2e8020f190bdad02258aff2b2f88f7db3dcab 2025-07-17T07:01:51.9428348Z * [new tag] trunk/9e132b770e06c2399f827d3d1963fe036e9744e6 -> trunk/9e132b770e06c2399f827d3d1963fe036e9744e6 2025-07-17T07:01:51.9429149Z * [new tag] trunk/9e5f4a844c0aebf964a435094005c92713fbe99a -> trunk/9e5f4a844c0aebf964a435094005c92713fbe99a 2025-07-17T07:01:51.9430043Z * [new tag] trunk/9e88d6c857fd2b276427f4527130e2ecb7ed731e -> trunk/9e88d6c857fd2b276427f4527130e2ecb7ed731e 2025-07-17T07:01:51.9431118Z * [new tag] trunk/9e9484d022f8d65e2066b5683b75c84ba0f36332 -> trunk/9e9484d022f8d65e2066b5683b75c84ba0f36332 2025-07-17T07:01:51.9432262Z * [new tag] trunk/9ed0060225a7b78c60c42f29be94444b537edd4a -> trunk/9ed0060225a7b78c60c42f29be94444b537edd4a 2025-07-17T07:01:51.9433098Z * [new tag] trunk/9f18482d41227df3cf2248dfa54bd6601e61e1ca -> trunk/9f18482d41227df3cf2248dfa54bd6601e61e1ca 2025-07-17T07:01:51.9433997Z * [new tag] trunk/9f37cce69334bccebf4b21503f0047d0c0bb320c -> trunk/9f37cce69334bccebf4b21503f0047d0c0bb320c 2025-07-17T07:01:51.9434920Z * [new tag] trunk/9f5153b1a495b3b2b7ad9f22a064fe4074d742aa -> trunk/9f5153b1a495b3b2b7ad9f22a064fe4074d742aa 2025-07-17T07:01:51.9435829Z * [new tag] trunk/9f5276dc07c788533af8945b1605df47a33313e7 -> trunk/9f5276dc07c788533af8945b1605df47a33313e7 2025-07-17T07:01:51.9436747Z * [new tag] trunk/9fe2d156a9f81d67e248c0edaf7feee1a8d6c4d5 -> trunk/9fe2d156a9f81d67e248c0edaf7feee1a8d6c4d5 2025-07-17T07:01:51.9437663Z * [new tag] trunk/9fed2addedb42da86b657165fe14eadc911232cf -> trunk/9fed2addedb42da86b657165fe14eadc911232cf 2025-07-17T07:01:51.9438882Z * [new tag] trunk/a00a697c1748937b0c6d13508ff76b8af4957d8a -> trunk/a00a697c1748937b0c6d13508ff76b8af4957d8a 2025-07-17T07:01:51.9439686Z * [new tag] trunk/a0308edb6cdfd8983e80a499890d9f320556e844 -> trunk/a0308edb6cdfd8983e80a499890d9f320556e844 2025-07-17T07:01:51.9440622Z * [new tag] trunk/a04a13c44908fe0ace4f76a228d045dbf5c015bc -> trunk/a04a13c44908fe0ace4f76a228d045dbf5c015bc 2025-07-17T07:01:51.9441609Z * [new tag] trunk/a0e0abd0379ed403adc926d3cc5459fa35aff0a0 -> trunk/a0e0abd0379ed403adc926d3cc5459fa35aff0a0 2025-07-17T07:01:51.9442567Z * [new tag] trunk/a10024d7dea47c52469059a47efe376eb20adca0 -> trunk/a10024d7dea47c52469059a47efe376eb20adca0 2025-07-17T07:01:51.9443417Z * [new tag] trunk/a1057cda31fe890d237fe9a2e6b3314cd4be3439 -> trunk/a1057cda31fe890d237fe9a2e6b3314cd4be3439 2025-07-17T07:01:51.9444384Z * [new tag] trunk/a1257446f8eaf81eb758e89b4e784bed47b9c733 -> trunk/a1257446f8eaf81eb758e89b4e784bed47b9c733 2025-07-17T07:01:51.9445275Z * [new tag] trunk/a1282b18239204b0344884ebd232b33b2d8b748f -> trunk/a1282b18239204b0344884ebd232b33b2d8b748f 2025-07-17T07:01:51.9446167Z * [new tag] trunk/a14f427db68e54500ef4cd9ed34cb9537263bb74 -> trunk/a14f427db68e54500ef4cd9ed34cb9537263bb74 2025-07-17T07:01:51.9447076Z * [new tag] trunk/a1dad2f2d2c082e2a3784c3d585ef0204b7ccf75 -> trunk/a1dad2f2d2c082e2a3784c3d585ef0204b7ccf75 2025-07-17T07:01:51.9447919Z * [new tag] trunk/a1e4f1f98a0b9596fe52aaf2f85b0778498d5f49 -> trunk/a1e4f1f98a0b9596fe52aaf2f85b0778498d5f49 2025-07-17T07:01:51.9448852Z * [new tag] trunk/a205e8fd7357cf5c2b95eb4bec7c8ff7b5c41651 -> trunk/a205e8fd7357cf5c2b95eb4bec7c8ff7b5c41651 2025-07-17T07:01:51.9449817Z * [new tag] trunk/a21806f0386c2671fc11ae37fe2fe96c4aeb37d9 -> trunk/a21806f0386c2671fc11ae37fe2fe96c4aeb37d9 2025-07-17T07:01:51.9450781Z * [new tag] trunk/a23f4471b952d8cd630b860639e0aaa9be957d60 -> trunk/a23f4471b952d8cd630b860639e0aaa9be957d60 2025-07-17T07:01:51.9451551Z * [new tag] trunk/a24afbff3f44399ac9513addce6bd50cacab22d2 -> trunk/a24afbff3f44399ac9513addce6bd50cacab22d2 2025-07-17T07:01:51.9452296Z * [new tag] trunk/a24ce67deedab025531660ddb44c148bb066edf8 -> trunk/a24ce67deedab025531660ddb44c148bb066edf8 2025-07-17T07:01:51.9453181Z * [new tag] trunk/a25d1443fae79a9984eaa44e973b2f5645c85da8 -> trunk/a25d1443fae79a9984eaa44e973b2f5645c85da8 2025-07-17T07:01:51.9454078Z * [new tag] trunk/a26bf3892778ca7cc457c772a1f5194c11b6f33c -> trunk/a26bf3892778ca7cc457c772a1f5194c11b6f33c 2025-07-17T07:01:51.9455005Z * [new tag] trunk/a28e6ae38f1e1de33ecf48b679ff1eda71fc2efc -> trunk/a28e6ae38f1e1de33ecf48b679ff1eda71fc2efc 2025-07-17T07:01:51.9456016Z * [new tag] trunk/a2a75be0f8d680cf1dc36c50f34f225b0c7c4d6a -> trunk/a2a75be0f8d680cf1dc36c50f34f225b0c7c4d6a 2025-07-17T07:01:51.9456944Z * [new tag] trunk/a2ad16be72bf989c01b96c4c56b1c108a71c087f -> trunk/a2ad16be72bf989c01b96c4c56b1c108a71c087f 2025-07-17T07:01:51.9458023Z * [new tag] trunk/a2b0b2698d5b953861b2e4f3cdee11136f07bd3b -> trunk/a2b0b2698d5b953861b2e4f3cdee11136f07bd3b 2025-07-17T07:01:51.9458885Z * [new tag] trunk/a3098a74d494020dbb906c05ef047013e1921662 -> trunk/a3098a74d494020dbb906c05ef047013e1921662 2025-07-17T07:01:51.9459769Z * [new tag] trunk/a317c63d1b70a7989a4cab09394691fb7a3d8323 -> trunk/a317c63d1b70a7989a4cab09394691fb7a3d8323 2025-07-17T07:01:51.9460557Z * [new tag] trunk/a355158fcba807fda1e47e5ee42babdbcf447947 -> trunk/a355158fcba807fda1e47e5ee42babdbcf447947 2025-07-17T07:01:51.9461490Z * [new tag] trunk/a35b3a9b95bbe0703de33802162a83cb7970ddc0 -> trunk/a35b3a9b95bbe0703de33802162a83cb7970ddc0 2025-07-17T07:01:51.9462380Z * [new tag] trunk/a369350065493109d1abfbb994695777ab11bcf4 -> trunk/a369350065493109d1abfbb994695777ab11bcf4 2025-07-17T07:01:51.9463566Z * [new tag] trunk/a38f433be2e94a64b095a44ba39879d02d0c2316 -> trunk/a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:01:51.9464388Z * [new tag] trunk/a3ec6d64b24fdbd61164240f3bcd8530434812b5 -> trunk/a3ec6d64b24fdbd61164240f3bcd8530434812b5 2025-07-17T07:01:51.9465291Z * [new tag] trunk/a46ea8a364e528828c4369d58d53802291f0b49b -> trunk/a46ea8a364e528828c4369d58d53802291f0b49b 2025-07-17T07:01:51.9466292Z * [new tag] trunk/a47ca4fc746a663c0e97d55a87815d0965d0a7e9 -> trunk/a47ca4fc746a663c0e97d55a87815d0965d0a7e9 2025-07-17T07:01:51.9467174Z * [new tag] trunk/a4b59498c5222a02a706fa1e1308a6035d65feab -> trunk/a4b59498c5222a02a706fa1e1308a6035d65feab 2025-07-17T07:01:51.9468116Z * [new tag] trunk/a4c7e7f98373ad8f309e419c6f98b0134933dcda -> trunk/a4c7e7f98373ad8f309e419c6f98b0134933dcda 2025-07-17T07:01:51.9469043Z * [new tag] trunk/a4d753295ee5662056bdfd1b00fa242071ac7125 -> trunk/a4d753295ee5662056bdfd1b00fa242071ac7125 2025-07-17T07:01:51.9469992Z * [new tag] trunk/a4ea242edc8626250e54a62aa073cba0e5322af2 -> trunk/a4ea242edc8626250e54a62aa073cba0e5322af2 2025-07-17T07:01:51.9470967Z * [new tag] trunk/a529a5daf5d348f4f8dac4fcd85521153b6a3d4d -> trunk/a529a5daf5d348f4f8dac4fcd85521153b6a3d4d 2025-07-17T07:01:51.9471980Z * [new tag] trunk/a5938ff431202864c92fec7d9042574348b1c15b -> trunk/a5938ff431202864c92fec7d9042574348b1c15b 2025-07-17T07:01:51.9473107Z * [new tag] trunk/a5b4463d60e5beaec546843b24876ce573890784 -> trunk/a5b4463d60e5beaec546843b24876ce573890784 2025-07-17T07:01:51.9473771Z * [new tag] trunk/a5c61eb78dda6b7b4c4beb8bc616f9545ea4807b -> trunk/a5c61eb78dda6b7b4c4beb8bc616f9545ea4807b 2025-07-17T07:01:51.9474719Z * [new tag] trunk/a5cbb2bcb3c005909a3c10042605e861e6390927 -> trunk/a5cbb2bcb3c005909a3c10042605e861e6390927 2025-07-17T07:01:51.9475669Z * [new tag] trunk/a5df6ffbc2cf8d61fc98bca1c10f22ff64b2318e -> trunk/a5df6ffbc2cf8d61fc98bca1c10f22ff64b2318e 2025-07-17T07:01:51.9476651Z * [new tag] trunk/a5e68814d556cf67c6511876410970dd08c3dd6d -> trunk/a5e68814d556cf67c6511876410970dd08c3dd6d 2025-07-17T07:01:51.9477572Z * [new tag] trunk/a5f59cc2eab3a5201712c52fe48c268357ba4f3c -> trunk/a5f59cc2eab3a5201712c52fe48c268357ba4f3c 2025-07-17T07:01:51.9478505Z * [new tag] trunk/a6084b71edb8d2856356724b5e71c4e2a861867f -> trunk/a6084b71edb8d2856356724b5e71c4e2a861867f 2025-07-17T07:01:51.9479581Z * [new tag] trunk/a6210fd07b8fe1924f24229bb30562608af4f41a -> trunk/a6210fd07b8fe1924f24229bb30562608af4f41a 2025-07-17T07:01:51.9480406Z * [new tag] trunk/a666cf3b38f5e7739f94b175f152b127a6d51344 -> trunk/a666cf3b38f5e7739f94b175f152b127a6d51344 2025-07-17T07:01:51.9481279Z * [new tag] trunk/a67eb1a0d627d59d95a99722d37cab3bf054261f -> trunk/a67eb1a0d627d59d95a99722d37cab3bf054261f 2025-07-17T07:01:51.9482290Z * [new tag] trunk/a69e27ca5ad4287add73972ef1b34b469e3c7d23 -> trunk/a69e27ca5ad4287add73972ef1b34b469e3c7d23 2025-07-17T07:01:51.9483204Z * [new tag] trunk/a6a3a441442a96f38d0771c985f753223cea2ba0 -> trunk/a6a3a441442a96f38d0771c985f753223cea2ba0 2025-07-17T07:01:51.9484167Z * [new tag] trunk/a6a8641c8aee5797324c3d41749bcae62b4e5de8 -> trunk/a6a8641c8aee5797324c3d41749bcae62b4e5de8 2025-07-17T07:01:51.9485131Z * [new tag] trunk/a6fab82b16011213cb010c8c50461b9a680748a2 -> trunk/a6fab82b16011213cb010c8c50461b9a680748a2 2025-07-17T07:01:51.9486061Z * [new tag] trunk/a73d9e0aec9319e56ba0c9b0ccc25db69c739faf -> trunk/a73d9e0aec9319e56ba0c9b0ccc25db69c739faf 2025-07-17T07:01:51.9486931Z * [new tag] trunk/a767e50adca9e08dd638be60c9e5604de15b9f15 -> trunk/a767e50adca9e08dd638be60c9e5604de15b9f15 2025-07-17T07:01:51.9487697Z * [new tag] trunk/a7b29c88b1f55bac02f38bf1faba68b6c4be4c9c -> trunk/a7b29c88b1f55bac02f38bf1faba68b6c4be4c9c 2025-07-17T07:01:51.9488646Z * [new tag] trunk/a7eb153bba0534daaa1b652d44bb9fb5bd1ac7c7 -> trunk/a7eb153bba0534daaa1b652d44bb9fb5bd1ac7c7 2025-07-17T07:01:51.9489539Z * [new tag] trunk/a82c171bb26949dda5f5932051a905a9bab38761 -> trunk/a82c171bb26949dda5f5932051a905a9bab38761 2025-07-17T07:01:51.9490438Z * [new tag] trunk/a85ad5552532926b78d489d1b60671f74bf6dc8e -> trunk/a85ad5552532926b78d489d1b60671f74bf6dc8e 2025-07-17T07:01:51.9491467Z * [new tag] trunk/a87dfc7480bc98d33dab274d7e95723aa9cf7840 -> trunk/a87dfc7480bc98d33dab274d7e95723aa9cf7840 2025-07-17T07:01:51.9492340Z * [new tag] trunk/a8b973673798ca79dfe616c9080415d09f9e990d -> trunk/a8b973673798ca79dfe616c9080415d09f9e990d 2025-07-17T07:01:51.9493232Z * [new tag] trunk/a8ec7babcf27584cf068c3a5a04bb3ea0102ab99 -> trunk/a8ec7babcf27584cf068c3a5a04bb3ea0102ab99 2025-07-17T07:01:51.9494119Z * [new tag] trunk/a8fe982993221048ee1665ce28add1b02888784d -> trunk/a8fe982993221048ee1665ce28add1b02888784d 2025-07-17T07:01:51.9495126Z * [new tag] trunk/a92b24cd839aa6807f3c9ea8b8787da99c4490c0 -> trunk/a92b24cd839aa6807f3c9ea8b8787da99c4490c0 2025-07-17T07:01:51.9496267Z * [new tag] trunk/a9352bd25e9f070e8f846b8fdbcd7c90ad5c66b3 -> trunk/a9352bd25e9f070e8f846b8fdbcd7c90ad5c66b3 2025-07-17T07:01:51.9497443Z * [new tag] trunk/a952956d05dd617355007ae31d8e936474a35f14 -> trunk/a952956d05dd617355007ae31d8e936474a35f14 2025-07-17T07:01:51.9498737Z * [new tag] trunk/a9537b626c91ce617139ade60b9107a2805a4248 -> trunk/a9537b626c91ce617139ade60b9107a2805a4248 2025-07-17T07:01:51.9499689Z * [new tag] trunk/a95504b10fff38b5308660e0b535961beed6c9f1 -> trunk/a95504b10fff38b5308660e0b535961beed6c9f1 2025-07-17T07:01:51.9500951Z * [new tag] trunk/a9a0501ec40d8fab07b0f8d2fcbf8eb7147c1ae8 -> trunk/a9a0501ec40d8fab07b0f8d2fcbf8eb7147c1ae8 2025-07-17T07:01:51.9501804Z * [new tag] trunk/a9ac9f2635a9be28eaf267ed41070081a4ce05d8 -> trunk/a9ac9f2635a9be28eaf267ed41070081a4ce05d8 2025-07-17T07:01:51.9502568Z * [new tag] trunk/a9d5157e2596e1d830158e83fab618aab1d4ee39 -> trunk/a9d5157e2596e1d830158e83fab618aab1d4ee39 2025-07-17T07:01:51.9503345Z * [new tag] trunk/a9ee4250d55c6342b80e2d57a8ad9a1992ddcdce -> trunk/a9ee4250d55c6342b80e2d57a8ad9a1992ddcdce 2025-07-17T07:01:51.9504285Z * [new tag] trunk/a9ef7c4d044d386993a6e82b5ad37b7db759b502 -> trunk/a9ef7c4d044d386993a6e82b5ad37b7db759b502 2025-07-17T07:01:51.9505170Z * [new tag] trunk/a9f902add02383ca1b0386eb865767641975fede -> trunk/a9f902add02383ca1b0386eb865767641975fede 2025-07-17T07:01:51.9506154Z * [new tag] trunk/aa116285768ba6a8cd34bf8d245e38d04c88810a -> trunk/aa116285768ba6a8cd34bf8d245e38d04c88810a 2025-07-17T07:01:51.9507062Z * [new tag] trunk/aa280ea19fb20923d048909fa98af092e18ca2fb -> trunk/aa280ea19fb20923d048909fa98af092e18ca2fb 2025-07-17T07:01:51.9508143Z * [new tag] trunk/aa2d54148d476383986855af3fe53862da861dda -> trunk/aa2d54148d476383986855af3fe53862da861dda 2025-07-17T07:01:51.9508968Z * [new tag] trunk/aab949aa96a6b197b75ffa25608fa84049ff68ad -> trunk/aab949aa96a6b197b75ffa25608fa84049ff68ad 2025-07-17T07:01:51.9510083Z * [new tag] trunk/aac0e8f0e998a9381f0eb5dd303a853515109eb0 -> trunk/aac0e8f0e998a9381f0eb5dd303a853515109eb0 2025-07-17T07:01:51.9510919Z * [new tag] trunk/aacb9440791f4e9567e2123f19bb062e75c2d242 -> trunk/aacb9440791f4e9567e2123f19bb062e75c2d242 2025-07-17T07:01:51.9511876Z * [new tag] trunk/ab2294d8289a7757a2fc321cdefac88e2b378edf -> trunk/ab2294d8289a7757a2fc321cdefac88e2b378edf 2025-07-17T07:01:51.9512763Z * [new tag] trunk/ab3393e923514438c9bdfc1afcd0b2aac8be8de9 -> trunk/ab3393e923514438c9bdfc1afcd0b2aac8be8de9 2025-07-17T07:01:51.9513852Z * [new tag] trunk/ab51a937371e61f91cacae7d9fb4b1fe61f24f03 -> trunk/ab51a937371e61f91cacae7d9fb4b1fe61f24f03 2025-07-17T07:01:51.9514779Z * [new tag] trunk/ab56e5add9c36f611a4d9af2f43213926b65d7b4 -> trunk/ab56e5add9c36f611a4d9af2f43213926b65d7b4 2025-07-17T07:01:51.9515681Z * [new tag] trunk/ab655816b8f76f511fb2262d45276d8d1b13d59c -> trunk/ab655816b8f76f511fb2262d45276d8d1b13d59c 2025-07-17T07:01:51.9516669Z * [new tag] trunk/ab6cb34480a14f3cf2446519189ee5f0c5e7278d -> trunk/ab6cb34480a14f3cf2446519189ee5f0c5e7278d 2025-07-17T07:01:51.9517528Z * [new tag] trunk/ab81fb846cd0e52bbc6a2eb366706350639e209f -> trunk/ab81fb846cd0e52bbc6a2eb366706350639e209f 2025-07-17T07:01:51.9518480Z * [new tag] trunk/ab8874bd26894a8ab956408ceee2e89c9ad7f450 -> trunk/ab8874bd26894a8ab956408ceee2e89c9ad7f450 2025-07-17T07:01:51.9519402Z * [new tag] trunk/abbdf9f3632af505b3265b4258a6c185b257edde -> trunk/abbdf9f3632af505b3265b4258a6c185b257edde 2025-07-17T07:01:51.9520542Z * [new tag] trunk/abeae997a35b1920a45be9c26eff7474f2c6c5dd -> trunk/abeae997a35b1920a45be9c26eff7474f2c6c5dd 2025-07-17T07:01:51.9521634Z * [new tag] trunk/abf4da0d242274ba7ab4c0859d105cae74a2dbc5 -> trunk/abf4da0d242274ba7ab4c0859d105cae74a2dbc5 2025-07-17T07:01:51.9522458Z * [new tag] trunk/ac706bfc7f942b8a97401486a840dd8f6452f5cb -> trunk/ac706bfc7f942b8a97401486a840dd8f6452f5cb 2025-07-17T07:01:51.9523371Z * [new tag] trunk/ac86ec0e60370c037e018137f2048cafd47c5c28 -> trunk/ac86ec0e60370c037e018137f2048cafd47c5c28 2025-07-17T07:01:51.9524328Z * [new tag] trunk/acaf6ba3c6d0bdec88ab3f6c2ef82650050558d2 -> trunk/acaf6ba3c6d0bdec88ab3f6c2ef82650050558d2 2025-07-17T07:01:51.9525213Z * [new tag] trunk/ad86c05b78d343f3346d07a07e17639e421d2cf4 -> trunk/ad86c05b78d343f3346d07a07e17639e421d2cf4 2025-07-17T07:01:51.9526185Z * [new tag] trunk/ada44e5ba78be9377814678d1986556af2d6e570 -> trunk/ada44e5ba78be9377814678d1986556af2d6e570 2025-07-17T07:01:51.9527139Z * [new tag] trunk/add0b450bd9907ba9d089c79ca4af96c0590d8ff -> trunk/add0b450bd9907ba9d089c79ca4af96c0590d8ff 2025-07-17T07:01:51.9528122Z * [new tag] trunk/adf6dd1e44c1b006ea6078576b64d5616cecfa5a -> trunk/adf6dd1e44c1b006ea6078576b64d5616cecfa5a 2025-07-17T07:01:51.9529116Z * [new tag] trunk/adf9644440e0d007853e7fdfd67b6a59248f4fbb -> trunk/adf9644440e0d007853e7fdfd67b6a59248f4fbb 2025-07-17T07:01:51.9530058Z * [new tag] trunk/ae0f1f89847564b04bdc43395e664e48f08aaca2 -> trunk/ae0f1f89847564b04bdc43395e664e48f08aaca2 2025-07-17T07:01:51.9531198Z * [new tag] trunk/ae1094b72b7db368f292ed6033de5b495dcdc878 -> trunk/ae1094b72b7db368f292ed6033de5b495dcdc878 2025-07-17T07:01:51.9532115Z * [new tag] trunk/ae86e8f6c829a3cfa9204949156fce2d048c919b -> trunk/ae86e8f6c829a3cfa9204949156fce2d048c919b 2025-07-17T07:01:51.9533043Z * [new tag] trunk/aec569da2325c09487ab659a0f28c3b51e60d779 -> trunk/aec569da2325c09487ab659a0f28c3b51e60d779 2025-07-17T07:01:51.9533946Z * [new tag] trunk/aee2bfc5bae2974f5a3023d51dfa8e3e641144cc -> trunk/aee2bfc5bae2974f5a3023d51dfa8e3e641144cc 2025-07-17T07:01:51.9534838Z * [new tag] trunk/aeffb68d3466635e4e95c50bffd7dfebaba94da2 -> trunk/aeffb68d3466635e4e95c50bffd7dfebaba94da2 2025-07-17T07:01:51.9535823Z * [new tag] trunk/af284b45d5cbfd19d168bf1d118b3a40cd8b3a92 -> trunk/af284b45d5cbfd19d168bf1d118b3a40cd8b3a92 2025-07-17T07:01:51.9536735Z * [new tag] trunk/af3d06909400413801b726a96f3ff52b5ee9ebb5 -> trunk/af3d06909400413801b726a96f3ff52b5ee9ebb5 2025-07-17T07:01:51.9537644Z * [new tag] trunk/af9c92b4cb9f406129dfd8c64c082c0aaf7c723f -> trunk/af9c92b4cb9f406129dfd8c64c082c0aaf7c723f 2025-07-17T07:01:51.9538535Z * [new tag] trunk/aff9c1eec599b8fa97240d192534b0f650c16b1b -> trunk/aff9c1eec599b8fa97240d192534b0f650c16b1b 2025-07-17T07:01:51.9539431Z * [new tag] trunk/b00b641ff1fbfa8f4f6152ffc631e0d24145a7a8 -> trunk/b00b641ff1fbfa8f4f6152ffc631e0d24145a7a8 2025-07-17T07:01:51.9540310Z * [new tag] trunk/b020971e7806bba39aecf636e59e743911831ad8 -> trunk/b020971e7806bba39aecf636e59e743911831ad8 2025-07-17T07:01:51.9541202Z * [new tag] trunk/b0556110e58e3bcf2c872b933e4fd4a0d34398ad -> trunk/b0556110e58e3bcf2c872b933e4fd4a0d34398ad 2025-07-17T07:01:51.9542204Z * [new tag] trunk/b07725a9516028a485153c4b5356b3e33b990f81 -> trunk/b07725a9516028a485153c4b5356b3e33b990f81 2025-07-17T07:01:51.9543040Z * [new tag] trunk/b096341963c4af3b2b0d7f598eba1cdc1c74fb6e -> trunk/b096341963c4af3b2b0d7f598eba1cdc1c74fb6e 2025-07-17T07:01:51.9543924Z * [new tag] trunk/b09bd414a6ccba158c09f586a278051588d90936 -> trunk/b09bd414a6ccba158c09f586a278051588d90936 2025-07-17T07:01:51.9544871Z * [new tag] trunk/b0fbbef1361ccaab8a5aec8e7cd62150e7b361de -> trunk/b0fbbef1361ccaab8a5aec8e7cd62150e7b361de 2025-07-17T07:01:51.9545752Z * [new tag] trunk/b146ca74f01df3cf711fd0f855e05805e490156c -> trunk/b146ca74f01df3cf711fd0f855e05805e490156c 2025-07-17T07:01:51.9546641Z * [new tag] trunk/b146e1a264960e5fb79cf4621ebe4b1345db3e64 -> trunk/b146e1a264960e5fb79cf4621ebe4b1345db3e64 2025-07-17T07:01:51.9547503Z * [new tag] trunk/b147b6c0e31d43a405416743fde1b0b606aa3839 -> trunk/b147b6c0e31d43a405416743fde1b0b606aa3839 2025-07-17T07:01:51.9548374Z * [new tag] trunk/b1713c665516d8355f5491d10b31f98c74ce27f1 -> trunk/b1713c665516d8355f5491d10b31f98c74ce27f1 2025-07-17T07:01:51.9549269Z * [new tag] trunk/b1a54fab9bcb0cc167773f9a885d4170447e1c68 -> trunk/b1a54fab9bcb0cc167773f9a885d4170447e1c68 2025-07-17T07:01:51.9550125Z * [new tag] trunk/b1b8e57cda03bd016594a4fc4944fa5e7f9dd961 -> trunk/b1b8e57cda03bd016594a4fc4944fa5e7f9dd961 2025-07-17T07:01:51.9551082Z * [new tag] trunk/b1d62febd03ac421197d5516596f98d3c46e9b44 -> trunk/b1d62febd03ac421197d5516596f98d3c46e9b44 2025-07-17T07:01:51.9552023Z * [new tag] trunk/b221be9140689d180ec339db05c5e235a95949d3 -> trunk/b221be9140689d180ec339db05c5e235a95949d3 2025-07-17T07:01:51.9553158Z * [new tag] trunk/b22b93a6babf86e823e979528bc0aab88fb7a012 -> trunk/b22b93a6babf86e823e979528bc0aab88fb7a012 2025-07-17T07:01:51.9554048Z * [new tag] trunk/b26da7741be37693ab1cd21115f3fca15b1cdb6b -> trunk/b26da7741be37693ab1cd21115f3fca15b1cdb6b 2025-07-17T07:01:51.9554986Z * [new tag] trunk/b2d473c8f8a6d0677940c174b38511f5ab3c3b65 -> trunk/b2d473c8f8a6d0677940c174b38511f5ab3c3b65 2025-07-17T07:01:51.9555964Z * [new tag] trunk/b2fc9cfea16c8eb52c1ce79b2032793dd1a545fb -> trunk/b2fc9cfea16c8eb52c1ce79b2032793dd1a545fb 2025-07-17T07:01:51.9556846Z * [new tag] trunk/b30e04b3c8a83e19dc9123f2f0229d80e208fb50 -> trunk/b30e04b3c8a83e19dc9123f2f0229d80e208fb50 2025-07-17T07:01:51.9557779Z * [new tag] trunk/b354328ecd6a313eab905ab6f74aed5ea2d3a2ce -> trunk/b354328ecd6a313eab905ab6f74aed5ea2d3a2ce 2025-07-17T07:01:51.9559258Z * [new tag] trunk/b359571c6043b40c4ae4fbb07135fd0f04902e21 -> trunk/b359571c6043b40c4ae4fbb07135fd0f04902e21 2025-07-17T07:01:51.9560097Z * [new tag] trunk/b3b4d28f4cf149cb7448c556d359ca852d815ab1 -> trunk/b3b4d28f4cf149cb7448c556d359ca852d815ab1 2025-07-17T07:01:51.9560986Z * [new tag] trunk/b40981c63078d99bb07afbdcfec1ce8f7519f26b -> trunk/b40981c63078d99bb07afbdcfec1ce8f7519f26b 2025-07-17T07:01:51.9561848Z * [new tag] trunk/b40c0b61eb5f5b6104cb732e709819e84518faa7 -> trunk/b40c0b61eb5f5b6104cb732e709819e84518faa7 2025-07-17T07:01:51.9562715Z * [new tag] trunk/b4228a94d11b1ba6599f443267824d2d918644f2 -> trunk/b4228a94d11b1ba6599f443267824d2d918644f2 2025-07-17T07:01:51.9563939Z * [new tag] trunk/b44306d3681d5b248e6b439d293ea0d5a8903a61 -> trunk/b44306d3681d5b248e6b439d293ea0d5a8903a61 2025-07-17T07:01:51.9564732Z * [new tag] trunk/b4442f42a93390760bb923cbe13b80993f5e8e78 -> trunk/b4442f42a93390760bb923cbe13b80993f5e8e78 2025-07-17T07:01:51.9565511Z * [new tag] trunk/b4476ca378be50034bd5cdc1eaa95104337c998a -> trunk/b4476ca378be50034bd5cdc1eaa95104337c998a 2025-07-17T07:01:51.9566532Z * [new tag] trunk/b487003182b7f2d6697064f184515369bf6c8cce -> trunk/b487003182b7f2d6697064f184515369bf6c8cce 2025-07-17T07:01:51.9567355Z * [new tag] trunk/b49edc0d6c72f744d744a026b0ce0d689e34b83b -> trunk/b49edc0d6c72f744d744a026b0ce0d689e34b83b 2025-07-17T07:01:51.9568289Z * [new tag] trunk/b4e3c9ea34cb607324639cd0bb0129740c300721 -> trunk/b4e3c9ea34cb607324639cd0bb0129740c300721 2025-07-17T07:01:51.9569184Z * [new tag] trunk/b4fc42ca807efc1a1b78f52d5481711a4e8bbea3 -> trunk/b4fc42ca807efc1a1b78f52d5481711a4e8bbea3 2025-07-17T07:01:51.9570166Z * [new tag] trunk/b50075343aeb519d135e00c44ff3577eaa25c61b -> trunk/b50075343aeb519d135e00c44ff3577eaa25c61b 2025-07-17T07:01:51.9571592Z * [new tag] trunk/b54eac2a5ed31106393bbc338de8637817809a1f -> trunk/b54eac2a5ed31106393bbc338de8637817809a1f 2025-07-17T07:01:51.9572264Z * [new tag] trunk/b5bfbba1841da810305262c7f47ee2dae54f335e -> trunk/b5bfbba1841da810305262c7f47ee2dae54f335e 2025-07-17T07:01:51.9573433Z * [new tag] trunk/b5c8b8d09f006b1b2911858882a56dfe6e325f36 -> trunk/b5c8b8d09f006b1b2911858882a56dfe6e325f36 2025-07-17T07:01:51.9574221Z * [new tag] trunk/b5ce77c1f5964293299eb1366f341872a4e47fa6 -> trunk/b5ce77c1f5964293299eb1366f341872a4e47fa6 2025-07-17T07:01:51.9575225Z * [new tag] trunk/b60569ed946ddcc267150a675916d68d7cac9085 -> trunk/b60569ed946ddcc267150a675916d68d7cac9085 2025-07-17T07:01:51.9576192Z * [new tag] trunk/b6188174795956f959feae0cbc33cbdb7901c4b6 -> trunk/b6188174795956f959feae0cbc33cbdb7901c4b6 2025-07-17T07:01:51.9577119Z * [new tag] trunk/b642a5c118baf4cd47dd7fe0190b83e04cee960f -> trunk/b642a5c118baf4cd47dd7fe0190b83e04cee960f 2025-07-17T07:01:51.9578031Z * [new tag] trunk/b6454a9058f2e50be9a3c26c128fec843b09c154 -> trunk/b6454a9058f2e50be9a3c26c128fec843b09c154 2025-07-17T07:01:51.9579160Z * [new tag] trunk/b6add8c8ba927a9f1687134ec2734b02a6ccb3c0 -> trunk/b6add8c8ba927a9f1687134ec2734b02a6ccb3c0 2025-07-17T07:01:51.9580226Z * [new tag] trunk/b6c00dfe249a7bfc1d61a322d5bc30f164353abf -> trunk/b6c00dfe249a7bfc1d61a322d5bc30f164353abf 2025-07-17T07:01:51.9581191Z * [new tag] trunk/b6e625e34f358c71b62409f96dc1e22e4791beef -> trunk/b6e625e34f358c71b62409f96dc1e22e4791beef 2025-07-17T07:01:51.9582148Z * [new tag] trunk/b6f84b3b0fef781653911420253dcff6767197dc -> trunk/b6f84b3b0fef781653911420253dcff6767197dc 2025-07-17T07:01:51.9583053Z * [new tag] trunk/b754b1fa43d20f5b31e17c396487ab56991912da -> trunk/b754b1fa43d20f5b31e17c396487ab56991912da 2025-07-17T07:01:51.9584177Z * [new tag] trunk/b7860c7863df9139bceabf5b0c186e6e4287aa4c -> trunk/b7860c7863df9139bceabf5b0c186e6e4287aa4c 2025-07-17T07:01:51.9585038Z * [new tag] trunk/b7a73a2cdbbe46e04aa145fb8c1615e608dcbde9 -> trunk/b7a73a2cdbbe46e04aa145fb8c1615e608dcbde9 2025-07-17T07:01:51.9585945Z * [new tag] trunk/b7b1109f49f5d0bd6145ae47c5c7d7d18c5659b0 -> trunk/b7b1109f49f5d0bd6145ae47c5c7d7d18c5659b0 2025-07-17T07:01:51.9587043Z * [new tag] trunk/b7def5ff1ca72fbb06350ffc75117efc68e149fb -> trunk/b7def5ff1ca72fbb06350ffc75117efc68e149fb 2025-07-17T07:01:51.9587833Z * [new tag] trunk/b83d8827bcd63501d7298267d94d103bf591c6c2 -> trunk/b83d8827bcd63501d7298267d94d103bf591c6c2 2025-07-17T07:01:51.9588736Z * [new tag] trunk/b85f10ea5006e8ae8fc769f48659ab7ad5eafb69 -> trunk/b85f10ea5006e8ae8fc769f48659ab7ad5eafb69 2025-07-17T07:01:51.9589621Z * [new tag] trunk/b86d5cef68d56f3924dc199424e65904a32d0743 -> trunk/b86d5cef68d56f3924dc199424e65904a32d0743 2025-07-17T07:01:51.9590838Z * [new tag] trunk/b878ca0c91bdccbdd907c15a01e5dcf249f0783c -> trunk/b878ca0c91bdccbdd907c15a01e5dcf249f0783c 2025-07-17T07:01:51.9591715Z * [new tag] trunk/b8ace6f95105751d0535cb0ce0d2c8f656c8b96c -> trunk/b8ace6f95105751d0535cb0ce0d2c8f656c8b96c 2025-07-17T07:01:51.9592748Z * [new tag] trunk/b8aee84fb9692f1a46c8c2a0ae6685df440f6e8f -> trunk/b8aee84fb9692f1a46c8c2a0ae6685df440f6e8f 2025-07-17T07:01:51.9593556Z * [new tag] trunk/b8bc2c2660e84034ff15232e2161e3ef9a6656d0 -> trunk/b8bc2c2660e84034ff15232e2161e3ef9a6656d0 2025-07-17T07:01:51.9594486Z * [new tag] trunk/b8c2d4c259b16f3dc044d049d964c44907ccecb6 -> trunk/b8c2d4c259b16f3dc044d049d964c44907ccecb6 2025-07-17T07:01:51.9595365Z * [new tag] trunk/b8d96c3f78a27e193f4fa9580f8d28298c8180e3 -> trunk/b8d96c3f78a27e193f4fa9580f8d28298c8180e3 2025-07-17T07:01:51.9596243Z * [new tag] trunk/b8fc5e0c0dda5da8315f9bc108a83a0f92252513 -> trunk/b8fc5e0c0dda5da8315f9bc108a83a0f92252513 2025-07-17T07:01:51.9597175Z * [new tag] trunk/b916d8a583cf4fb018c44eaf5efefa9ca76da366 -> trunk/b916d8a583cf4fb018c44eaf5efefa9ca76da366 2025-07-17T07:01:51.9598072Z * [new tag] trunk/b95dadd7170626273bb03b1264f04f3f051908da -> trunk/b95dadd7170626273bb03b1264f04f3f051908da 2025-07-17T07:01:51.9598979Z * [new tag] trunk/b981fb6744d034a0b2a0baf7e138cfb8dc83ca96 -> trunk/b981fb6744d034a0b2a0baf7e138cfb8dc83ca96 2025-07-17T07:01:51.9599827Z * [new tag] trunk/b9afdd9bcc738697c6eefc90899508ab783bf6ab -> trunk/b9afdd9bcc738697c6eefc90899508ab783bf6ab 2025-07-17T07:01:51.9600989Z * [new tag] trunk/b9b84d8011b08ac62cabf9100043c65863372fea -> trunk/b9b84d8011b08ac62cabf9100043c65863372fea 2025-07-17T07:01:51.9601917Z * [new tag] trunk/b9dc2fa4f7aa237a19248705abf82f5eae662182 -> trunk/b9dc2fa4f7aa237a19248705abf82f5eae662182 2025-07-17T07:01:51.9602990Z * [new tag] trunk/ba0d0de5e652650f8a59c85fc25a90f4ed9e2dc1 -> trunk/ba0d0de5e652650f8a59c85fc25a90f4ed9e2dc1 2025-07-17T07:01:51.9603841Z * [new tag] trunk/bb1f3d1a55e3f8eafb279995824c1ef87f24b341 -> trunk/bb1f3d1a55e3f8eafb279995824c1ef87f24b341 2025-07-17T07:01:51.9605017Z * [new tag] trunk/bb3c911c2d64ccad03349e50a0a220a2889ae85c -> trunk/bb3c911c2d64ccad03349e50a0a220a2889ae85c 2025-07-17T07:01:51.9605799Z * [new tag] trunk/bb462a6237c163774c99e01462703ebce55f4589 -> trunk/bb462a6237c163774c99e01462703ebce55f4589 2025-07-17T07:01:51.9606704Z * [new tag] trunk/bb476310a456b1fd418c79573ed34ad68b4871d4 -> trunk/bb476310a456b1fd418c79573ed34ad68b4871d4 2025-07-17T07:01:51.9607703Z * [new tag] trunk/bbb930aba2e769e3ed981f2a035133891de36dae -> trunk/bbb930aba2e769e3ed981f2a035133891de36dae 2025-07-17T07:01:51.9608640Z * [new tag] trunk/bbbced94a43cf764ddfe719e7d4c161a3992830c -> trunk/bbbced94a43cf764ddfe719e7d4c161a3992830c 2025-07-17T07:01:51.9609560Z * [new tag] trunk/bbe681ed510227ce05f99db2a36c1af5c2cc302b -> trunk/bbe681ed510227ce05f99db2a36c1af5c2cc302b 2025-07-17T07:01:51.9610502Z * [new tag] trunk/bbf1a6feac0e88772be103a1b159b871b5c00b4a -> trunk/bbf1a6feac0e88772be103a1b159b871b5c00b4a 2025-07-17T07:01:51.9611494Z * [new tag] trunk/bc3972b80a7abe85036f48b610532fce39ea5097 -> trunk/bc3972b80a7abe85036f48b610532fce39ea5097 2025-07-17T07:01:51.9612365Z * [new tag] trunk/bc5a11b58180d40157175f45c69d60a9b9961315 -> trunk/bc5a11b58180d40157175f45c69d60a9b9961315 2025-07-17T07:01:51.9613314Z * [new tag] trunk/bc65253369933160a2da3fc786d027a572faf6b7 -> trunk/bc65253369933160a2da3fc786d027a572faf6b7 2025-07-17T07:01:51.9614222Z * [new tag] trunk/bc6e0661a6ec7e536bee60b9c929f71643bb6c89 -> trunk/bc6e0661a6ec7e536bee60b9c929f71643bb6c89 2025-07-17T07:01:51.9615408Z * [new tag] trunk/bc9091a524a1ebe4de16af4dd8f442db7d1cb138 -> trunk/bc9091a524a1ebe4de16af4dd8f442db7d1cb138 2025-07-17T07:01:51.9616317Z * [new tag] trunk/bc9bd2a766ae8cf2aba8f3d0153cd8ecd05b4465 -> trunk/bc9bd2a766ae8cf2aba8f3d0153cd8ecd05b4465 2025-07-17T07:01:51.9617289Z * [new tag] trunk/bcad962550f2bfd850684250a3c881c9f38ad601 -> trunk/bcad962550f2bfd850684250a3c881c9f38ad601 2025-07-17T07:01:51.9618177Z * [new tag] trunk/bcc98bb2a4dd11ac696082731f8980b72deb6750 -> trunk/bcc98bb2a4dd11ac696082731f8980b72deb6750 2025-07-17T07:01:51.9619110Z * [new tag] trunk/bccb8473fed94dbc6f1392d0c5b4a51150ee4a9a -> trunk/bccb8473fed94dbc6f1392d0c5b4a51150ee4a9a 2025-07-17T07:01:51.9620526Z * [new tag] trunk/bcf50636ba1b93a833267c645d887888df06e9ea -> trunk/bcf50636ba1b93a833267c645d887888df06e9ea 2025-07-17T07:01:51.9621359Z * [new tag] trunk/bd364c901d5b20500ec5cbe275e93c955809d900 -> trunk/bd364c901d5b20500ec5cbe275e93c955809d900 2025-07-17T07:01:51.9622274Z * [new tag] trunk/bd3c32916cf5f85ddb9dd3c36e7311adfa8808af -> trunk/bd3c32916cf5f85ddb9dd3c36e7311adfa8808af 2025-07-17T07:01:51.9623259Z * [new tag] trunk/bd6b5fddbf5fb0b603ab8a7428079d9a86cf532a -> trunk/bd6b5fddbf5fb0b603ab8a7428079d9a86cf532a 2025-07-17T07:01:51.9624148Z * [new tag] trunk/bdacf08b8682b9fbe3a8656a53b1f8b1cb007fd8 -> trunk/bdacf08b8682b9fbe3a8656a53b1f8b1cb007fd8 2025-07-17T07:01:51.9625325Z * [new tag] trunk/bdb1553b77bb28df580ea41d726417ac91028ec6 -> trunk/bdb1553b77bb28df580ea41d726417ac91028ec6 2025-07-17T07:01:51.9626140Z * [new tag] trunk/bdb78191662c01ab1263108febac29a8560337d0 -> trunk/bdb78191662c01ab1263108febac29a8560337d0 2025-07-17T07:01:51.9627090Z * [new tag] trunk/bdbf2792a8f15907f6bbf231f73dab0e8efe1c50 -> trunk/bdbf2792a8f15907f6bbf231f73dab0e8efe1c50 2025-07-17T07:01:51.9627961Z * [new tag] trunk/be124a61a4933603795644d068d2c0e5f444e766 -> trunk/be124a61a4933603795644d068d2c0e5f444e766 2025-07-17T07:01:51.9629070Z * [new tag] trunk/be2ab96347a1e7206a57f69b58263c4455ff8f76 -> trunk/be2ab96347a1e7206a57f69b58263c4455ff8f76 2025-07-17T07:01:51.9630076Z * [new tag] trunk/be2ad70cfa1360da5c23a04ff6ca3480fa02f278 -> trunk/be2ad70cfa1360da5c23a04ff6ca3480fa02f278 2025-07-17T07:01:51.9630990Z * [new tag] trunk/be2e43264d0bce254efabcc60c368d1418bde57f -> trunk/be2e43264d0bce254efabcc60c368d1418bde57f 2025-07-17T07:01:51.9632099Z * [new tag] trunk/be56a8d7ac0dc72f6946354d7ac5be9ece0f1c35 -> trunk/be56a8d7ac0dc72f6946354d7ac5be9ece0f1c35 2025-07-17T07:01:51.9632934Z * [new tag] trunk/beb52f5c0ac8efddeafa862f274bc247db989695 -> trunk/beb52f5c0ac8efddeafa862f274bc247db989695 2025-07-17T07:01:51.9634029Z * [new tag] trunk/bee93f9f0d16f4f563812bb5c16e862de15724c1 -> trunk/bee93f9f0d16f4f563812bb5c16e862de15724c1 2025-07-17T07:01:51.9634736Z * [new tag] trunk/beed033b6e6ac57c0b4a1f47eb436e115a52e41b -> trunk/beed033b6e6ac57c0b4a1f47eb436e115a52e41b 2025-07-17T07:01:51.9635653Z * [new tag] trunk/bf06190e21fdf539c13e1ec01271653d0729733a -> trunk/bf06190e21fdf539c13e1ec01271653d0729733a 2025-07-17T07:01:51.9636574Z * [new tag] trunk/bf1ebe0531e1b0390ea09d66ac78500d3c6d3a15 -> trunk/bf1ebe0531e1b0390ea09d66ac78500d3c6d3a15 2025-07-17T07:01:51.9637521Z * [new tag] trunk/bf50d715539acedcb31a6d8f787149eff6213fdb -> trunk/bf50d715539acedcb31a6d8f787149eff6213fdb 2025-07-17T07:01:51.9638396Z * [new tag] trunk/bf798a2f016ca0001750436150e7a2bdb2676d1a -> trunk/bf798a2f016ca0001750436150e7a2bdb2676d1a 2025-07-17T07:01:51.9639172Z * [new tag] trunk/bf7e290854b7f0ab3fb89251d0100821f1b3a70e -> trunk/bf7e290854b7f0ab3fb89251d0100821f1b3a70e 2025-07-17T07:01:51.9640634Z * [new tag] trunk/bf897b4cea2b7481499a81afa30bfc69a8e685c4 -> trunk/bf897b4cea2b7481499a81afa30bfc69a8e685c4 2025-07-17T07:01:51.9641495Z * [new tag] trunk/bfcababbcb95ec42046737dbcf61f8e258075ace -> trunk/bfcababbcb95ec42046737dbcf61f8e258075ace 2025-07-17T07:01:51.9642541Z * [new tag] trunk/bfccfa0b31221d5df0b263de5a41fb9f7c84b97d -> trunk/bfccfa0b31221d5df0b263de5a41fb9f7c84b97d 2025-07-17T07:01:51.9643214Z * [new tag] trunk/c038719731abdcd415a5e82c5f3826f2358229fc -> trunk/c038719731abdcd415a5e82c5f3826f2358229fc 2025-07-17T07:01:51.9644110Z * [new tag] trunk/c04a4e709410936c12cdc9db5aaf47164f74a367 -> trunk/c04a4e709410936c12cdc9db5aaf47164f74a367 2025-07-17T07:01:51.9645247Z * [new tag] trunk/c062550a3598d27c2d6572db7c0f4ff90a84cc84 -> trunk/c062550a3598d27c2d6572db7c0f4ff90a84cc84 2025-07-17T07:01:51.9646187Z * [new tag] trunk/c06c2569ee53c169c96f42b45bdb64f390d2f23b -> trunk/c06c2569ee53c169c96f42b45bdb64f390d2f23b 2025-07-17T07:01:51.9647101Z * [new tag] trunk/c09b05487877a3f8b239523b3806f5dd9fc82051 -> trunk/c09b05487877a3f8b239523b3806f5dd9fc82051 2025-07-17T07:01:51.9648205Z * [new tag] trunk/c09cf29d7d010c0547f2f6771921a0ee2ec4904b -> trunk/c09cf29d7d010c0547f2f6771921a0ee2ec4904b 2025-07-17T07:01:51.9649050Z * [new tag] trunk/c09eba877f9c16908b3a925ef694604c1c761b85 -> trunk/c09eba877f9c16908b3a925ef694604c1c761b85 2025-07-17T07:01:51.9650020Z * [new tag] trunk/c0e155a8d230b04f186d1b7540b01213be2b9fc6 -> trunk/c0e155a8d230b04f186d1b7540b01213be2b9fc6 2025-07-17T07:01:51.9651604Z * [new tag] trunk/c0ee01c2fb54e2ae924270f7fd53069915a61a50 -> trunk/c0ee01c2fb54e2ae924270f7fd53069915a61a50 2025-07-17T07:01:51.9652498Z * [new tag] trunk/c10339559de1464c2c5aeb16649c382eefcfb572 -> trunk/c10339559de1464c2c5aeb16649c382eefcfb572 2025-07-17T07:01:51.9653400Z * [new tag] trunk/c11888e7a6557ff64e24ca06e22946c116a7c1e4 -> trunk/c11888e7a6557ff64e24ca06e22946c116a7c1e4 2025-07-17T07:01:51.9654497Z * [new tag] trunk/c13e725edd8dd21406c629bf625f2d6c59ceedd1 -> trunk/c13e725edd8dd21406c629bf625f2d6c59ceedd1 2025-07-17T07:01:51.9655436Z * [new tag] trunk/c14110056f1b6e989850f20ae56d47fbc775e890 -> trunk/c14110056f1b6e989850f20ae56d47fbc775e890 2025-07-17T07:01:51.9656513Z * [new tag] trunk/c1446e1e9d2f5a1bae5fb3d58e58f16c15bb15c3 -> trunk/c1446e1e9d2f5a1bae5fb3d58e58f16c15bb15c3 2025-07-17T07:01:51.9657406Z * [new tag] trunk/c165b36a31585d4e061a84e1977428afd931b82d -> trunk/c165b36a31585d4e061a84e1977428afd931b82d 2025-07-17T07:01:51.9658182Z * [new tag] trunk/c174f3a6a55864cedb8f6d9014e9b8cadf91186b -> trunk/c174f3a6a55864cedb8f6d9014e9b8cadf91186b 2025-07-17T07:01:51.9659125Z * [new tag] trunk/c177abd217ecef5cc096d50adbf5990525714dd3 -> trunk/c177abd217ecef5cc096d50adbf5990525714dd3 2025-07-17T07:01:51.9660029Z * [new tag] trunk/c199a4d0fd03ab3f7e36c6133839cfaf34ba59da -> trunk/c199a4d0fd03ab3f7e36c6133839cfaf34ba59da 2025-07-17T07:01:51.9660945Z * [new tag] trunk/c1a629f76d0afc2ddb4a5c7c1925caa1d12b5a5e -> trunk/c1a629f76d0afc2ddb4a5c7c1925caa1d12b5a5e 2025-07-17T07:01:51.9661857Z * [new tag] trunk/c1ad4b8e7a16f54c35a3908b56ed7d9f95eef586 -> trunk/c1ad4b8e7a16f54c35a3908b56ed7d9f95eef586 2025-07-17T07:01:51.9662936Z * [new tag] trunk/c1ae768baa9598ea97f301999a227b34a2efdecf -> trunk/c1ae768baa9598ea97f301999a227b34a2efdecf 2025-07-17T07:01:51.9663922Z * [new tag] trunk/c1cbaca7fd9937c0a089d98aa69066daf35c898f -> trunk/c1cbaca7fd9937c0a089d98aa69066daf35c898f 2025-07-17T07:01:51.9664831Z * [new tag] trunk/c1f531f0b0e6faf443d90f8de2936e866c8c27c2 -> trunk/c1f531f0b0e6faf443d90f8de2936e866c8c27c2 2025-07-17T07:01:51.9665684Z * [new tag] trunk/c202a7329ad798da676762a8af2aa588f882d288 -> trunk/c202a7329ad798da676762a8af2aa588f882d288 2025-07-17T07:01:51.9666768Z * [new tag] trunk/c2185dc4a5626848df37cad214b73d5ae7dd4f17 -> trunk/c2185dc4a5626848df37cad214b73d5ae7dd4f17 2025-07-17T07:01:51.9667607Z * [new tag] trunk/c219dbd2fc70227ba543c98e9740a84723ba36da -> trunk/c219dbd2fc70227ba543c98e9740a84723ba36da 2025-07-17T07:01:51.9668557Z * [new tag] trunk/c2510fcd86152028c3e6cf483740b177a10ac9b9 -> trunk/c2510fcd86152028c3e6cf483740b177a10ac9b9 2025-07-17T07:01:51.9669450Z * [new tag] trunk/c26ce593d8737cab4b55483b62956a6fea9e4375 -> trunk/c26ce593d8737cab4b55483b62956a6fea9e4375 2025-07-17T07:01:51.9670359Z * [new tag] trunk/c27f83dd91ff3599b5157866e6be014c20b967a0 -> trunk/c27f83dd91ff3599b5157866e6be014c20b967a0 2025-07-17T07:01:51.9671255Z * [new tag] trunk/c28e74e45743feac10559c30dbf71cc35bc12ccb -> trunk/c28e74e45743feac10559c30dbf71cc35bc12ccb 2025-07-17T07:01:51.9672315Z * [new tag] trunk/c2beeadeb40e927c51bc5bcd409b3f28374a5190 -> trunk/c2beeadeb40e927c51bc5bcd409b3f28374a5190 2025-07-17T07:01:51.9677242Z * [new tag] trunk/c2d1b225e62bdf3adbba91891f53bc60315adaac -> trunk/c2d1b225e62bdf3adbba91891f53bc60315adaac 2025-07-17T07:01:51.9678111Z * [new tag] trunk/c2f4cc59a70bfd7d7e46e9b5596bae8e4ae2cf9c -> trunk/c2f4cc59a70bfd7d7e46e9b5596bae8e4ae2cf9c 2025-07-17T07:01:51.9679052Z * [new tag] trunk/c329a8f19cc94cb7f9d3fc814484cded138ee3ca -> trunk/c329a8f19cc94cb7f9d3fc814484cded138ee3ca 2025-07-17T07:01:51.9680009Z * [new tag] trunk/c37ddcaefbe9b877e1816ce97dedb8ad26d09450 -> trunk/c37ddcaefbe9b877e1816ce97dedb8ad26d09450 2025-07-17T07:01:51.9681108Z * [new tag] trunk/c3ecabf0593066750156989fc75acbeeaddf0791 -> trunk/c3ecabf0593066750156989fc75acbeeaddf0791 2025-07-17T07:01:51.9681971Z * [new tag] trunk/c48d0f4643b7a69ebe24069e932ce1465a31cdbe -> trunk/c48d0f4643b7a69ebe24069e932ce1465a31cdbe 2025-07-17T07:01:51.9682901Z * [new tag] trunk/c4b93e6579c8e2f1252067c4923cbb859c476fb4 -> trunk/c4b93e6579c8e2f1252067c4923cbb859c476fb4 2025-07-17T07:01:51.9683840Z * [new tag] trunk/c4cdcda754e2b73b030902dd06fb651c7a026510 -> trunk/c4cdcda754e2b73b030902dd06fb651c7a026510 2025-07-17T07:01:51.9684950Z * [new tag] trunk/c515385b0ac4a94deef652159e71fe0912615d14 -> trunk/c515385b0ac4a94deef652159e71fe0912615d14 2025-07-17T07:01:51.9686381Z * [new tag] trunk/c54778625ec45b94eb97f52da3ad6c2eb3d3c3c9 -> trunk/c54778625ec45b94eb97f52da3ad6c2eb3d3c3c9 2025-07-17T07:01:51.9687220Z * [new tag] trunk/c553c55be76daaf70d977a4ac7664b46cbcfa7c4 -> trunk/c553c55be76daaf70d977a4ac7664b46cbcfa7c4 2025-07-17T07:01:51.9688121Z * [new tag] trunk/c5589074e64b28afd737174ae1e9c2ba0b925e56 -> trunk/c5589074e64b28afd737174ae1e9c2ba0b925e56 2025-07-17T07:01:51.9689092Z * [new tag] trunk/c55eef79f8880e4b610a0ca6f6131e690dc948dd -> trunk/c55eef79f8880e4b610a0ca6f6131e690dc948dd 2025-07-17T07:01:51.9690040Z * [new tag] trunk/c5a4fe9c17bc20eb46b15fda554d17030bb6a5b9 -> trunk/c5a4fe9c17bc20eb46b15fda554d17030bb6a5b9 2025-07-17T07:01:51.9690941Z * [new tag] trunk/c5b46b5408e136a5a3060b11a96afa51bd009fd5 -> trunk/c5b46b5408e136a5a3060b11a96afa51bd009fd5 2025-07-17T07:01:51.9691887Z * [new tag] trunk/c5d00e150ad503510bf28adf09f69201aeb94f5c -> trunk/c5d00e150ad503510bf28adf09f69201aeb94f5c 2025-07-17T07:01:51.9692709Z * [new tag] trunk/c5d3e7a4ff460eed70b8443485a7e3568e87aee9 -> trunk/c5d3e7a4ff460eed70b8443485a7e3568e87aee9 2025-07-17T07:01:51.9693595Z * [new tag] trunk/c60327ba74f4db232a2832f2c7ca4b2db43a3132 -> trunk/c60327ba74f4db232a2832f2c7ca4b2db43a3132 2025-07-17T07:01:51.9694530Z * [new tag] trunk/c60d8188d23801d44343c96746ec2e7d2971c5d7 -> trunk/c60d8188d23801d44343c96746ec2e7d2971c5d7 2025-07-17T07:01:51.9695592Z * [new tag] trunk/c620d0b5c7e8679413d620624725471223ce8359 -> trunk/c620d0b5c7e8679413d620624725471223ce8359 2025-07-17T07:01:51.9696444Z * [new tag] trunk/c68af9af1b3652a8e25bd6d0ff8dae89f206a81a -> trunk/c68af9af1b3652a8e25bd6d0ff8dae89f206a81a 2025-07-17T07:01:51.9697616Z * [new tag] trunk/c6a27bae3656021720074520ac6dad9cebe83ff5 -> trunk/c6a27bae3656021720074520ac6dad9cebe83ff5 2025-07-17T07:01:51.9699686Z * [new tag] trunk/c6b4f98625bb6b22bb9a60112a6d58e684a97e1b -> trunk/c6b4f98625bb6b22bb9a60112a6d58e684a97e1b 2025-07-17T07:01:51.9700812Z * [new tag] trunk/c74fd35050a7241f0c439501ef735aa6cdde751f -> trunk/c74fd35050a7241f0c439501ef735aa6cdde751f 2025-07-17T07:01:51.9701700Z * [new tag] trunk/c75c732481915c25295a0ae6321d39aa314eaa93 -> trunk/c75c732481915c25295a0ae6321d39aa314eaa93 2025-07-17T07:01:51.9704591Z * [new tag] trunk/c78bbdf4102d2c13bf6aa1abe4352aa7bca401ca -> trunk/c78bbdf4102d2c13bf6aa1abe4352aa7bca401ca 2025-07-17T07:01:51.9705136Z * [new tag] trunk/c78fce9e79b79686b87f4007cbaec819bdd0223f -> trunk/c78fce9e79b79686b87f4007cbaec819bdd0223f 2025-07-17T07:01:51.9705556Z * [new tag] trunk/c79c7bbe615265b6b3d7df39d6d5a68afd7d6b2a -> trunk/c79c7bbe615265b6b3d7df39d6d5a68afd7d6b2a 2025-07-17T07:01:51.9706099Z * [new tag] trunk/c7b6c98d1097bec9dc92bde2fe324aa126a5daa2 -> trunk/c7b6c98d1097bec9dc92bde2fe324aa126a5daa2 2025-07-17T07:01:51.9706505Z * [new tag] trunk/c808af514d59e25ea4a880c1e1e07d3232984e5d -> trunk/c808af514d59e25ea4a880c1e1e07d3232984e5d 2025-07-17T07:01:51.9707088Z * [new tag] trunk/c811f41cf58238b49e9c2f0a9fa00dcc471b2e10 -> trunk/c811f41cf58238b49e9c2f0a9fa00dcc471b2e10 2025-07-17T07:01:51.9707975Z * [new tag] trunk/c82a174ceab79f77ba18405dd263eb03692608fd -> trunk/c82a174ceab79f77ba18405dd263eb03692608fd 2025-07-17T07:01:51.9708928Z * [new tag] trunk/c83041cac2e9ca537b210b74876dbcee5e08ae9a -> trunk/c83041cac2e9ca537b210b74876dbcee5e08ae9a 2025-07-17T07:01:51.9709933Z * [new tag] trunk/c843909d9e32f92b2e31cf9b8f066daf311a6f18 -> trunk/c843909d9e32f92b2e31cf9b8f066daf311a6f18 2025-07-17T07:01:51.9710860Z * [new tag] trunk/c88e87f3553b7ef0b743fb030317fabb6fe2dc29 -> trunk/c88e87f3553b7ef0b743fb030317fabb6fe2dc29 2025-07-17T07:01:51.9711757Z * [new tag] trunk/c8c221c0b3abbb8b5e20138080644dd5f5cd0aa1 -> trunk/c8c221c0b3abbb8b5e20138080644dd5f5cd0aa1 2025-07-17T07:01:51.9712674Z * [new tag] trunk/c8c892b4a50fdad624753db5b8d2bbb0fc5bb110 -> trunk/c8c892b4a50fdad624753db5b8d2bbb0fc5bb110 2025-07-17T07:01:51.9713539Z * [new tag] trunk/c8d39a10457ea5d65184c6e8f037f46c5525d869 -> trunk/c8d39a10457ea5d65184c6e8f037f46c5525d869 2025-07-17T07:01:51.9714556Z * [new tag] trunk/c9174a20f75ba007b0f520326ced5c65d8a2b269 -> trunk/c9174a20f75ba007b0f520326ced5c65d8a2b269 2025-07-17T07:01:51.9715313Z * [new tag] trunk/c92f1075aaf3649f6368af2a3df9b5167f941b3f -> trunk/c92f1075aaf3649f6368af2a3df9b5167f941b3f 2025-07-17T07:01:51.9716260Z * [new tag] trunk/c9404faacb7c18af17086be26587b46e141afbcc -> trunk/c9404faacb7c18af17086be26587b46e141afbcc 2025-07-17T07:01:51.9717140Z * [new tag] trunk/c95705dac2da8134c946cdd573944632ef22f230 -> trunk/c95705dac2da8134c946cdd573944632ef22f230 2025-07-17T07:01:51.9718045Z * [new tag] trunk/c95f7fa874a3116f1067f9092456ee7281003614 -> trunk/c95f7fa874a3116f1067f9092456ee7281003614 2025-07-17T07:01:51.9719030Z * [new tag] trunk/c9a5bf09baa1a6776555e901331b1baefe6691bc -> trunk/c9a5bf09baa1a6776555e901331b1baefe6691bc 2025-07-17T07:01:51.9720030Z * [new tag] trunk/c9afcffed0870c211d2e8be422812d6a70aef3e2 -> trunk/c9afcffed0870c211d2e8be422812d6a70aef3e2 2025-07-17T07:01:51.9720931Z * [new tag] trunk/c9e9a0c82316ed59f6ef989a6000f0d5231b9b16 -> trunk/c9e9a0c82316ed59f6ef989a6000f0d5231b9b16 2025-07-17T07:01:51.9721900Z * [new tag] trunk/ca3cabd24ace1899ca4669431f5b0556c5ab9ebf -> trunk/ca3cabd24ace1899ca4669431f5b0556c5ab9ebf 2025-07-17T07:01:51.9722782Z * [new tag] trunk/ca5a40395d8b212dce0a57df01ca056eae55446f -> trunk/ca5a40395d8b212dce0a57df01ca056eae55446f 2025-07-17T07:01:51.9723792Z * [new tag] trunk/cadcb5d36802579441da3dbe3c2eecc74a0eae78 -> trunk/cadcb5d36802579441da3dbe3c2eecc74a0eae78 2025-07-17T07:01:51.9724602Z * [new tag] trunk/cb711c8fa04673d3490306e2b14539ab7dca3c23 -> trunk/cb711c8fa04673d3490306e2b14539ab7dca3c23 2025-07-17T07:01:51.9725622Z * [new tag] trunk/cb853945a77af54d9989ae7ac36d0007c0215b10 -> trunk/cb853945a77af54d9989ae7ac36d0007c0215b10 2025-07-17T07:01:51.9726436Z * [new tag] trunk/cb9b479f4f799eea058de9be3a0e09f977fc82da -> trunk/cb9b479f4f799eea058de9be3a0e09f977fc82da 2025-07-17T07:01:51.9727332Z * [new tag] trunk/cbafba57941c1d5f78640845320ac89157570b44 -> trunk/cbafba57941c1d5f78640845320ac89157570b44 2025-07-17T07:01:51.9728342Z * [new tag] trunk/cbcffce48a3422a5e9a2103888248c82d52d199c -> trunk/cbcffce48a3422a5e9a2103888248c82d52d199c 2025-07-17T07:01:51.9729230Z * [new tag] trunk/cc09d3a5ba2a5ae032078570e6123d523952ed22 -> trunk/cc09d3a5ba2a5ae032078570e6123d523952ed22 2025-07-17T07:01:51.9730175Z * [new tag] trunk/cc0faeb80fff17b3d170aa70041865aafb1790a9 -> trunk/cc0faeb80fff17b3d170aa70041865aafb1790a9 2025-07-17T07:01:51.9731058Z * [new tag] trunk/cc3ea2d84004b6348c77a285ba2639932b87c101 -> trunk/cc3ea2d84004b6348c77a285ba2639932b87c101 2025-07-17T07:01:51.9732010Z * [new tag] trunk/cc410d3761001499611bca6cf835239b86093791 -> trunk/cc410d3761001499611bca6cf835239b86093791 2025-07-17T07:01:51.9732953Z * [new tag] trunk/ccb1f687d63f0215ebcf3c1340c6682edeaccd28 -> trunk/ccb1f687d63f0215ebcf3c1340c6682edeaccd28 2025-07-17T07:01:51.9733719Z * [new tag] trunk/ccb67f39b4d75943cdc531d9032b3ba9235cfd56 -> trunk/ccb67f39b4d75943cdc531d9032b3ba9235cfd56 2025-07-17T07:01:51.9734902Z * [new tag] trunk/ccc6279b4086d55cd1f7e2d699473478610d8a7b -> trunk/ccc6279b4086d55cd1f7e2d699473478610d8a7b 2025-07-17T07:01:51.9735894Z * [new tag] trunk/cce4d213a67b7536dce431fd29c92f9791c9f81d -> trunk/cce4d213a67b7536dce431fd29c92f9791c9f81d 2025-07-17T07:01:51.9736858Z * [new tag] trunk/cd1a924dba145334aacb7c1b7276d6cef7ca41cc -> trunk/cd1a924dba145334aacb7c1b7276d6cef7ca41cc 2025-07-17T07:01:51.9737621Z * [new tag] trunk/cd361fc247a9abdbe9851867e31ac3cefcff299e -> trunk/cd361fc247a9abdbe9851867e31ac3cefcff299e 2025-07-17T07:01:51.9738596Z * [new tag] trunk/cd66ff80307862ef8e75520054ecd19a5eff9f7e -> trunk/cd66ff80307862ef8e75520054ecd19a5eff9f7e 2025-07-17T07:01:51.9739527Z * [new tag] trunk/cd75cf3cabedeac2ae4bbb266c05ca9b64181abe -> trunk/cd75cf3cabedeac2ae4bbb266c05ca9b64181abe 2025-07-17T07:01:51.9740485Z * [new tag] trunk/cd80f9a4c35a63ef415dbf6a361e4520a9cabf7b -> trunk/cd80f9a4c35a63ef415dbf6a361e4520a9cabf7b 2025-07-17T07:01:51.9741375Z * [new tag] trunk/cd82096973985042ed2eaa84e69fbf0f067f9301 -> trunk/cd82096973985042ed2eaa84e69fbf0f067f9301 2025-07-17T07:01:51.9742341Z * [new tag] trunk/cd995bfb2aac8891465809be3ce29543bd524287 -> trunk/cd995bfb2aac8891465809be3ce29543bd524287 2025-07-17T07:01:51.9743288Z * [new tag] trunk/cdb144fcf0aa4300c6de7158cb18ff7503d48492 -> trunk/cdb144fcf0aa4300c6de7158cb18ff7503d48492 2025-07-17T07:01:51.9744133Z * [new tag] trunk/cdfa33a328f2d3fdaeb645fde186194ddc2e66c0 -> trunk/cdfa33a328f2d3fdaeb645fde186194ddc2e66c0 2025-07-17T07:01:51.9745016Z * [new tag] trunk/ce1a07570d95cdd1543b9b2d45b3899c59e6802f -> trunk/ce1a07570d95cdd1543b9b2d45b3899c59e6802f 2025-07-17T07:01:51.9745818Z * [new tag] trunk/ce3406817d50b3357fa644784cc84ff167ce40ce -> trunk/ce3406817d50b3357fa644784cc84ff167ce40ce 2025-07-17T07:01:51.9747248Z * [new tag] trunk/ce44877961af7c8ec618f525853ce7edf3efa4eb -> trunk/ce44877961af7c8ec618f525853ce7edf3efa4eb 2025-07-17T07:01:51.9748159Z * [new tag] trunk/ce6e0523f9911c605860c4f5f278c2dad893dde9 -> trunk/ce6e0523f9911c605860c4f5f278c2dad893dde9 2025-07-17T07:01:51.9749012Z * [new tag] trunk/ce73b0c53f1f215345dcfe3953871a4aaf170a0c -> trunk/ce73b0c53f1f215345dcfe3953871a4aaf170a0c 2025-07-17T07:01:51.9749910Z * [new tag] trunk/ce79056471737557dcc64378985cd2b036e7322c -> trunk/ce79056471737557dcc64378985cd2b036e7322c 2025-07-17T07:01:51.9750828Z * [new tag] trunk/ce8180a61de405c519e00ac48eab8ff765ea71e6 -> trunk/ce8180a61de405c519e00ac48eab8ff765ea71e6 2025-07-17T07:01:51.9751858Z * [new tag] trunk/ce97a5dcfa3cb10c7805ff5cb44abd6a16b4ae8b -> trunk/ce97a5dcfa3cb10c7805ff5cb44abd6a16b4ae8b 2025-07-17T07:01:51.9752754Z * [new tag] trunk/ce9ba071fd29013e72100dd97728d01c860720d9 -> trunk/ce9ba071fd29013e72100dd97728d01c860720d9 2025-07-17T07:01:51.9753657Z * [new tag] trunk/cec264c8c6d74d4fbf5ee31983737b8b5e316dd0 -> trunk/cec264c8c6d74d4fbf5ee31983737b8b5e316dd0 2025-07-17T07:01:51.9754569Z * [new tag] trunk/cec59b76ca606c3e5d34ac0d0f9e0e22b8cfe5bb -> trunk/cec59b76ca606c3e5d34ac0d0f9e0e22b8cfe5bb 2025-07-17T07:01:51.9755484Z * [new tag] trunk/cf0749c92fdfcb8a583ea15f351add88c4d33618 -> trunk/cf0749c92fdfcb8a583ea15f351add88c4d33618 2025-07-17T07:01:51.9756856Z * [new tag] trunk/cf3247b74aaeb956b3c2b31d05e965a0aca5a8b4 -> trunk/cf3247b74aaeb956b3c2b31d05e965a0aca5a8b4 2025-07-17T07:01:51.9757693Z * [new tag] trunk/cf90c9f8d1632777ec5f4b6ccaa14bc5bf259e9c -> trunk/cf90c9f8d1632777ec5f4b6ccaa14bc5bf259e9c 2025-07-17T07:01:51.9758718Z * [new tag] trunk/cf9878d7a20b11d761934ae7732b01644ae137d6 -> trunk/cf9878d7a20b11d761934ae7732b01644ae137d6 2025-07-17T07:01:51.9759695Z * [new tag] trunk/d061a02e6ecf3b62f409578c7d05a564264d1288 -> trunk/d061a02e6ecf3b62f409578c7d05a564264d1288 2025-07-17T07:01:51.9760680Z * [new tag] trunk/d06a406656bf0b4bc9af2d056558dc1630a6e5b9 -> trunk/d06a406656bf0b4bc9af2d056558dc1630a6e5b9 2025-07-17T07:01:51.9761570Z * [new tag] trunk/d083841c0e0d879e460aebb45daac086a240a7dd -> trunk/d083841c0e0d879e460aebb45daac086a240a7dd 2025-07-17T07:01:51.9762564Z * [new tag] trunk/d0a9629435aaceb5acbf31aad70f2109cb8a3ea2 -> trunk/d0a9629435aaceb5acbf31aad70f2109cb8a3ea2 2025-07-17T07:01:51.9763480Z * [new tag] trunk/d0cfa3e5bf662b72068e26e06a1a5c2e640ce8d9 -> trunk/d0cfa3e5bf662b72068e26e06a1a5c2e640ce8d9 2025-07-17T07:01:51.9764412Z * [new tag] trunk/d158e9ea82ce3440e2e9a1bbbecae53a79869404 -> trunk/d158e9ea82ce3440e2e9a1bbbecae53a79869404 2025-07-17T07:01:51.9765294Z * [new tag] trunk/d1947a87074c5db2568038878b1948ea3a33cc23 -> trunk/d1947a87074c5db2568038878b1948ea3a33cc23 2025-07-17T07:01:51.9766248Z * [new tag] trunk/d1b4e0fa9a5feb22fc6de1d36dc4c9dac685caed -> trunk/d1b4e0fa9a5feb22fc6de1d36dc4c9dac685caed 2025-07-17T07:01:51.9767133Z * [new tag] trunk/d1c924c68aaf650439c2193f74cfefc9f16076f1 -> trunk/d1c924c68aaf650439c2193f74cfefc9f16076f1 2025-07-17T07:01:51.9768080Z * [new tag] trunk/d26ca5de058dbcf56ac52bb43e84dd98df2ace97 -> trunk/d26ca5de058dbcf56ac52bb43e84dd98df2ace97 2025-07-17T07:01:51.9768954Z * [new tag] trunk/d27d36136ce35d5d6dc3faa818ba840ba61d4357 -> trunk/d27d36136ce35d5d6dc3faa818ba840ba61d4357 2025-07-17T07:01:51.9769746Z * [new tag] trunk/d283fc79b15844ac470b3429b4dac5661621ae5c -> trunk/d283fc79b15844ac470b3429b4dac5661621ae5c 2025-07-17T07:01:51.9770779Z * [new tag] trunk/d2a2bfcb5867d4920e0a45113861cde6f74857b9 -> trunk/d2a2bfcb5867d4920e0a45113861cde6f74857b9 2025-07-17T07:01:51.9771689Z * [new tag] trunk/d2f06d2b062c9d868a898044bc324547f34c4760 -> trunk/d2f06d2b062c9d868a898044bc324547f34c4760 2025-07-17T07:01:51.9772681Z * [new tag] trunk/d309cd1d502eab8e9cb536876bf5eed88634eb41 -> trunk/d309cd1d502eab8e9cb536876bf5eed88634eb41 2025-07-17T07:01:51.9773716Z * [new tag] trunk/d32deb664a1dfc0e36f0a8d670695046632ebf2a -> trunk/d32deb664a1dfc0e36f0a8d670695046632ebf2a 2025-07-17T07:01:51.9774542Z * [new tag] trunk/d3d64c6db090ee9be051202e6ac6fd4acd5d3e97 -> trunk/d3d64c6db090ee9be051202e6ac6fd4acd5d3e97 2025-07-17T07:01:51.9775617Z * [new tag] trunk/d3d655ad14ee4cd1c135ac57bbf75d5623fc9fa6 -> trunk/d3d655ad14ee4cd1c135ac57bbf75d5623fc9fa6 2025-07-17T07:01:51.9776877Z * [new tag] trunk/d3da03d6fa02aecd01ff41246def793b8fc630bf -> trunk/d3da03d6fa02aecd01ff41246def793b8fc630bf 2025-07-17T07:01:51.9777780Z * [new tag] trunk/d3efd732348f8a455ca23e7fcae043651e41f65e -> trunk/d3efd732348f8a455ca23e7fcae043651e41f65e 2025-07-17T07:01:51.9778674Z * [new tag] trunk/d40aaa42ee8d7283d8de02c68c5625d67acf668a -> trunk/d40aaa42ee8d7283d8de02c68c5625d67acf668a 2025-07-17T07:01:51.9779594Z * [new tag] trunk/d41f62b7a06c51e4a57df4d58e7a2d86d2faa875 -> trunk/d41f62b7a06c51e4a57df4d58e7a2d86d2faa875 2025-07-17T07:01:51.9780409Z * [new tag] trunk/d42c11819f003a848c782d6090f44bbca11c7d60 -> trunk/d42c11819f003a848c782d6090f44bbca11c7d60 2025-07-17T07:01:51.9781320Z * [new tag] trunk/d43c0bdf46d82806cff195a6d2bfa103ab46dc14 -> trunk/d43c0bdf46d82806cff195a6d2bfa103ab46dc14 2025-07-17T07:01:51.9782221Z * [new tag] trunk/d4ad28042993dd09c80775d7fc5756fb4d13fdd4 -> trunk/d4ad28042993dd09c80775d7fc5756fb4d13fdd4 2025-07-17T07:01:51.9783197Z * [new tag] trunk/d4d0ede6bacb4b3b33c0e4aa4cb0e79d34e697ec -> trunk/d4d0ede6bacb4b3b33c0e4aa4cb0e79d34e697ec 2025-07-17T07:01:51.9784078Z * [new tag] trunk/d512584718838876ae14b32bb53cd836a1ceccae -> trunk/d512584718838876ae14b32bb53cd836a1ceccae 2025-07-17T07:01:51.9784847Z * [new tag] trunk/d55dc00f8479699f454e91c779b53e2a16cffc21 -> trunk/d55dc00f8479699f454e91c779b53e2a16cffc21 2025-07-17T07:01:51.9785828Z * [new tag] trunk/d56f11a1f275f2dc037cd5099b70240745990693 -> trunk/d56f11a1f275f2dc037cd5099b70240745990693 2025-07-17T07:01:51.9786632Z * [new tag] trunk/d5781c8d21b3dca35715a093ba52c5698551ad9b -> trunk/d5781c8d21b3dca35715a093ba52c5698551ad9b 2025-07-17T07:01:51.9787567Z * [new tag] trunk/d58ed04d89c34c6930d0f28be351c53db407078f -> trunk/d58ed04d89c34c6930d0f28be351c53db407078f 2025-07-17T07:01:51.9788575Z * [new tag] trunk/d59ed21d0fdcbb34b5c4b121a3b49ae64d8da367 -> trunk/d59ed21d0fdcbb34b5c4b121a3b49ae64d8da367 2025-07-17T07:01:51.9789529Z * [new tag] trunk/d5a89178b05db3124f16c733e9b00f8c5c4ab038 -> trunk/d5a89178b05db3124f16c733e9b00f8c5c4ab038 2025-07-17T07:01:51.9790316Z * [new tag] trunk/d5b4a329607514b30944c7682efcf79cf8950ed3 -> trunk/d5b4a329607514b30944c7682efcf79cf8950ed3 2025-07-17T07:01:51.9791257Z * [new tag] trunk/d5d14ee823e70224a154884a5c2912643d648094 -> trunk/d5d14ee823e70224a154884a5c2912643d648094 2025-07-17T07:01:51.9792178Z * [new tag] trunk/d5e6f4209424b96237ea5468dd47899e1124dfcb -> trunk/d5e6f4209424b96237ea5468dd47899e1124dfcb 2025-07-17T07:01:51.9793523Z * [new tag] trunk/d6237721c074484ea5e72fc05614587886e57fd6 -> trunk/d6237721c074484ea5e72fc05614587886e57fd6 2025-07-17T07:01:51.9794358Z * [new tag] trunk/d632cf2cc9aac8ab0e03d1537982265e42be95e5 -> trunk/d632cf2cc9aac8ab0e03d1537982265e42be95e5 2025-07-17T07:01:51.9795287Z * [new tag] trunk/d66b4bcc3f89ac6b9d2ccfd4cb01b0aa85d4e3fd -> trunk/d66b4bcc3f89ac6b9d2ccfd4cb01b0aa85d4e3fd 2025-07-17T07:01:51.9796071Z * [new tag] trunk/d6ee5144cab20141c8e0cf48856a90a6517753f1 -> trunk/d6ee5144cab20141c8e0cf48856a90a6517753f1 2025-07-17T07:01:51.9797234Z * [new tag] trunk/d75d30eeb610b164e69d0678a2e2b2dea81eec0f -> trunk/d75d30eeb610b164e69d0678a2e2b2dea81eec0f 2025-07-17T07:01:51.9798019Z * [new tag] trunk/d76323d41742cbc05ec6857319b267d2c7ea8fd9 -> trunk/d76323d41742cbc05ec6857319b267d2c7ea8fd9 2025-07-17T07:01:51.9798852Z * [new tag] trunk/d79651571fb082a7a20631059563ecdf9d11b7e6 -> trunk/d79651571fb082a7a20631059563ecdf9d11b7e6 2025-07-17T07:01:51.9799772Z * [new tag] trunk/d797038ea9f3951c7602e8710f4866f73d7e7acd -> trunk/d797038ea9f3951c7602e8710f4866f73d7e7acd 2025-07-17T07:01:51.9800689Z * [new tag] trunk/d7e1b8b11d7430c7633dcad6f6596b5df8fa02f7 -> trunk/d7e1b8b11d7430c7633dcad6f6596b5df8fa02f7 2025-07-17T07:01:51.9801632Z * [new tag] trunk/d7e3c9ce8289f26704b140e68dac2849df750040 -> trunk/d7e3c9ce8289f26704b140e68dac2849df750040 2025-07-17T07:01:51.9802526Z * [new tag] trunk/d7e657da358d6d393e2105f94f1312fb969654a2 -> trunk/d7e657da358d6d393e2105f94f1312fb969654a2 2025-07-17T07:01:51.9803423Z * [new tag] trunk/d83ff89d3bf54ecc95d441411ecbf07831cc0e5a -> trunk/d83ff89d3bf54ecc95d441411ecbf07831cc0e5a 2025-07-17T07:01:51.9804354Z * [new tag] trunk/d846e213553621cb4581f1f36ac0023e528ddb65 -> trunk/d846e213553621cb4581f1f36ac0023e528ddb65 2025-07-17T07:01:51.9805301Z * [new tag] trunk/d84efde3f02bdc4780e5c9ec43f9c515c744e9be -> trunk/d84efde3f02bdc4780e5c9ec43f9c515c744e9be 2025-07-17T07:01:51.9806179Z * [new tag] trunk/d859e65826d511f8379db4a8c4109417e26e0772 -> trunk/d859e65826d511f8379db4a8c4109417e26e0772 2025-07-17T07:01:51.9807582Z * [new tag] trunk/d89f30ad45b9d4bfe5cf5ab441b53e849e55df7b -> trunk/d89f30ad45b9d4bfe5cf5ab441b53e849e55df7b 2025-07-17T07:01:51.9808473Z * [new tag] trunk/d8bb5ac260dd81f75c8add6eadd8ce11e62be1a3 -> trunk/d8bb5ac260dd81f75c8add6eadd8ce11e62be1a3 2025-07-17T07:01:51.9809478Z * [new tag] trunk/d9426a81d2ab54f809a3b32a6ab2e606073fe66f -> trunk/d9426a81d2ab54f809a3b32a6ab2e606073fe66f 2025-07-17T07:01:51.9810277Z * [new tag] trunk/d9577df312d477e8fa5b9d7bc61fb1f2c07b8e48 -> trunk/d9577df312d477e8fa5b9d7bc61fb1f2c07b8e48 2025-07-17T07:01:51.9811179Z * [new tag] trunk/d96dec84151dfdea1aa2bf568deebdc182214f45 -> trunk/d96dec84151dfdea1aa2bf568deebdc182214f45 2025-07-17T07:01:51.9812186Z * [new tag] trunk/d9799a2ee7adb9d15f7a2d47ee17fc39c588af60 -> trunk/d9799a2ee7adb9d15f7a2d47ee17fc39c588af60 2025-07-17T07:01:51.9812987Z * [new tag] trunk/d98fa4a1033079ae19591aff5c3c354617bff482 -> trunk/d98fa4a1033079ae19591aff5c3c354617bff482 2025-07-17T07:01:51.9813885Z * [new tag] trunk/d99cac28160428a8b006890abffaaa0754bd28e1 -> trunk/d99cac28160428a8b006890abffaaa0754bd28e1 2025-07-17T07:01:51.9814781Z * [new tag] trunk/d9b8369f3976e89eef96c903dfede746fb438f63 -> trunk/d9b8369f3976e89eef96c903dfede746fb438f63 2025-07-17T07:01:51.9815619Z * [new tag] trunk/da05b7fb94fa6382c43e165a525a76d8ae62cadd -> trunk/da05b7fb94fa6382c43e165a525a76d8ae62cadd 2025-07-17T07:01:51.9816600Z * [new tag] trunk/da1f337bc43ce2d720aca1349f9c09712e037382 -> trunk/da1f337bc43ce2d720aca1349f9c09712e037382 2025-07-17T07:01:51.9817502Z * [new tag] trunk/da1f8980dfd646f14dc33bf0bc70fcb4d09096b3 -> trunk/da1f8980dfd646f14dc33bf0bc70fcb4d09096b3 2025-07-17T07:01:51.9818500Z * [new tag] trunk/da4e7c77a12746b0806a63af58d7c913ed241f55 -> trunk/da4e7c77a12746b0806a63af58d7c913ed241f55 2025-07-17T07:01:51.9819392Z * [new tag] trunk/da50835bdebd43275e0be4b1362e1bb92b568e72 -> trunk/da50835bdebd43275e0be4b1362e1bb92b568e72 2025-07-17T07:01:51.9820292Z * [new tag] trunk/da910e603a764c1f935dd0409b4c18bd84b1f045 -> trunk/da910e603a764c1f935dd0409b4c18bd84b1f045 2025-07-17T07:01:51.9821254Z * [new tag] trunk/dabb55baff2292ddd7728882b4e2113b8cb9e23b -> trunk/dabb55baff2292ddd7728882b4e2113b8cb9e23b 2025-07-17T07:01:51.9822305Z * [new tag] trunk/db01f1032ff133dc7cc3097b76bfb1b73ff533ce -> trunk/db01f1032ff133dc7cc3097b76bfb1b73ff533ce 2025-07-17T07:01:51.9823143Z * [new tag] trunk/db188503cb705230664c94d8b3a85165a00d632d -> trunk/db188503cb705230664c94d8b3a85165a00d632d 2025-07-17T07:01:51.9824031Z * [new tag] trunk/db491825e05e8bed9c7ed5d9fdd6ce206ab3167c -> trunk/db491825e05e8bed9c7ed5d9fdd6ce206ab3167c 2025-07-17T07:01:51.9824941Z * [new tag] trunk/db5970c1a67968f3b76d204d75789021d4304337 -> trunk/db5970c1a67968f3b76d204d75789021d4304337 2025-07-17T07:01:51.9826184Z * [new tag] trunk/dbec08bc1cc9113c13652128a4ddcfcf2426a8c0 -> trunk/dbec08bc1cc9113c13652128a4ddcfcf2426a8c0 2025-07-17T07:01:51.9827026Z * [new tag] trunk/dbf7d421dabced2335d17c7d7e57c1770f2f12c0 -> trunk/dbf7d421dabced2335d17c7d7e57c1770f2f12c0 2025-07-17T07:01:51.9827927Z * [new tag] trunk/dc5e8f7999cccb51efcf0f5fe197a740a918c73d -> trunk/dc5e8f7999cccb51efcf0f5fe197a740a918c73d 2025-07-17T07:01:51.9828813Z * [new tag] trunk/dcb97cd519e4e4974bc3bd4960512b557216f3e6 -> trunk/dcb97cd519e4e4974bc3bd4960512b557216f3e6 2025-07-17T07:01:51.9829828Z * [new tag] trunk/dd1b6621bc04a37b60ac304d913a3c838b0b1e4d -> trunk/dd1b6621bc04a37b60ac304d913a3c838b0b1e4d 2025-07-17T07:01:51.9830749Z * [new tag] trunk/dd3e7170c20cade9a308900aa4df46ab7656d253 -> trunk/dd3e7170c20cade9a308900aa4df46ab7656d253 2025-07-17T07:01:51.9831726Z * [new tag] trunk/dd41a3907cde3ce19fb69bd79c4055233a1e28d3 -> trunk/dd41a3907cde3ce19fb69bd79c4055233a1e28d3 2025-07-17T07:01:51.9832654Z * [new tag] trunk/dd78d6e7eaecf0aa5317076a721bc719c96b41f8 -> trunk/dd78d6e7eaecf0aa5317076a721bc719c96b41f8 2025-07-17T07:01:51.9833388Z * [new tag] trunk/dd93883231226576220a8ec0fa266c9e22eae82c -> trunk/dd93883231226576220a8ec0fa266c9e22eae82c 2025-07-17T07:01:51.9834292Z * [new tag] trunk/ddf502c988133835a89959bef945bf9c5f06b428 -> trunk/ddf502c988133835a89959bef945bf9c5f06b428 2025-07-17T07:01:51.9835237Z * [new tag] trunk/de1930a4290a27f3f03dd1d3454fb6ef9d12f3ea -> trunk/de1930a4290a27f3f03dd1d3454fb6ef9d12f3ea 2025-07-17T07:01:51.9836283Z * [new tag] trunk/de45c5f673ce261e9a82c54280beeda36cff640e -> trunk/de45c5f673ce261e9a82c54280beeda36cff640e 2025-07-17T07:01:51.9837216Z * [new tag] trunk/dea4864ce08115a0cdc871b84dd3080567d8b5e4 -> trunk/dea4864ce08115a0cdc871b84dd3080567d8b5e4 2025-07-17T07:01:51.9838114Z * [new tag] trunk/df72078fe1339751e702c7511c23b4597d022dcc -> trunk/df72078fe1339751e702c7511c23b4597d022dcc 2025-07-17T07:01:51.9839028Z * [new tag] trunk/dfa2649434f539d7580d38f08890176e73d45158 -> trunk/dfa2649434f539d7580d38f08890176e73d45158 2025-07-17T07:01:51.9839991Z * [new tag] trunk/dfc31b3345d78b0a49d446dcc1957404606a3aa2 -> trunk/dfc31b3345d78b0a49d446dcc1957404606a3aa2 2025-07-17T07:01:51.9841022Z * [new tag] trunk/dfcda613b65499e4b593b5bbb64812d57194858e -> trunk/dfcda613b65499e4b593b5bbb64812d57194858e 2025-07-17T07:01:51.9841928Z * [new tag] trunk/dfdd636cfa06ac70c4aa7ad5ee5e854c46b25a8e -> trunk/dfdd636cfa06ac70c4aa7ad5ee5e854c46b25a8e 2025-07-17T07:01:51.9842878Z * [new tag] trunk/dfef1e44085bb156abc4aff0f34a0b82a4a337b8 -> trunk/dfef1e44085bb156abc4aff0f34a0b82a4a337b8 2025-07-17T07:01:51.9843791Z * [new tag] trunk/e01fde82131c7f0b4c122222694911ee6fab36ca -> trunk/e01fde82131c7f0b4c122222694911ee6fab36ca 2025-07-17T07:01:51.9844698Z * [new tag] trunk/e0447bb5f84dca38e7515d1b1fdea42c647e5acd -> trunk/e0447bb5f84dca38e7515d1b1fdea42c647e5acd 2025-07-17T07:01:51.9845588Z * [new tag] trunk/e071837594f26a3bbaeee11eaa74f3bfd9c998ce -> trunk/e071837594f26a3bbaeee11eaa74f3bfd9c998ce 2025-07-17T07:01:51.9846494Z * [new tag] trunk/e0850123355a919427be3cb02f40c9a9e144fc3c -> trunk/e0850123355a919427be3cb02f40c9a9e144fc3c 2025-07-17T07:01:51.9847464Z * [new tag] trunk/e0ab1b538a646bff0739830d74a8e6ea6bf1eddd -> trunk/e0ab1b538a646bff0739830d74a8e6ea6bf1eddd 2025-07-17T07:01:51.9848505Z * [new tag] trunk/e0ae4ecca87928a96c97fb60cbb36aca0fc182f2 -> trunk/e0ae4ecca87928a96c97fb60cbb36aca0fc182f2 2025-07-17T07:01:51.9849420Z * [new tag] trunk/e0fd48be7d526625cacfd093f3db13506ca9cc17 -> trunk/e0fd48be7d526625cacfd093f3db13506ca9cc17 2025-07-17T07:01:51.9850902Z * [new tag] trunk/e1180c7228ba8c8b16cabf78706d4a67ca189a6b -> trunk/e1180c7228ba8c8b16cabf78706d4a67ca189a6b 2025-07-17T07:01:51.9851902Z * [new tag] trunk/e124a0d88ca2aa04bfaca2dcabf5de6244048e45 -> trunk/e124a0d88ca2aa04bfaca2dcabf5de6244048e45 2025-07-17T07:01:51.9852684Z * [new tag] trunk/e12597090c482ebf1eff7739794667a92cbff657 -> trunk/e12597090c482ebf1eff7739794667a92cbff657 2025-07-17T07:01:51.9853705Z * [new tag] trunk/e15848669f84d3767bfca724a29a6a6dde3308b9 -> trunk/e15848669f84d3767bfca724a29a6a6dde3308b9 2025-07-17T07:01:51.9854608Z * [new tag] trunk/e15ea965a1e84029eb900b93f5776767a39fd91e -> trunk/e15ea965a1e84029eb900b93f5776767a39fd91e 2025-07-17T07:01:51.9855641Z * [new tag] trunk/e15f4248ad2797539f1bf965bd0a3500b8ed15ed -> trunk/e15f4248ad2797539f1bf965bd0a3500b8ed15ed 2025-07-17T07:01:51.9856490Z * [new tag] trunk/e1723098805fcbf3ab7f3d20750fb0665bf65004 -> trunk/e1723098805fcbf3ab7f3d20750fb0665bf65004 2025-07-17T07:01:51.9857538Z * [new tag] trunk/e1a20988f3724317a7ee79c1777d574a8282a122 -> trunk/e1a20988f3724317a7ee79c1777d574a8282a122 2025-07-17T07:01:51.9859002Z * [new tag] trunk/e1aee86646aa6d1b9cb9d34351e43936401c5efc -> trunk/e1aee86646aa6d1b9cb9d34351e43936401c5efc 2025-07-17T07:01:51.9859834Z * [new tag] trunk/e1db10e05aa720aef1989773adcf48f311bcf920 -> trunk/e1db10e05aa720aef1989773adcf48f311bcf920 2025-07-17T07:01:51.9860585Z * [new tag] trunk/e1f28fe17bc55389fc5a31bad588d22e5ecca722 -> trunk/e1f28fe17bc55389fc5a31bad588d22e5ecca722 2025-07-17T07:01:51.9861559Z * [new tag] trunk/e20784f228abca24ab263cf0f69534667365ecba -> trunk/e20784f228abca24ab263cf0f69534667365ecba 2025-07-17T07:01:51.9862507Z * [new tag] trunk/e2351f2dcf7d300460edb9d0c2eb0ea11cc547c5 -> trunk/e2351f2dcf7d300460edb9d0c2eb0ea11cc547c5 2025-07-17T07:01:51.9863372Z * [new tag] trunk/e25ce0f928bfdd545323fcc2f88209233309bbbc -> trunk/e25ce0f928bfdd545323fcc2f88209233309bbbc 2025-07-17T07:01:51.9864179Z * [new tag] trunk/e265b719bd67f7c0a2b9001daef442a70232dcc8 -> trunk/e265b719bd67f7c0a2b9001daef442a70232dcc8 2025-07-17T07:01:51.9865067Z * [new tag] trunk/e28925aa7566da3ffe4f13dd8d59a6767bf97b2e -> trunk/e28925aa7566da3ffe4f13dd8d59a6767bf97b2e 2025-07-17T07:01:51.9865967Z * [new tag] trunk/e290a4c645e00926f0bfa44488f9f7dbd7cb2d0b -> trunk/e290a4c645e00926f0bfa44488f9f7dbd7cb2d0b 2025-07-17T07:01:51.9866892Z * [new tag] trunk/e2c9d8d6414927ce754bbc40b767edf103cf16da -> trunk/e2c9d8d6414927ce754bbc40b767edf103cf16da 2025-07-17T07:01:51.9868187Z * [new tag] trunk/e2f64eedafc6f0dcad9256ac1f38c0359477679a -> trunk/e2f64eedafc6f0dcad9256ac1f38c0359477679a 2025-07-17T07:01:51.9868973Z * [new tag] trunk/e311886e3d57c83a88b97a084dd0b95d6d1537a8 -> trunk/e311886e3d57c83a88b97a084dd0b95d6d1537a8 2025-07-17T07:01:51.9869896Z * [new tag] trunk/e31f20529276356092b5c63c2920d5b17ca9f4ba -> trunk/e31f20529276356092b5c63c2920d5b17ca9f4ba 2025-07-17T07:01:51.9870861Z * [new tag] trunk/e323d46b617c1552cc0f017ad8ec80189eacf187 -> trunk/e323d46b617c1552cc0f017ad8ec80189eacf187 2025-07-17T07:01:51.9871742Z * [new tag] trunk/e3320965b48d87b7257997c35db722302d923ead -> trunk/e3320965b48d87b7257997c35db722302d923ead 2025-07-17T07:01:51.9873055Z * [new tag] trunk/e375d21bb9b0ef6fefe7a8af5a054a17de8c63c9 -> trunk/e375d21bb9b0ef6fefe7a8af5a054a17de8c63c9 2025-07-17T07:01:51.9873806Z * [new tag] trunk/e3977e843de6c9c43be00ee8c67c533debfc0dc9 -> trunk/e3977e843de6c9c43be00ee8c67c533debfc0dc9 2025-07-17T07:01:51.9874681Z * [new tag] trunk/e3afbb03623559fa3fa7ba607c07c86ebed889f0 -> trunk/e3afbb03623559fa3fa7ba607c07c86ebed889f0 2025-07-17T07:01:51.9875917Z * [new tag] trunk/e3b44edfd837199880aed3ec802383617da731f3 -> trunk/e3b44edfd837199880aed3ec802383617da731f3 2025-07-17T07:01:51.9876855Z * [new tag] trunk/e3f2597b4529927b812b443cf8da19b44ed8dea4 -> trunk/e3f2597b4529927b812b443cf8da19b44ed8dea4 2025-07-17T07:01:51.9877751Z * [new tag] trunk/e3f8141c25b7213885f0b7b64f2b0ccc7858be95 -> trunk/e3f8141c25b7213885f0b7b64f2b0ccc7858be95 2025-07-17T07:01:51.9878687Z * [new tag] trunk/e3fe001d9e38153a7797ad25377fdfa34e461113 -> trunk/e3fe001d9e38153a7797ad25377fdfa34e461113 2025-07-17T07:01:51.9879685Z * [new tag] trunk/e40ade5182233f548b25f2732effe3719d16e9ad -> trunk/e40ade5182233f548b25f2732effe3719d16e9ad 2025-07-17T07:01:51.9880635Z * [new tag] trunk/e466dab164d9236bfe5817ec8e4d24c7b9d3e392 -> trunk/e466dab164d9236bfe5817ec8e4d24c7b9d3e392 2025-07-17T07:01:51.9881959Z * [new tag] trunk/e472daa80963aae389089f9dc324b04261e2a5ef -> trunk/e472daa80963aae389089f9dc324b04261e2a5ef 2025-07-17T07:01:51.9882855Z * [new tag] trunk/e4ae60a413b99688d308794baaa1c685b5c19025 -> trunk/e4ae60a413b99688d308794baaa1c685b5c19025 2025-07-17T07:01:51.9883816Z * [new tag] trunk/e4c17d5e1ccd0e730caef484af291243bc1d9cde -> trunk/e4c17d5e1ccd0e730caef484af291243bc1d9cde 2025-07-17T07:01:51.9884794Z * [new tag] trunk/e4c9f6d9a286bd367ce3a157428f588e267d2630 -> trunk/e4c9f6d9a286bd367ce3a157428f588e267d2630 2025-07-17T07:01:51.9885690Z * [new tag] trunk/e517066f41342f0f01043121dcb8ac6b235ee40c -> trunk/e517066f41342f0f01043121dcb8ac6b235ee40c 2025-07-17T07:01:51.9886640Z * [new tag] trunk/e53ddaf1f6ef3f1d22075ad33a5922fb8b277fe0 -> trunk/e53ddaf1f6ef3f1d22075ad33a5922fb8b277fe0 2025-07-17T07:01:51.9887514Z * [new tag] trunk/e581f015eebf483eb6f19447caf2d2452a602c2f -> trunk/e581f015eebf483eb6f19447caf2d2452a602c2f 2025-07-17T07:01:51.9888422Z * [new tag] trunk/e583b888194c8c74ebbd332c09c394acebbbbcff -> trunk/e583b888194c8c74ebbd332c09c394acebbbbcff 2025-07-17T07:01:51.9889368Z * [new tag] trunk/e5a0b73ce9e7bc489150932b60f673155d6ddc30 -> trunk/e5a0b73ce9e7bc489150932b60f673155d6ddc30 2025-07-17T07:01:51.9890220Z * [new tag] trunk/e5a11971919009b5e5bd050b3ed52e324f1ce894 -> trunk/e5a11971919009b5e5bd050b3ed52e324f1ce894 2025-07-17T07:01:51.9891215Z * [new tag] trunk/e5ea24fb27e0ef5dbb0c4039d88c0f4faa221fef -> trunk/e5ea24fb27e0ef5dbb0c4039d88c0f4faa221fef 2025-07-17T07:01:51.9892115Z * [new tag] trunk/e5ed267f830ce9fc6579f928082e563fbed37e3f -> trunk/e5ed267f830ce9fc6579f928082e563fbed37e3f 2025-07-17T07:01:51.9893003Z * [new tag] trunk/e5edd013ab418b8b3609cb3cb1df3804b69d8eef -> trunk/e5edd013ab418b8b3609cb3cb1df3804b69d8eef 2025-07-17T07:01:51.9893913Z * [new tag] trunk/e5f6ffd8109bee99416d9b127f961ab0e253a1c2 -> trunk/e5f6ffd8109bee99416d9b127f961ab0e253a1c2 2025-07-17T07:01:51.9894826Z * [new tag] trunk/e600e044a770d29d1fe5d9638b274a7d4f22f969 -> trunk/e600e044a770d29d1fe5d9638b274a7d4f22f969 2025-07-17T07:01:51.9895805Z * [new tag] trunk/e6252f62efa5e6e4ab0d56967d7461f1e8a7eb7c -> trunk/e6252f62efa5e6e4ab0d56967d7461f1e8a7eb7c 2025-07-17T07:01:51.9896723Z * [new tag] trunk/e694280d1215caf70f41575f2611bfa26c69ebdb -> trunk/e694280d1215caf70f41575f2611bfa26c69ebdb 2025-07-17T07:01:51.9897676Z * [new tag] trunk/e6d71f37896e99ced7daf9ee43a30c0ec03d60f2 -> trunk/e6d71f37896e99ced7daf9ee43a30c0ec03d60f2 2025-07-17T07:01:51.9898940Z * [new tag] trunk/e6d8ed02cbeabe961d1a5303973a6d89ee851736 -> trunk/e6d8ed02cbeabe961d1a5303973a6d89ee851736 2025-07-17T07:01:51.9899720Z * [new tag] trunk/e6ed4074e8411000eea2f0de0c0829d909e4701f -> trunk/e6ed4074e8411000eea2f0de0c0829d909e4701f 2025-07-17T07:01:51.9900618Z * [new tag] trunk/e71bb021b9553ddc2db6cb8ea7bf8643552f09fc -> trunk/e71bb021b9553ddc2db6cb8ea7bf8643552f09fc 2025-07-17T07:01:51.9901615Z * [new tag] trunk/e7698ff5cf40729d11df6c32c6df0a163e5d0ce0 -> trunk/e7698ff5cf40729d11df6c32c6df0a163e5d0ce0 2025-07-17T07:01:51.9902565Z * [new tag] trunk/e78f2ac92b709a060aa323d6e527ec2ecc36fb93 -> trunk/e78f2ac92b709a060aa323d6e527ec2ecc36fb93 2025-07-17T07:01:51.9903604Z * [new tag] trunk/e7a66166ce5294a52bf3966aef49562f94343475 -> trunk/e7a66166ce5294a52bf3966aef49562f94343475 2025-07-17T07:01:51.9904567Z * [new tag] trunk/e7da21806fbc96d47ac03ea8f6507deb0ab48481 -> trunk/e7da21806fbc96d47ac03ea8f6507deb0ab48481 2025-07-17T07:01:51.9905484Z * [new tag] trunk/e8217ad8becd2b297682c685a9179997cb0a98cc -> trunk/e8217ad8becd2b297682c685a9179997cb0a98cc 2025-07-17T07:01:51.9906368Z * [new tag] trunk/e895e9689c625cbcd8f46880115e0d093713fa37 -> trunk/e895e9689c625cbcd8f46880115e0d093713fa37 2025-07-17T07:01:51.9907331Z * [new tag] trunk/e8b3dfa7c0b6d74fc84714dbb79cb6a8bad43ae7 -> trunk/e8b3dfa7c0b6d74fc84714dbb79cb6a8bad43ae7 2025-07-17T07:01:51.9908185Z * [new tag] trunk/e8bfce9a43960f417a36636a0e7f6a58b7dc56d2 -> trunk/e8bfce9a43960f417a36636a0e7f6a58b7dc56d2 2025-07-17T07:01:51.9909146Z * [new tag] trunk/e8cf5ff5641c1ce4c3046a85df55b15eaaea38b2 -> trunk/e8cf5ff5641c1ce4c3046a85df55b15eaaea38b2 2025-07-17T07:01:51.9910386Z * [new tag] trunk/e8d29c45e02402bcb4ae7726abd415bebd441852 -> trunk/e8d29c45e02402bcb4ae7726abd415bebd441852 2025-07-17T07:01:51.9911045Z * [new tag] trunk/e90148c91d3b8a759db558374c8539e47338926b -> trunk/e90148c91d3b8a759db558374c8539e47338926b 2025-07-17T07:01:51.9912276Z * [new tag] trunk/e92e3eaf4eb815ea28db9a5af9d9ee48c3f7be3f -> trunk/e92e3eaf4eb815ea28db9a5af9d9ee48c3f7be3f 2025-07-17T07:01:51.9913084Z * [new tag] trunk/e9367a7a4288e626f01fada3912d68756f1ca6d3 -> trunk/e9367a7a4288e626f01fada3912d68756f1ca6d3 2025-07-17T07:01:51.9914069Z * [new tag] trunk/e959dd017d7dcd7c6351c9ef303f3667aebe11f5 -> trunk/e959dd017d7dcd7c6351c9ef303f3667aebe11f5 2025-07-17T07:01:51.9914956Z * [new tag] trunk/e95e8eed0a96f024dc84012494f465b34eae9d22 -> trunk/e95e8eed0a96f024dc84012494f465b34eae9d22 2025-07-17T07:01:51.9915878Z * [new tag] trunk/e96f530af5bf8ceaeaa87ec7f4651490a25bce07 -> trunk/e96f530af5bf8ceaeaa87ec7f4651490a25bce07 2025-07-17T07:01:51.9916783Z * [new tag] trunk/e98dd95446e009ace1722498effbf32250d623e4 -> trunk/e98dd95446e009ace1722498effbf32250d623e4 2025-07-17T07:01:51.9917729Z * [new tag] trunk/e99a2a2dba90c9b6276219104bc99aff2f67e2f7 -> trunk/e99a2a2dba90c9b6276219104bc99aff2f67e2f7 2025-07-17T07:01:51.9918649Z * [new tag] trunk/e99cc126a4f4bd5233ff363f27d14a0fff4b4392 -> trunk/e99cc126a4f4bd5233ff363f27d14a0fff4b4392 2025-07-17T07:01:51.9919591Z * [new tag] trunk/e9fdaf8701b599fd943bb899639b5e8a4966b3c3 -> trunk/e9fdaf8701b599fd943bb899639b5e8a4966b3c3 2025-07-17T07:01:51.9920520Z * [new tag] trunk/ea23eb4b98ae8af40f0c38538a9e63f1b0dc40e2 -> trunk/ea23eb4b98ae8af40f0c38538a9e63f1b0dc40e2 2025-07-17T07:01:51.9921306Z * [new tag] trunk/ea37f720995616d8570045c63486198a021f8285 -> trunk/ea37f720995616d8570045c63486198a021f8285 2025-07-17T07:01:51.9922177Z * [new tag] trunk/ea74fdd24aa7d98433231f4a3d75cfd241d8720e -> trunk/ea74fdd24aa7d98433231f4a3d75cfd241d8720e 2025-07-17T07:01:51.9923122Z * [new tag] trunk/eab45643f22e58ee12d95d8b0162d51ca0a50801 -> trunk/eab45643f22e58ee12d95d8b0162d51ca0a50801 2025-07-17T07:01:51.9923948Z * [new tag] trunk/eabf7cd3c552e6c17321a63d7e5a19da92909910 -> trunk/eabf7cd3c552e6c17321a63d7e5a19da92909910 2025-07-17T07:01:51.9924868Z * [new tag] trunk/eaceb243df6fc17a37a2c1d9f8775d1561a0f67c -> trunk/eaceb243df6fc17a37a2c1d9f8775d1561a0f67c 2025-07-17T07:01:51.9925758Z * [new tag] trunk/eaf32fffb7e83b5331951b254ad213ceb2ae72f8 -> trunk/eaf32fffb7e83b5331951b254ad213ceb2ae72f8 2025-07-17T07:01:51.9926759Z * [new tag] trunk/eaf704914eb63efffafeb402633f76f2cd521ef4 -> trunk/eaf704914eb63efffafeb402633f76f2cd521ef4 2025-07-17T07:01:51.9927701Z * [new tag] trunk/eb152ab1dd9eafc777b6642bb141ab1b8376359a -> trunk/eb152ab1dd9eafc777b6642bb141ab1b8376359a 2025-07-17T07:01:51.9929057Z * [new tag] trunk/eb2af14f8e75c22c0a2fcf407005d2e8173d0df5 -> trunk/eb2af14f8e75c22c0a2fcf407005d2e8173d0df5 2025-07-17T07:01:51.9929922Z * [new tag] trunk/eb331b59fedb7f70229ca491029f2f7f27bbff54 -> trunk/eb331b59fedb7f70229ca491029f2f7f27bbff54 2025-07-17T07:01:51.9930831Z * [new tag] trunk/eb4cf59ecdf69dd53805a37525a7f750c8f1e0dd -> trunk/eb4cf59ecdf69dd53805a37525a7f750c8f1e0dd 2025-07-17T07:01:51.9931780Z * [new tag] trunk/eb9efb37c8f315f1d30e86d5797490c6a8666889 -> trunk/eb9efb37c8f315f1d30e86d5797490c6a8666889 2025-07-17T07:01:51.9932718Z * [new tag] trunk/eba5fc91ac924a7a05569c33b0f90f0faf22696d -> trunk/eba5fc91ac924a7a05569c33b0f90f0faf22696d 2025-07-17T07:01:51.9933619Z * [new tag] trunk/ebab2799423987a16975ab07ee49dd944e5315e6 -> trunk/ebab2799423987a16975ab07ee49dd944e5315e6 2025-07-17T07:01:51.9934497Z * [new tag] trunk/ebf83b8b7772632c0558db9a88281ee10ff2df38 -> trunk/ebf83b8b7772632c0558db9a88281ee10ff2df38 2025-07-17T07:01:51.9935497Z * [new tag] trunk/ec0276103fc023b93c6eb17639d4605656ea4f10 -> trunk/ec0276103fc023b93c6eb17639d4605656ea4f10 2025-07-17T07:01:51.9936469Z * [new tag] trunk/ec08eb8ba22e66b113e4f2aba1f6afb738f9d861 -> trunk/ec08eb8ba22e66b113e4f2aba1f6afb738f9d861 2025-07-17T07:01:51.9937220Z * [new tag] trunk/ec816d73b4c8f74a14e275891939de6cfd6891b6 -> trunk/ec816d73b4c8f74a14e275891939de6cfd6891b6 2025-07-17T07:01:51.9938165Z * [new tag] trunk/ecd73c58eeaf7e919316f9b9596f8c677af96c66 -> trunk/ecd73c58eeaf7e919316f9b9596f8c677af96c66 2025-07-17T07:01:51.9939119Z * [new tag] trunk/ed03492238c9ef76f5c1df6cfa54da83a2a384e1 -> trunk/ed03492238c9ef76f5c1df6cfa54da83a2a384e1 2025-07-17T07:01:51.9939982Z * [new tag] trunk/ed051c308464904e07d33c39524b3931dc8c947a -> trunk/ed051c308464904e07d33c39524b3931dc8c947a 2025-07-17T07:01:51.9941123Z * [new tag] trunk/ed508cc0182b923789555d4e1ec1f8b23707b7db -> trunk/ed508cc0182b923789555d4e1ec1f8b23707b7db 2025-07-17T07:01:51.9942006Z * [new tag] trunk/ed5d6d2a200a0f03b164053322341bb10acc051d -> trunk/ed5d6d2a200a0f03b164053322341bb10acc051d 2025-07-17T07:01:51.9942778Z * [new tag] trunk/ed661a5f11de116539f70882f4f59a61d5e3e209 -> trunk/ed661a5f11de116539f70882f4f59a61d5e3e209 2025-07-17T07:01:51.9943720Z * [new tag] trunk/ed6ae20cf0e31d49d54177251293267205e24021 -> trunk/ed6ae20cf0e31d49d54177251293267205e24021 2025-07-17T07:01:51.9944643Z * [new tag] trunk/ed6df0e324233fda094e3c1cb4de4009d1ba2413 -> trunk/ed6df0e324233fda094e3c1cb4de4009d1ba2413 2025-07-17T07:01:51.9945845Z * [new tag] trunk/ed911747c2a56cf84d648d677403d5bf6b2ebd0b -> trunk/ed911747c2a56cf84d648d677403d5bf6b2ebd0b 2025-07-17T07:01:51.9946776Z * [new tag] trunk/eda0a9cc90b9a63127a49d617329f98b6404e90d -> trunk/eda0a9cc90b9a63127a49d617329f98b6404e90d 2025-07-17T07:01:51.9947958Z * [new tag] trunk/edb92e16ba9ad12bd51c4f73389556e163e9cc08 -> trunk/edb92e16ba9ad12bd51c4f73389556e163e9cc08 2025-07-17T07:01:51.9948854Z * [new tag] trunk/edd45f3a020f892c17672cc2d08f64cb960006ad -> trunk/edd45f3a020f892c17672cc2d08f64cb960006ad 2025-07-17T07:01:51.9949784Z * [new tag] trunk/edd9c09e73aaa8d1a1667fa7ea5a337aa31fa6ec -> trunk/edd9c09e73aaa8d1a1667fa7ea5a337aa31fa6ec 2025-07-17T07:01:51.9950731Z * [new tag] trunk/eddddea9087a8840ce5ae1bbb3c6f59556d7c0f0 -> trunk/eddddea9087a8840ce5ae1bbb3c6f59556d7c0f0 2025-07-17T07:01:51.9951595Z * [new tag] trunk/ede6ead8cd8e925cb093f2b3016342e645bd728d -> trunk/ede6ead8cd8e925cb093f2b3016342e645bd728d 2025-07-17T07:01:51.9952383Z * [new tag] trunk/edf7bb4f514220f96ddfa646ae6e9e930a305ec1 -> trunk/edf7bb4f514220f96ddfa646ae6e9e930a305ec1 2025-07-17T07:01:51.9953124Z * [new tag] trunk/ee0992871c99fc6a1e19eb839ab65391a168d2f8 -> trunk/ee0992871c99fc6a1e19eb839ab65391a168d2f8 2025-07-17T07:01:51.9954084Z * [new tag] trunk/ee4d343499c80be16a58d5ac604da6e2130cd94d -> trunk/ee4d343499c80be16a58d5ac604da6e2130cd94d 2025-07-17T07:01:51.9955076Z * [new tag] trunk/ee56e9f8a8202bfa02c2d9ae3cfa07d4a41ab567 -> trunk/ee56e9f8a8202bfa02c2d9ae3cfa07d4a41ab567 2025-07-17T07:01:51.9956058Z * [new tag] trunk/ee5c2908cbfcbda6e8a5737d9939d36ba417d627 -> trunk/ee5c2908cbfcbda6e8a5737d9939d36ba417d627 2025-07-17T07:01:51.9956946Z * [new tag] trunk/ee9ac36c2316f430c4de2a0982bde3cb8087cbe7 -> trunk/ee9ac36c2316f430c4de2a0982bde3cb8087cbe7 2025-07-17T07:01:51.9957863Z * [new tag] trunk/eea3bcb3d146f16a8f8c91a9e2a8f1b76c5917f4 -> trunk/eea3bcb3d146f16a8f8c91a9e2a8f1b76c5917f4 2025-07-17T07:01:51.9959028Z * [new tag] trunk/eeaefa133694811aac1fd215bcae2e7f1e098d1d -> trunk/eeaefa133694811aac1fd215bcae2e7f1e098d1d 2025-07-17T07:01:51.9959895Z * [new tag] trunk/eecaa0bbc6a39c89366f9ec97797479c27f6d760 -> trunk/eecaa0bbc6a39c89366f9ec97797479c27f6d760 2025-07-17T07:01:51.9961075Z * [new tag] trunk/eef253d9f6fc8b9774d199889a817f50e89572c2 -> trunk/eef253d9f6fc8b9774d199889a817f50e89572c2 2025-07-17T07:01:51.9962066Z * [new tag] trunk/ef4cca2d79eba61441da46906b30f8f6165cc455 -> trunk/ef4cca2d79eba61441da46906b30f8f6165cc455 2025-07-17T07:01:51.9963007Z * [new tag] trunk/ef6d2cee7a93c1e8f52a7595b6f630d0fcd83692 -> trunk/ef6d2cee7a93c1e8f52a7595b6f630d0fcd83692 2025-07-17T07:01:51.9963956Z * [new tag] trunk/ef6dfa06a9ff84bf04b0d83cfab4cb396a508a0e -> trunk/ef6dfa06a9ff84bf04b0d83cfab4cb396a508a0e 2025-07-17T07:01:51.9964822Z * [new tag] trunk/ef97bd47131423e0819b293dc227b62d0c376023 -> trunk/ef97bd47131423e0819b293dc227b62d0c376023 2025-07-17T07:01:51.9965786Z * [new tag] trunk/efbf07e7ea46c33c936cac882eb1c8b782d1d070 -> trunk/efbf07e7ea46c33c936cac882eb1c8b782d1d070 2025-07-17T07:01:51.9966712Z * [new tag] trunk/effe376db07c7071fa4d704dd0787a394a5eda50 -> trunk/effe376db07c7071fa4d704dd0787a394a5eda50 2025-07-17T07:01:51.9967631Z * [new tag] trunk/f04fd4dc4eaf16557f9212240bdaa8377d51bad1 -> trunk/f04fd4dc4eaf16557f9212240bdaa8377d51bad1 2025-07-17T07:01:51.9968521Z * [new tag] trunk/f096820d0f845f36529fe774cf761d5fc4ad02a8 -> trunk/f096820d0f845f36529fe774cf761d5fc4ad02a8 2025-07-17T07:01:51.9969420Z * [new tag] trunk/f0b388665efd20c1ec35fc48afb115a0f15116ce -> trunk/f0b388665efd20c1ec35fc48afb115a0f15116ce 2025-07-17T07:01:51.9970518Z * [new tag] trunk/f0bee87eea03f6ded538590e2a073cc62ecd2818 -> trunk/f0bee87eea03f6ded538590e2a073cc62ecd2818 2025-07-17T07:01:51.9971506Z * [new tag] trunk/f1331f3f1b43d1848341a0f0da66a13cb05570d0 -> trunk/f1331f3f1b43d1848341a0f0da66a13cb05570d0 2025-07-17T07:01:51.9972755Z * [new tag] trunk/f140fac8dcfa6ec20d594bc4637417a83bc90036 -> trunk/f140fac8dcfa6ec20d594bc4637417a83bc90036 2025-07-17T07:01:51.9973664Z * [new tag] trunk/f151b201236f959e3874b73dde9bfae5e10dae78 -> trunk/f151b201236f959e3874b73dde9bfae5e10dae78 2025-07-17T07:01:51.9974547Z * [new tag] trunk/f154f9b3040369a7979d5de7acb6fe21433eda83 -> trunk/f154f9b3040369a7979d5de7acb6fe21433eda83 2025-07-17T07:01:51.9976220Z * [new tag] trunk/f16053f0c9a09fa337fbf85aaf64f88712b8dcdb -> trunk/f16053f0c9a09fa337fbf85aaf64f88712b8dcdb 2025-07-17T07:01:51.9977056Z * [new tag] trunk/f179b7198522e6d93bd103efba1a1ebd5a2cf891 -> trunk/f179b7198522e6d93bd103efba1a1ebd5a2cf891 2025-07-17T07:01:51.9977995Z * [new tag] trunk/f17f6581252ff07e75f33fdce0b4248acdb5da0b -> trunk/f17f6581252ff07e75f33fdce0b4248acdb5da0b 2025-07-17T07:01:51.9978900Z * [new tag] trunk/f1f49e56b079bfc73b1b2fb061d150ec787ee716 -> trunk/f1f49e56b079bfc73b1b2fb061d150ec787ee716 2025-07-17T07:01:51.9979800Z * [new tag] trunk/f2b44424a1f3481ddc066e8dd62c0cafa1298e92 -> trunk/f2b44424a1f3481ddc066e8dd62c0cafa1298e92 2025-07-17T07:01:51.9980736Z * [new tag] trunk/f2d70898c6fad6c6e867d9bf60eced6aacfa9782 -> trunk/f2d70898c6fad6c6e867d9bf60eced6aacfa9782 2025-07-17T07:01:51.9981653Z * [new tag] trunk/f2e712ca14dbbb8435427fd536b0dd63994a9265 -> trunk/f2e712ca14dbbb8435427fd536b0dd63994a9265 2025-07-17T07:01:51.9982566Z * [new tag] trunk/f2ecf6145fde55baa8a91e27b6b3489172f0e639 -> trunk/f2ecf6145fde55baa8a91e27b6b3489172f0e639 2025-07-17T07:01:51.9983530Z * [new tag] trunk/f34335bf3373301ba3af3ff8268cd0cd1715bd2e -> trunk/f34335bf3373301ba3af3ff8268cd0cd1715bd2e 2025-07-17T07:01:51.9984442Z * [new tag] trunk/f34ab1628b6798cc62f55260dfad872f5df8fd94 -> trunk/f34ab1628b6798cc62f55260dfad872f5df8fd94 2025-07-17T07:01:51.9985391Z * [new tag] trunk/f3e6c8e834a49f738e329b802b4b10912e33915f -> trunk/f3e6c8e834a49f738e329b802b4b10912e33915f 2025-07-17T07:01:51.9986317Z * [new tag] trunk/f3ec16c26a24bcbdcb96c692e321be1a4464067c -> trunk/f3ec16c26a24bcbdcb96c692e321be1a4464067c 2025-07-17T07:01:51.9987292Z * [new tag] trunk/f402eed4d9515dee5bb9b11a2b157de57a5988c1 -> trunk/f402eed4d9515dee5bb9b11a2b157de57a5988c1 2025-07-17T07:01:51.9988168Z * [new tag] trunk/f40efde2a474ffd77c021686da2f14864d6aeb6d -> trunk/f40efde2a474ffd77c021686da2f14864d6aeb6d 2025-07-17T07:01:51.9989384Z * [new tag] trunk/f41d017aa6ca1bd121cee6e428875b7fd31a7ad0 -> trunk/f41d017aa6ca1bd121cee6e428875b7fd31a7ad0 2025-07-17T07:01:51.9990664Z * [new tag] trunk/f4376cac54978b9d7c859c1e3495a57d72ab9601 -> trunk/f4376cac54978b9d7c859c1e3495a57d72ab9601 2025-07-17T07:01:51.9991482Z * [new tag] trunk/f4406689b8805ed23dfef34c6320bb99ea6d3767 -> trunk/f4406689b8805ed23dfef34c6320bb99ea6d3767 2025-07-17T07:01:51.9992437Z * [new tag] trunk/f44a9eee4778d2a2aafe3a0beeea7a37ab8d380e -> trunk/f44a9eee4778d2a2aafe3a0beeea7a37ab8d380e 2025-07-17T07:01:51.9993613Z * [new tag] trunk/f45f483884ef524f2d8260dcf2b2ba2c203eab2f -> trunk/f45f483884ef524f2d8260dcf2b2ba2c203eab2f 2025-07-17T07:01:51.9994492Z * [new tag] trunk/f45f6e86b9ef3d9c94b87c2240dbe9a07bb9fd3d -> trunk/f45f6e86b9ef3d9c94b87c2240dbe9a07bb9fd3d 2025-07-17T07:01:51.9995343Z * [new tag] trunk/f48a1576606186cf2104c82be367b617912322de -> trunk/f48a1576606186cf2104c82be367b617912322de 2025-07-17T07:01:51.9996321Z * [new tag] trunk/f4d60a68dd2fb9fda92af83df016f4cfe3af00ba -> trunk/f4d60a68dd2fb9fda92af83df016f4cfe3af00ba 2025-07-17T07:01:51.9997223Z * [new tag] trunk/f56bfb3030acff03e21d568089f3af9e09ec7cb2 -> trunk/f56bfb3030acff03e21d568089f3af9e09ec7cb2 2025-07-17T07:01:51.9998147Z * [new tag] trunk/f58a680d09e13658a52c6ba05c63c15759846bcc -> trunk/f58a680d09e13658a52c6ba05c63c15759846bcc 2025-07-17T07:01:51.9999266Z * [new tag] trunk/f59c76b5494f83abbb2ca169e13201439396c1aa -> trunk/f59c76b5494f83abbb2ca169e13201439396c1aa 2025-07-17T07:01:52.0000047Z * [new tag] trunk/f5bbaa22536437e9f689db8ee7a5ca472fdf4bd0 -> trunk/f5bbaa22536437e9f689db8ee7a5ca472fdf4bd0 2025-07-17T07:01:52.0000930Z * [new tag] trunk/f5e1b24945cf7852a1425923ca543e1f83be14b1 -> trunk/f5e1b24945cf7852a1425923ca543e1f83be14b1 2025-07-17T07:01:52.0001799Z * [new tag] trunk/f5e6e52f2504d0d2c553fd292c89d999b2033616 -> trunk/f5e6e52f2504d0d2c553fd292c89d999b2033616 2025-07-17T07:01:52.0002709Z * [new tag] trunk/f5eb42e4c0405141134e9c52919ebb36ec77ca5f -> trunk/f5eb42e4c0405141134e9c52919ebb36ec77ca5f 2025-07-17T07:01:52.0003600Z * [new tag] trunk/f5f4beaf562911b5cf1a9e8b9eae5e5946681f3c -> trunk/f5f4beaf562911b5cf1a9e8b9eae5e5946681f3c 2025-07-17T07:01:52.0004345Z * [new tag] trunk/f6d138807f138868de0397936e2bee482c1fb987 -> trunk/f6d138807f138868de0397936e2bee482c1fb987 2025-07-17T07:01:52.0005242Z * [new tag] trunk/f6e18bc1054624bb148632a85c10371d6cc62492 -> trunk/f6e18bc1054624bb148632a85c10371d6cc62492 2025-07-17T07:01:52.0006195Z * [new tag] trunk/f70c80105ebc2a118af848c80a18d6efff820f72 -> trunk/f70c80105ebc2a118af848c80a18d6efff820f72 2025-07-17T07:01:52.0007098Z * [new tag] trunk/f7127b9b940a98596599acda1f89fc5153635a5d -> trunk/f7127b9b940a98596599acda1f89fc5153635a5d 2025-07-17T07:01:52.0007963Z * [new tag] trunk/f7130c097efa826313df44f0dcfa7d4d2e4253ec -> trunk/f7130c097efa826313df44f0dcfa7d4d2e4253ec 2025-07-17T07:01:52.0008754Z * [new tag] trunk/f742b32a2ff6326f67512d2b426cdc2da8413a75 -> trunk/f742b32a2ff6326f67512d2b426cdc2da8413a75 2025-07-17T07:01:52.0009508Z * [new tag] trunk/f79689bd3d145e76746e4cf81e99c40df8272a72 -> trunk/f79689bd3d145e76746e4cf81e99c40df8272a72 2025-07-17T07:01:52.0010649Z * [new tag] trunk/f7a5ad6c2987ee5a83aa5d868cee3b8067d3de94 -> trunk/f7a5ad6c2987ee5a83aa5d868cee3b8067d3de94 2025-07-17T07:01:52.0011431Z * [new tag] trunk/f80a61adf57badf999387efe47b8b2ff3388bd96 -> trunk/f80a61adf57badf999387efe47b8b2ff3388bd96 2025-07-17T07:01:52.0012339Z * [new tag] trunk/f810480dbefabbff6cf0852c9f610f84dd440b8d -> trunk/f810480dbefabbff6cf0852c9f610f84dd440b8d 2025-07-17T07:01:52.0013248Z * [new tag] trunk/f810e98143b7b1fe3436d5315101b22aa8438775 -> trunk/f810e98143b7b1fe3436d5315101b22aa8438775 2025-07-17T07:01:52.0014150Z * [new tag] trunk/f8293116f55a9ad399e4938f0fe3e34c6faa47c4 -> trunk/f8293116f55a9ad399e4938f0fe3e34c6faa47c4 2025-07-17T07:01:52.0015838Z * [new tag] trunk/f85954e043a5b6affb589012e8cc5eff8fdb5358 -> trunk/f85954e043a5b6affb589012e8cc5eff8fdb5358 2025-07-17T07:01:52.0016335Z * [new tag] trunk/f860992db5601a78f73eefc0a56af1d7158d6953 -> trunk/f860992db5601a78f73eefc0a56af1d7158d6953 2025-07-17T07:01:52.0017540Z * [new tag] trunk/f87d1179391d66854e3c6ca20717803cfa22f878 -> trunk/f87d1179391d66854e3c6ca20717803cfa22f878 2025-07-17T07:01:52.0018415Z * [new tag] trunk/f88d7a7a34d5a54d58f0a7edc01ff69f46221b6c -> trunk/f88d7a7a34d5a54d58f0a7edc01ff69f46221b6c 2025-07-17T07:01:52.0019367Z * [new tag] trunk/f8baec8984ed90b526fdc03eec19d8039e7d373b -> trunk/f8baec8984ed90b526fdc03eec19d8039e7d373b 2025-07-17T07:01:52.0020289Z * [new tag] trunk/f8c0a4bd28087b02958b92d7b4f41ebc607292b7 -> trunk/f8c0a4bd28087b02958b92d7b4f41ebc607292b7 2025-07-17T07:01:52.0021235Z * [new tag] trunk/f8cc4c0af802269fbe16a418386a94b3b6547c74 -> trunk/f8cc4c0af802269fbe16a418386a94b3b6547c74 2025-07-17T07:01:52.0022214Z * [new tag] trunk/f97f03c7efcf2b7a45384b9094eb6be4cb419546 -> trunk/f97f03c7efcf2b7a45384b9094eb6be4cb419546 2025-07-17T07:01:52.0023166Z * [new tag] trunk/fa0ea57f5e083daab1eb9cda39ab53a5159b077d -> trunk/fa0ea57f5e083daab1eb9cda39ab53a5159b077d 2025-07-17T07:01:52.0024059Z * [new tag] trunk/fa1c20ae9285f7994a73d2d06025065f96b67a57 -> trunk/fa1c20ae9285f7994a73d2d06025065f96b67a57 2025-07-17T07:01:52.0024966Z * [new tag] trunk/fa3c38c7ae17b8d8fccd0958831f9f1ced9e46b3 -> trunk/fa3c38c7ae17b8d8fccd0958831f9f1ced9e46b3 2025-07-17T07:01:52.0025847Z * [new tag] trunk/fa4f07b5b80bdcf99a1c7452de41939d2ab5886f -> trunk/fa4f07b5b80bdcf99a1c7452de41939d2ab5886f 2025-07-17T07:01:52.0026719Z * [new tag] trunk/fa705f791249c5eee912096f1d7ac7b2d5b60e8c -> trunk/fa705f791249c5eee912096f1d7ac7b2d5b60e8c 2025-07-17T07:01:52.0027645Z * [new tag] trunk/fab53dfdf1d89cecd5e82b12cced9b6dd217e87c -> trunk/fab53dfdf1d89cecd5e82b12cced9b6dd217e87c 2025-07-17T07:01:52.0028530Z * [new tag] trunk/fab85fc5f995ef79e67dc4b083f8cfabe7a90798 -> trunk/fab85fc5f995ef79e67dc4b083f8cfabe7a90798 2025-07-17T07:01:52.0029459Z * [new tag] trunk/fac0cc16efd342637a3d1953caf2ecfa8a89947c -> trunk/fac0cc16efd342637a3d1953caf2ecfa8a89947c 2025-07-17T07:01:52.0030400Z * [new tag] trunk/fadc936fad0793e931ed2eb89577e1d10d212f71 -> trunk/fadc936fad0793e931ed2eb89577e1d10d212f71 2025-07-17T07:01:52.0031282Z * [new tag] trunk/fb45649df7267e97efffec8305cddcb23e97d53f -> trunk/fb45649df7267e97efffec8305cddcb23e97d53f 2025-07-17T07:01:52.0032245Z * [new tag] trunk/fb462cec8d8674ad547c55dbe90710bde1dc2019 -> trunk/fb462cec8d8674ad547c55dbe90710bde1dc2019 2025-07-17T07:01:52.0033375Z * [new tag] trunk/fb731fe371cb1b5bf95de84b19c213590526acb2 -> trunk/fb731fe371cb1b5bf95de84b19c213590526acb2 2025-07-17T07:01:52.0034292Z * [new tag] trunk/fb75dea2c1b93c78dccf08d5fd5e20b362ecd405 -> trunk/fb75dea2c1b93c78dccf08d5fd5e20b362ecd405 2025-07-17T07:01:52.0035283Z * [new tag] trunk/fb9a5d248f36ddce041025c8fc5be0d8bee454b0 -> trunk/fb9a5d248f36ddce041025c8fc5be0d8bee454b0 2025-07-17T07:01:52.0036220Z * [new tag] trunk/fbbab794ef6befbe7ffab3648e9b1f2042942fed -> trunk/fbbab794ef6befbe7ffab3648e9b1f2042942fed 2025-07-17T07:01:52.0037111Z * [new tag] trunk/fbd88ae2b5f444cb4b419c415258caeaef4b7325 -> trunk/fbd88ae2b5f444cb4b419c415258caeaef4b7325 2025-07-17T07:01:52.0038068Z * [new tag] trunk/fc0376e8b162d8fcf4375ae30566139eae7d48ed -> trunk/fc0376e8b162d8fcf4375ae30566139eae7d48ed 2025-07-17T07:01:52.0039013Z * [new tag] trunk/fc10d4b1d649f1460b587f3ab45ad4403d84518c -> trunk/fc10d4b1d649f1460b587f3ab45ad4403d84518c 2025-07-17T07:01:52.0040337Z * [new tag] trunk/fc177801afcbf12543b5e193e736b155f79d4ad3 -> trunk/fc177801afcbf12543b5e193e736b155f79d4ad3 2025-07-17T07:01:52.0041227Z * [new tag] trunk/fc5ae1229341ca2841a977197c644733c6a5c789 -> trunk/fc5ae1229341ca2841a977197c644733c6a5c789 2025-07-17T07:01:52.0042139Z * [new tag] trunk/fc772692622e89335dc28638c512bb33dbed1729 -> trunk/fc772692622e89335dc28638c512bb33dbed1729 2025-07-17T07:01:52.0043449Z * [new tag] trunk/fca7013f85d4e7ac07bdd461f490aa159feac6a0 -> trunk/fca7013f85d4e7ac07bdd461f490aa159feac6a0 2025-07-17T07:01:52.0044350Z * [new tag] trunk/fcbf7c749a839cc817927ceba8ea2887cc28dbf5 -> trunk/fcbf7c749a839cc817927ceba8ea2887cc28dbf5 2025-07-17T07:01:52.0045137Z * [new tag] trunk/fcc682be4bda58894a15fee1d9041c6043fea66f -> trunk/fcc682be4bda58894a15fee1d9041c6043fea66f 2025-07-17T07:01:52.0046064Z * [new tag] trunk/fd4bb29410c035b31ca55262c3012cadb1194aae -> trunk/fd4bb29410c035b31ca55262c3012cadb1194aae 2025-07-17T07:01:52.0047247Z * [new tag] trunk/fd4f704905f95b46c4c8fb4108461ff56ce750c5 -> trunk/fd4f704905f95b46c4c8fb4108461ff56ce750c5 2025-07-17T07:01:52.0047994Z * [new tag] trunk/fd8ea3c8a35e5b4aea3ebd712f2c7ee28c5a3655 -> trunk/fd8ea3c8a35e5b4aea3ebd712f2c7ee28c5a3655 2025-07-17T07:01:52.0048872Z * [new tag] trunk/fdc5b42a8fa2b15526c81f2c83f614e086056320 -> trunk/fdc5b42a8fa2b15526c81f2c83f614e086056320 2025-07-17T07:01:52.0049737Z * [new tag] trunk/fdf5d97fa8393f56aea2779877efd8a264ad5811 -> trunk/fdf5d97fa8393f56aea2779877efd8a264ad5811 2025-07-17T07:01:52.0051381Z * [new tag] trunk/fe1f1a38dff7c59ec0ec5b05fef058629845d061 -> trunk/fe1f1a38dff7c59ec0ec5b05fef058629845d061 2025-07-17T07:01:52.0052223Z * [new tag] trunk/fec571cfd458c4644a2f483dbf9f5480feca7939 -> trunk/fec571cfd458c4644a2f483dbf9f5480feca7939 2025-07-17T07:01:52.0053115Z * [new tag] trunk/fec8af8b98f5e17740ff947e9f8a1c447a497413 -> trunk/fec8af8b98f5e17740ff947e9f8a1c447a497413 2025-07-17T07:01:52.0054015Z * [new tag] trunk/fedbd1a48e1e474cf9da5637aae89b5bc4c20626 -> trunk/fedbd1a48e1e474cf9da5637aae89b5bc4c20626 2025-07-17T07:01:52.0054772Z * [new tag] trunk/fee2377f9ea62223f69ea9904c5e25ccb2af5961 -> trunk/fee2377f9ea62223f69ea9904c5e25ccb2af5961 2025-07-17T07:01:52.0055608Z * [new tag] trunk/feea575082439a0496dd404a4925b7d551039065 -> trunk/feea575082439a0496dd404a4925b7d551039065 2025-07-17T07:01:52.0056675Z * [new tag] trunk/ff611d971fe5362a71c15109cf020d30e6c4b2b9 -> trunk/ff611d971fe5362a71c15109cf020d30e6c4b2b9 2025-07-17T07:01:52.0057729Z * [new tag] trunk/ff7dd1776f9cb8448336338b19b9b53073f2fbda -> trunk/ff7dd1776f9cb8448336338b19b9b53073f2fbda 2025-07-17T07:01:52.0058573Z * [new tag] trunk/ff8b53c056e6556187690a37c944c92feb964d2d -> trunk/ff8b53c056e6556187690a37c944c92feb964d2d 2025-07-17T07:01:52.0059432Z * [new tag] trunk/ffac0de07e0173a073c92c157f43c515009c0de1 -> trunk/ffac0de07e0173a073c92c157f43c515009c0de1 2025-07-17T07:01:52.0060602Z * [new tag] trunk/ffaed8c569406839335bf46dafc4c3e8871e4b8a -> trunk/ffaed8c569406839335bf46dafc4c3e8871e4b8a 2025-07-17T07:01:52.0061450Z * [new tag] trunk/ffc6cbfaf78ca219092ce64dcf113377ae698300 -> trunk/ffc6cbfaf78ca219092ce64dcf113377ae698300 2025-07-17T07:01:52.0062187Z * [new tag] v0.1.1 -> v0.1.1 2025-07-17T07:01:52.0063131Z * [new tag] v0.1.10 -> v0.1.10 2025-07-17T07:01:52.0063837Z * [new tag] v0.1.11 -> v0.1.11 2025-07-17T07:01:52.0064749Z * [new tag] v0.1.12 -> v0.1.12 2025-07-17T07:01:52.0065432Z * [new tag] v0.1.2 -> v0.1.2 2025-07-17T07:01:52.0066271Z * [new tag] v0.1.3 -> v0.1.3 2025-07-17T07:01:52.0067088Z * [new tag] v0.1.4 -> v0.1.4 2025-07-17T07:01:52.0067915Z * [new tag] v0.1.5 -> v0.1.5 2025-07-17T07:01:52.0068643Z * [new tag] v0.1.6 -> v0.1.6 2025-07-17T07:01:52.0069574Z * [new tag] v0.1.7 -> v0.1.7 2025-07-17T07:01:52.0070273Z * [new tag] v0.1.8 -> v0.1.8 2025-07-17T07:01:52.0071168Z * [new tag] v0.1.9 -> v0.1.9 2025-07-17T07:01:52.0072002Z * [new tag] v0.2.0 -> v0.2.0 2025-07-17T07:01:52.0076321Z * [new tag] v0.3.0 -> v0.3.0 2025-07-17T07:01:52.0077289Z * [new tag] v0.3.1 -> v0.3.1 2025-07-17T07:01:52.0078164Z * [new tag] v0.4.0 -> v0.4.0 2025-07-17T07:01:52.0078846Z * [new tag] v0.4.1 -> v0.4.1 2025-07-17T07:01:52.0079742Z * [new tag] v1.0.0 -> v1.0.0 2025-07-17T07:01:52.0080597Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-07-17T07:01:52.0081419Z * [new tag] v1.0.1 -> v1.0.1 2025-07-17T07:01:52.0082290Z * [new tag] v1.0rc0 -> v1.0rc0 2025-07-17T07:01:52.0083254Z * [new tag] v1.0rc1 -> v1.0rc1 2025-07-17T07:01:52.0083774Z * [new tag] v1.1.0 -> v1.1.0 2025-07-17T07:01:52.0084729Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-07-17T07:01:52.0085716Z * [new tag] v1.10.0 -> v1.10.0 2025-07-17T07:01:52.0086657Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-07-17T07:01:52.0087518Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-07-17T07:01:52.0088107Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-07-17T07:01:52.0089054Z * [new tag] v1.10.1 -> v1.10.1 2025-07-17T07:01:52.0089663Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-07-17T07:01:52.0090275Z * [new tag] v1.10.2 -> v1.10.2 2025-07-17T07:01:52.0090962Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-07-17T07:01:52.0092022Z * [new tag] v1.11.0 -> v1.11.0 2025-07-17T07:01:52.0092953Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-07-17T07:01:52.0093841Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-07-17T07:01:52.0094752Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-07-17T07:01:52.0095720Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-07-17T07:01:52.0096636Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-07-17T07:01:52.0097180Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-07-17T07:01:52.0097908Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-07-17T07:01:52.0098849Z * [new tag] v1.12.0 -> v1.12.0 2025-07-17T07:01:52.0100135Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-07-17T07:01:52.0101027Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-07-17T07:01:52.0101903Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-07-17T07:01:52.0102780Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-07-17T07:01:52.0103514Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-07-17T07:01:52.0104563Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-07-17T07:01:52.0105176Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-07-17T07:01:52.0105793Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-07-17T07:01:52.0106431Z * [new tag] v1.12.1 -> v1.12.1 2025-07-17T07:01:52.0107496Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-07-17T07:01:52.0108370Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-07-17T07:01:52.0109286Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-07-17T07:01:52.0110164Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-07-17T07:01:52.0110711Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-07-17T07:01:52.0111733Z * [new tag] v1.13.0 -> v1.13.0 2025-07-17T07:01:52.0112487Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-07-17T07:01:52.0113372Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-07-17T07:01:52.0114240Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-07-17T07:01:52.0115257Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-07-17T07:01:52.0115884Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-07-17T07:01:52.0116533Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-07-17T07:01:52.0117630Z * [new tag] v1.13.1 -> v1.13.1 2025-07-17T07:01:52.0118079Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-07-17T07:01:52.0119112Z * [new tag] v1.2.0 -> v1.2.0 2025-07-17T07:01:52.0119956Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-07-17T07:01:52.0120695Z * [new tag] v1.3.0 -> v1.3.0 2025-07-17T07:01:52.0121640Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-07-17T07:01:52.0122170Z * [new tag] v1.3.1 -> v1.3.1 2025-07-17T07:01:52.0123108Z * [new tag] v1.4.0 -> v1.4.0 2025-07-17T07:01:52.0123984Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-07-17T07:01:52.0124559Z * [new tag] v1.4.1 -> v1.4.1 2025-07-17T07:01:52.0125531Z * [new tag] v1.5.0 -> v1.5.0 2025-07-17T07:01:52.0126413Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-07-17T07:01:52.0127256Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-07-17T07:01:52.0128259Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-07-17T07:01:52.0128920Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-07-17T07:01:52.0129562Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-07-17T07:01:52.0130583Z * [new tag] v1.5.1 -> v1.5.1 2025-07-17T07:01:52.0131200Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-07-17T07:01:52.0131817Z * [new tag] v1.6.0 -> v1.6.0 2025-07-17T07:01:52.0132804Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-07-17T07:01:52.0133694Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-07-17T07:01:52.0134657Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-07-17T07:01:52.0135400Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-07-17T07:01:52.0136440Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-07-17T07:01:52.0137191Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-07-17T07:01:52.0137873Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-07-17T07:01:52.0138836Z * [new tag] v1.7.0 -> v1.7.0 2025-07-17T07:01:52.0139670Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-07-17T07:01:52.0140724Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-07-17T07:01:52.0141601Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-07-17T07:01:52.0142123Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-07-17T07:01:52.0143116Z * [new tag] v1.7.1 -> v1.7.1 2025-07-17T07:01:52.0144122Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-07-17T07:01:52.0144999Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-07-17T07:01:52.0145662Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-07-17T07:01:52.0146602Z * [new tag] v1.8.0 -> v1.8.0 2025-07-17T07:01:52.0147162Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-07-17T07:01:52.0148205Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-07-17T07:01:52.0149082Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-07-17T07:01:52.0149788Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-07-17T07:01:52.0150467Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-07-17T07:01:52.0151196Z * [new tag] v1.8.1 -> v1.8.1 2025-07-17T07:01:52.0152089Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-07-17T07:01:52.0153122Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-07-17T07:01:52.0153744Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-07-17T07:01:52.0155144Z * [new tag] v1.8.2 -> v1.8.2 2025-07-17T07:01:52.0155707Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-07-17T07:01:52.0156609Z * [new tag] v1.9.0 -> v1.9.0 2025-07-17T07:01:52.0157470Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-07-17T07:01:52.0158626Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-07-17T07:01:52.0159590Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-07-17T07:01:52.0160151Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-07-17T07:01:52.0161148Z * [new tag] v1.9.1 -> v1.9.1 2025-07-17T07:01:52.0162211Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-07-17T07:01:52.0162770Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-07-17T07:01:52.0163772Z * [new tag] v2.0.0 -> v2.0.0 2025-07-17T07:01:52.0169759Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-07-17T07:01:52.0170051Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-07-17T07:01:52.0170216Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-07-17T07:01:52.0170370Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-07-17T07:01:52.0170511Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-07-17T07:01:52.0170652Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-07-17T07:01:52.0170800Z * [new tag] v2.0.1 -> v2.0.1 2025-07-17T07:01:52.0170945Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-07-17T07:01:52.0171322Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-07-17T07:01:52.0172331Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-07-17T07:01:52.0173033Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-07-17T07:01:52.0174479Z * [new tag] v2.1.0 -> v2.1.0 2025-07-17T07:01:52.0175340Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-07-17T07:01:52.0176308Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-07-17T07:01:52.0177240Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-07-17T07:01:52.0178208Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-07-17T07:01:52.0178985Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-07-17T07:01:52.0179631Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-07-17T07:01:52.0180585Z * [new tag] v2.1.1 -> v2.1.1 2025-07-17T07:01:52.0181496Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-07-17T07:01:52.0182343Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-07-17T07:01:52.0183291Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-07-17T07:01:52.0184217Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-07-17T07:01:52.0185058Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-07-17T07:01:52.0185610Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-07-17T07:01:52.0186552Z * [new tag] v2.1.2 -> v2.1.2 2025-07-17T07:01:52.0187598Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-07-17T07:01:52.0188481Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-07-17T07:01:52.0189116Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-07-17T07:01:52.0190026Z * [new tag] v2.2.0 -> v2.2.0 2025-07-17T07:01:52.0190878Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-07-17T07:01:52.0191718Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-07-17T07:01:52.0192531Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-07-17T07:01:52.0193414Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-07-17T07:01:52.0194147Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-07-17T07:01:52.0195115Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-07-17T07:01:52.0195718Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-07-17T07:01:52.0196358Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-07-17T07:01:52.0197284Z * [new tag] v2.2.1 -> v2.2.1 2025-07-17T07:01:52.0198166Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-07-17T07:01:52.0198767Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-07-17T07:01:52.0199407Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-07-17T07:01:52.0200271Z * [new tag] v2.2.2 -> v2.2.2 2025-07-17T07:01:52.0201261Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-07-17T07:01:52.0201828Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-07-17T07:01:52.0202523Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-07-17T07:01:52.0203508Z * [new tag] v2.3.0 -> v2.3.0 2025-07-17T07:01:52.0204372Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-07-17T07:01:52.0205248Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-07-17T07:01:52.0206251Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-07-17T07:01:52.0206879Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-07-17T07:01:52.0208282Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-07-17T07:01:52.0209150Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-07-17T07:01:52.0210057Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-07-17T07:01:52.0210943Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-07-17T07:01:52.0211548Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-07-17T07:01:52.0212443Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-07-17T07:01:52.0213335Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-07-17T07:01:52.0213932Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-07-17T07:01:52.0214594Z * [new tag] v2.3.1 -> v2.3.1 2025-07-17T07:01:52.0215608Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-07-17T07:01:52.0216536Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-07-17T07:01:52.0217425Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-07-17T07:01:52.0218301Z * [new tag] v2.4.0 -> v2.4.0 2025-07-17T07:01:52.0219186Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-07-17T07:01:52.0220062Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-07-17T07:01:52.0220926Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-07-17T07:01:52.0221662Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-07-17T07:01:52.0222759Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-07-17T07:01:52.0223590Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-07-17T07:01:52.0224463Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-07-17T07:01:52.0225332Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-07-17T07:01:52.0226233Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-07-17T07:01:52.0226834Z * [new tag] v2.4.1 -> v2.4.1 2025-07-17T07:01:52.0227980Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-07-17T07:01:52.0228879Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-07-17T07:01:52.0229839Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-07-17T07:01:52.0230687Z * [new tag] v2.5.0 -> v2.5.0 2025-07-17T07:01:52.0231552Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-07-17T07:01:52.0232104Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-07-17T07:01:52.0233116Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-07-17T07:01:52.0233940Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-07-17T07:01:52.0234806Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-07-17T07:01:52.0235722Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-07-17T07:01:52.0236634Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-07-17T07:01:52.0237491Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-07-17T07:01:52.0238407Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-07-17T07:01:52.0239274Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-07-17T07:01:52.0239904Z * [new tag] v2.5.1 -> v2.5.1 2025-07-17T07:01:52.0240560Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-07-17T07:01:52.0241232Z * [new tag] v2.6.0 -> v2.6.0 2025-07-17T07:01:52.0242326Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-07-17T07:01:52.0243271Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-07-17T07:01:52.0244260Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-07-17T07:01:52.0245074Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-07-17T07:01:52.0246196Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-07-17T07:01:52.0247219Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-07-17T07:01:52.0248136Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-07-17T07:01:52.0249133Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-07-17T07:01:52.0250018Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-07-17T07:01:52.0251089Z * [new tag] v2.7.0 -> v2.7.0 2025-07-17T07:01:52.0251967Z * [new tag] v2.7.0-rc1 -> v2.7.0-rc1 2025-07-17T07:01:52.0252598Z * [new tag] v2.7.0-rc10 -> v2.7.0-rc10 2025-07-17T07:01:52.0253685Z * [new tag] v2.7.0-rc2 -> v2.7.0-rc2 2025-07-17T07:01:52.0254612Z * [new tag] v2.7.0-rc3 -> v2.7.0-rc3 2025-07-17T07:01:52.0255610Z * [new tag] v2.7.0-rc4 -> v2.7.0-rc4 2025-07-17T07:01:52.0256571Z * [new tag] v2.7.0-rc5 -> v2.7.0-rc5 2025-07-17T07:01:52.0257489Z * [new tag] v2.7.0-rc6 -> v2.7.0-rc6 2025-07-17T07:01:52.0258356Z * [new tag] v2.7.0-rc7 -> v2.7.0-rc7 2025-07-17T07:01:52.0259372Z * [new tag] v2.7.0-rc8 -> v2.7.0-rc8 2025-07-17T07:01:52.0260273Z * [new tag] v2.7.0-rc9 -> v2.7.0-rc9 2025-07-17T07:01:52.0260899Z * [new tag] v2.7.1 -> v2.7.1 2025-07-17T07:01:52.0261823Z * [new tag] v2.7.1-rc1 -> v2.7.1-rc1 2025-07-17T07:01:52.0262778Z * [new tag] v2.7.1-rc2 -> v2.7.1-rc2 2025-07-17T07:01:52.0263714Z * [new tag] v2.7.1-rc3 -> v2.7.1-rc3 2025-07-17T07:01:52.0265012Z * [new tag] v2.7.1-rc4 -> v2.7.1-rc4 2025-07-17T07:01:52.0265868Z * [new tag] v2.7.1-rc5 -> v2.7.1-rc5 2025-07-17T07:01:52.0266800Z * [new tag] v2.8.0-rc1 -> v2.8.0-rc1 2025-07-17T07:01:52.0267696Z * [new tag] v2.8.0-rc2 -> v2.8.0-rc2 2025-07-17T07:01:52.0268668Z * [new tag] v2.8.0-rc3 -> v2.8.0-rc3 2025-07-17T07:01:52.0269575Z * [new tag] v2.8.0-rc4 -> v2.8.0-rc4 2025-07-17T07:01:52.0270491Z * [new tag] v2.8.0-rc5 -> v2.8.0-rc5 2025-07-17T07:01:52.0271359Z * [new tag] whc_flight_1 -> whc_flight_1 2025-07-17T07:01:52.0272069Z * [new tag] whc_flight_2 -> whc_flight_2 2025-07-17T07:01:52.0272997Z * [new tag] whc_flight_4 -> whc_flight_4 2025-07-17T07:01:52.1081605Z [command]/usr/bin/git rev-parse --verify --quiet a38f433be2e94a64b095a44ba39879d02d0c2316^{object} 2025-07-17T07:01:52.1105423Z a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:01:52.1109320Z ##[endgroup] 2025-07-17T07:01:52.1109781Z ##[group]Determining the checkout info 2025-07-17T07:01:52.1110285Z ##[endgroup] 2025-07-17T07:01:52.1114007Z [command]/usr/bin/git sparse-checkout disable 2025-07-17T07:01:52.1154203Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-07-17T07:01:52.1195199Z ##[group]Checking out the ref 2025-07-17T07:01:52.1198792Z [command]/usr/bin/git checkout --progress --force a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:01:53.1476023Z Updating files: 87% (17126/19589) 2025-07-17T07:01:53.1600825Z Updating files: 88% (17239/19589) 2025-07-17T07:01:53.1710537Z Updating files: 89% (17435/19589) 2025-07-17T07:01:53.1867320Z Updating files: 90% (17631/19589) 2025-07-17T07:01:53.1997026Z Updating files: 91% (17826/19589) 2025-07-17T07:01:53.2117075Z Updating files: 92% (18022/19589) 2025-07-17T07:01:53.2266914Z Updating files: 93% (18218/19589) 2025-07-17T07:01:53.2447599Z Updating files: 94% (18414/19589) 2025-07-17T07:01:53.2642856Z Updating files: 95% (18610/19589) 2025-07-17T07:01:53.2795475Z Updating files: 96% (18806/19589) 2025-07-17T07:01:53.2962325Z Updating files: 97% (19002/19589) 2025-07-17T07:01:53.3225878Z Updating files: 98% (19198/19589) 2025-07-17T07:01:53.3377560Z Updating files: 99% (19394/19589) 2025-07-17T07:01:53.3377907Z Updating files: 100% (19589/19589) 2025-07-17T07:01:53.3378217Z Updating files: 100% (19589/19589), done. 2025-07-17T07:01:53.3636602Z Note: switching to 'a38f433be2e94a64b095a44ba39879d02d0c2316'. 2025-07-17T07:01:53.3636969Z 2025-07-17T07:01:53.3637201Z You are in 'detached HEAD' state. You can look around, make experimental 2025-07-17T07:01:53.3637782Z changes and commit them, and you can discard any commits you make in this 2025-07-17T07:01:53.3638340Z state without impacting any branches by switching back to a branch. 2025-07-17T07:01:53.3638684Z 2025-07-17T07:01:53.3638895Z If you want to create a new branch to retain commits you create, you may 2025-07-17T07:01:53.3639424Z do so (now or later) by using -c with the switch command. Example: 2025-07-17T07:01:53.3639719Z 2025-07-17T07:01:53.3639846Z git switch -c 2025-07-17T07:01:53.3640048Z 2025-07-17T07:01:53.3640157Z Or undo this operation with: 2025-07-17T07:01:53.3640536Z 2025-07-17T07:01:53.3640633Z git switch - 2025-07-17T07:01:53.3640782Z 2025-07-17T07:01:53.3641023Z Turn off this advice by setting config variable advice.detachedHead to false 2025-07-17T07:01:53.3641381Z 2025-07-17T07:01:53.3641665Z HEAD is now at a38f433be2e [Docker builds] Move from Miniconda to Miniforge (#158370) 2025-07-17T07:01:53.3720229Z ##[endgroup] 2025-07-17T07:01:53.3720674Z ##[group]Setting up auth for fetching submodules 2025-07-17T07:01:53.3725941Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-07-17T07:01:53.3765126Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-07-17T07:01:53.3790706Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-07-17T07:01:53.3816189Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-07-17T07:01:53.3837924Z ##[endgroup] 2025-07-17T07:01:53.3838346Z ##[group]Fetching submodules 2025-07-17T07:01:53.3841135Z [command]/usr/bin/git submodule sync --recursive 2025-07-17T07:01:53.4168947Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-07-17T07:01:53.4491184Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-07-17T07:01:53.4493333Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-07-17T07:01:53.4774606Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-07-17T07:01:53.4777555Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-07-17T07:01:53.4780685Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-07-17T07:01:53.4784425Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-07-17T07:01:53.4787567Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-07-17T07:01:53.4791041Z Submodule 'third_party/aiter' (https://github.com/ROCm/aiter.git) registered for path 'third_party/aiter' 2025-07-17T07:01:53.4799190Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-07-17T07:01:53.4803173Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-07-17T07:01:53.4806965Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-07-17T07:01:53.4810826Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-07-17T07:01:53.4814931Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-07-17T07:01:53.4819105Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-07-17T07:01:53.4827590Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-07-17T07:01:53.4832672Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-07-17T07:01:53.4837292Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-07-17T07:01:53.4841757Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-07-17T07:01:53.4846680Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-07-17T07:01:53.4852684Z Submodule 'third_party/gloo' (https://github.com/pytorch/gloo) registered for path 'third_party/gloo' 2025-07-17T07:01:53.4862252Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-07-17T07:01:53.4867082Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-07-17T07:01:53.4872195Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-07-17T07:01:53.4878393Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-07-17T07:01:53.4883599Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-07-17T07:01:53.4888805Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-07-17T07:01:53.4899035Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-07-17T07:01:53.4904374Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-07-17T07:01:53.4910184Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-07-17T07:01:53.4915631Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-07-17T07:01:53.4921402Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-07-17T07:01:53.4927158Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-07-17T07:01:53.4937927Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-07-17T07:01:53.4943847Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-07-17T07:01:53.4949998Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-07-17T07:01:53.4956136Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-07-17T07:01:53.4964134Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-07-17T07:01:53.4994504Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-07-17T07:01:53.7387271Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2025-07-17T07:01:53.7388069Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-07-17T07:01:53.7392104Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-07-17T07:01:57.0377358Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-07-17T07:01:57.0379042Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-07-17T07:01:57.0380724Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-07-17T07:01:57.0382335Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2025-07-17T07:01:57.0383986Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-07-17T07:01:57.0503521Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-07-17T07:01:57.0505029Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-07-17T07:01:57.0506784Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-07-17T07:01:57.0508193Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2025-07-17T07:01:57.0509549Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-07-17T07:01:57.0510891Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-07-17T07:01:57.0512325Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-07-17T07:01:57.0513714Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-07-17T07:01:57.0515139Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2025-07-17T07:01:57.0516487Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2025-07-17T07:01:57.0517835Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-07-17T07:01:57.0519222Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-07-17T07:01:57.0761211Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2025-07-17T07:01:57.2142459Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-07-17T07:01:57.2247925Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-07-17T07:02:09.2820854Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-07-17T07:02:09.2822450Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-07-17T07:02:09.2823907Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2025-07-17T07:02:09.2825245Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2025-07-17T07:02:09.2826680Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-07-17T07:02:09.2828111Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-07-17T07:02:09.2829516Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-07-17T07:02:09.2831022Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-07-17T07:02:09.2832728Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter'... 2025-07-17T07:02:09.2834082Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2025-07-17T07:02:09.2835456Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-07-17T07:02:09.3821975Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-07-17T07:02:14.7807482Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-07-17T07:02:14.7966361Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-07-17T07:02:14.8090737Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-07-17T07:02:14.8186086Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-07-17T07:02:14.8433280Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-07-17T07:02:14.9192775Z Submodule path 'third_party/NVTX': checked out '2942f167cc30c5e3a44a2aecd5b0d9c07ff61a07' 2025-07-17T07:02:14.9696383Z Submodule path 'third_party/VulkanMemoryAllocator': checked out '1d8f600fd424278486eade7ed3e877c99f0846b1' 2025-07-17T07:02:15.6897200Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-07-17T07:02:15.8298949Z Submodule path 'third_party/aiter': checked out '01aae101b9e5e94d6c16a9514c9fb8df99c93150' 2025-07-17T07:02:15.8318919Z Submodule '3rdparty/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/aiter/3rdparty/composable_kernel' 2025-07-17T07:02:15.8346649Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter/3rdparty/composable_kernel'... 2025-07-17T07:02:18.5121972Z Submodule path 'third_party/aiter/3rdparty/composable_kernel': checked out 'cffe8fa2a442ac8e80dd236a1a5d24fe3d7e0cbf' 2025-07-17T07:02:18.5357929Z Submodule path 'third_party/benchmark': checked out '299e5928955cc62af9968370293b916f5130916f' 2025-07-17T07:02:18.8141714Z Submodule path 'third_party/composable_kernel': checked out '434d19f696da62c12b5372b32cbc9ba968588d7e' 2025-07-17T07:02:18.8659974Z Submodule path 'third_party/cpp-httplib': checked out '3af7f2c16147f3fbc6e4d717032daf505dc1652c' 2025-07-17T07:02:18.9617766Z Submodule path 'third_party/cpuinfo': checked out '5e3d2445e6a84d9599bee2bf78edbb4d80865e1d' 2025-07-17T07:02:19.0055110Z Submodule path 'third_party/cudnn_frontend': checked out 'f937055efc6d414d11f4c6577e3977fe74f35fb6' 2025-07-17T07:02:19.6195984Z Submodule path 'third_party/cutlass': checked out 'b995f933179c22d3fe0d871c3a53d11e4681950f' 2025-07-17T07:02:19.7456528Z Submodule path 'third_party/fbgemm': checked out '157e88b750c452bef2ab4653fe9d1eeb151ce4c3' 2025-07-17T07:02:19.7476373Z Submodule 'external/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/external/asmjit' 2025-07-17T07:02:19.7478951Z Submodule 'external/composable_kernel' (https://github.com/jwfromm/composable_kernel.git) registered for path 'third_party/fbgemm/external/composable_kernel' 2025-07-17T07:02:19.7481611Z Submodule 'external/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/external/cpuinfo' 2025-07-17T07:02:19.7484190Z Submodule 'external/cutlass' (https://github.com/jwfromm/cutlass) registered for path 'third_party/fbgemm/external/cutlass' 2025-07-17T07:02:19.7487056Z Submodule 'external/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/external/googletest' 2025-07-17T07:02:19.7490033Z Submodule 'external/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/external/hipify_torch' 2025-07-17T07:02:19.7492887Z Submodule 'external/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/fbgemm/external/json' 2025-07-17T07:02:19.7521996Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/asmjit'... 2025-07-17T07:02:21.0464214Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/hipify_torch'... 2025-07-17T07:02:21.0465188Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cpuinfo'... 2025-07-17T07:02:21.0466099Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/googletest'... 2025-07-17T07:02:21.0622339Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/composable_kernel'... 2025-07-17T07:02:21.1623210Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cutlass'... 2025-07-17T07:02:22.2323502Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/json'... 2025-07-17T07:02:27.0334720Z Submodule path 'third_party/fbgemm/external/asmjit': checked out 'e5d7c0bd5d9aec44d68830187138149e6a8c4e32' 2025-07-17T07:02:27.2602153Z Submodule path 'third_party/fbgemm/external/composable_kernel': checked out '4a61bdd4bd4ed730e078aebc7c0fcf046ff29406' 2025-07-17T07:02:27.3593222Z Submodule path 'third_party/fbgemm/external/cpuinfo': checked out '6543fec09b2f04ac4a666882998b534afc9c1349' 2025-07-17T07:02:27.6744791Z From https://github.com/jwfromm/cutlass 2025-07-17T07:02:27.6745319Z * branch 3ed8d2ec4ba35ef5d9d8353826209b6f868f63d3 -> FETCH_HEAD 2025-07-17T07:02:28.2494043Z Submodule path 'third_party/fbgemm/external/cutlass': checked out '3ed8d2ec4ba35ef5d9d8353826209b6f868f63d3' 2025-07-17T07:02:28.2948091Z Submodule path 'third_party/fbgemm/external/googletest': checked out 'f8d7d77c06936315286eb55f8de22cd23c188571' 2025-07-17T07:02:28.3069009Z Submodule path 'third_party/fbgemm/external/hipify_torch': checked out 'a4337c69fe0e2552a7b7b0669178926beeed828c' 2025-07-17T07:02:28.4137264Z Submodule path 'third_party/fbgemm/external/json': checked out '9cca280a4d0ccf0c08f47a99aa71d1b0e52f8d03' 2025-07-17T07:02:28.4840919Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-07-17T07:02:28.4860266Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-07-17T07:02:28.4862601Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-07-17T07:02:28.4890228Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-07-17T07:02:31.0701050Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-07-17T07:02:31.3053654Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-07-17T07:02:31.8621002Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-07-17T07:02:32.0002386Z Submodule path 'third_party/flatbuffers': checked out 'a2cd1ea3b6d3fee220106b5fed3f7ce8da9eb757' 2025-07-17T07:02:32.0341833Z Submodule path 'third_party/fmt': checked out '40626af88bd7df9a5fb80be7b25ac85b122d6c21' 2025-07-17T07:02:32.0732369Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-07-17T07:02:32.0977886Z Submodule path 'third_party/gloo': checked out 'c7b7b022c124d9643957d9bd55f57ac59fce8fa2' 2025-07-17T07:02:32.1421437Z Submodule path 'third_party/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-07-17T07:02:32.1549943Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3' 2025-07-17T07:02:32.1566246Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-07-17T07:02:32.1591863Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-07-17T07:02:43.1441156Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d' 2025-07-17T07:02:43.1645173Z Submodule path 'third_party/ittapi': checked out 'dec1d23ca65ab069d225dfe40dea14f455170959' 2025-07-17T07:02:43.2511870Z Submodule path 'third_party/kineto': checked out '5e7501833f1021ce6f618572d3baf657b6319658' 2025-07-17T07:02:43.2529256Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-07-17T07:02:43.2531577Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-07-17T07:02:43.2534278Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-07-17T07:02:43.2561939Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-07-17T07:02:43.9271372Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-07-17T07:02:44.5567171Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-07-17T07:02:44.6379008Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-07-17T07:02:44.6396763Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-07-17T07:02:44.6399296Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-07-17T07:02:44.6402102Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-07-17T07:02:44.6404842Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-07-17T07:02:44.6407676Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-07-17T07:02:44.6410797Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-07-17T07:02:44.6413794Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-07-17T07:02:44.6417080Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-07-17T07:02:44.6447125Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-07-17T07:02:46.0288521Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-07-17T07:02:46.0290214Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-07-17T07:02:46.0291458Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-07-17T07:02:46.0292616Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-07-17T07:02:46.0293884Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-07-17T07:02:46.0600833Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-07-17T07:02:46.1602426Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-07-17T07:02:53.5902159Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-07-17T07:02:53.6081184Z Submodule path 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2025-07-17T07:02:54.0495119Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-07-17T07:02:54.0908133Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-07-17T07:02:54.1946484Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-07-17T07:02:54.2115940Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-07-17T07:02:54.2503068Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-07-17T07:02:54.3072391Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-07-17T07:02:54.3475977Z Submodule path 'third_party/kleidiai': checked out 'cca02c2f69dd18e1f12647c1c0bdc8cf90e680c7' 2025-07-17T07:02:54.3852221Z Submodule path 'third_party/mimalloc': checked out 'fbd8b99c2b828428947d70fdc046bb55609be93e' 2025-07-17T07:02:54.4970032Z Submodule path 'third_party/nlohmann': checked out '55f93686c01528224f448c19128836e7df245f72' 2025-07-17T07:02:54.8961464Z Submodule path 'third_party/onnx': checked out 'e709452ef2bbc1d113faf678c24e6d3467696e83' 2025-07-17T07:02:54.8998481Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11' 2025-07-17T07:02:54.9026375Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'... 2025-07-17T07:02:55.7547920Z Submodule path 'third_party/onnx/third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4' 2025-07-17T07:02:55.8239841Z Submodule path 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2025-07-17T07:02:55.8272307Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-07-17T07:02:55.8275359Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-07-17T07:02:55.8278519Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-07-17T07:02:55.8306320Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2025-07-17T07:02:56.2119937Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2025-07-17T07:02:56.2121128Z Cloning into 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'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-07-17T07:03:11.9878366Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-07-17T07:03:11.9918545Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-07-17T07:03:11.9961034Z Entering 'third_party/kleidiai' 2025-07-17T07:03:12.0005707Z Entering 'third_party/mimalloc' 2025-07-17T07:03:12.0048582Z Entering 'third_party/nlohmann' 2025-07-17T07:03:12.0091722Z Entering 'third_party/onnx' 2025-07-17T07:03:12.0151648Z Entering 'third_party/onnx/third_party/pybind11' 2025-07-17T07:03:12.0197243Z Entering 'third_party/opentelemetry-cpp' 2025-07-17T07:03:12.0241074Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-07-17T07:03:12.0282636Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-07-17T07:03:12.0322836Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-07-17T07:03:12.0362908Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-07-17T07:03:12.0407636Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-07-17T07:03:12.0448512Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-07-17T07:03:12.0489083Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-07-17T07:03:12.0528930Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-07-17T07:03:12.0571504Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-07-17T07:03:12.0613687Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-07-17T07:03:12.0674721Z Entering 'third_party/pocketfft' 2025-07-17T07:03:12.0716548Z Entering 'third_party/protobuf' 2025-07-17T07:03:12.0760792Z Entering 'third_party/protobuf/third_party/benchmark' 2025-07-17T07:03:12.0802077Z Entering 'third_party/protobuf/third_party/googletest' 2025-07-17T07:03:12.0843844Z Entering 'third_party/psimd' 2025-07-17T07:03:12.0885727Z Entering 'third_party/pthreadpool' 2025-07-17T07:03:12.0927590Z Entering 'third_party/pybind11' 2025-07-17T07:03:12.0969114Z Entering 'third_party/python-peachpy' 2025-07-17T07:03:12.1010728Z Entering 'third_party/sleef' 2025-07-17T07:03:12.1052126Z Entering 'third_party/tensorpipe' 2025-07-17T07:03:12.1096231Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-07-17T07:03:12.1135952Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-07-17T07:03:12.1176281Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-07-17T07:03:12.1216734Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T07:03:12.1257471Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T07:03:12.1313904Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-07-17T07:03:12.1610729Z Entering 'android/libs/fbjni' 2025-07-17T07:03:12.1653870Z Entering 'third_party/FP16' 2025-07-17T07:03:12.1698217Z Entering 'third_party/FXdiv' 2025-07-17T07:03:12.1740205Z Entering 'third_party/NNPACK' 2025-07-17T07:03:12.1782834Z Entering 'third_party/NVTX' 2025-07-17T07:03:12.1825752Z Entering 'third_party/VulkanMemoryAllocator' 2025-07-17T07:03:12.1867977Z Entering 'third_party/XNNPACK' 2025-07-17T07:03:12.1925748Z Entering 'third_party/aiter' 2025-07-17T07:03:12.1973021Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-07-17T07:03:12.2022387Z Entering 'third_party/benchmark' 2025-07-17T07:03:12.2064223Z Entering 'third_party/composable_kernel' 2025-07-17T07:03:12.2112555Z Entering 'third_party/cpp-httplib' 2025-07-17T07:03:12.2154206Z Entering 'third_party/cpuinfo' 2025-07-17T07:03:12.2196876Z Entering 'third_party/cudnn_frontend' 2025-07-17T07:03:12.2239303Z Entering 'third_party/cutlass' 2025-07-17T07:03:12.2292875Z Entering 'third_party/fbgemm' 2025-07-17T07:03:12.2337788Z Entering 'third_party/fbgemm/external/asmjit' 2025-07-17T07:03:12.2379089Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-07-17T07:03:12.2426972Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-07-17T07:03:12.2469159Z Entering 'third_party/fbgemm/external/cutlass' 2025-07-17T07:03:12.2519073Z Entering 'third_party/fbgemm/external/googletest' 2025-07-17T07:03:12.2560141Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-07-17T07:03:12.2600772Z Entering 'third_party/fbgemm/external/json' 2025-07-17T07:03:12.2644607Z Entering 'third_party/flash-attention' 2025-07-17T07:03:12.2687514Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-07-17T07:03:12.2734773Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-07-17T07:03:12.2786243Z Entering 'third_party/flatbuffers' 2025-07-17T07:03:12.2830572Z Entering 'third_party/fmt' 2025-07-17T07:03:12.2872462Z Entering 'third_party/gemmlowp/gemmlowp' 2025-07-17T07:03:12.2915032Z Entering 'third_party/gloo' 2025-07-17T07:03:12.2956649Z Entering 'third_party/googletest' 2025-07-17T07:03:12.2998072Z Entering 'third_party/ideep' 2025-07-17T07:03:12.3040306Z Entering 'third_party/ideep/mkl-dnn' 2025-07-17T07:03:12.3087926Z Entering 'third_party/ittapi' 2025-07-17T07:03:12.3129999Z Entering 'third_party/kineto' 2025-07-17T07:03:12.3171482Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-07-17T07:03:12.3213927Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-07-17T07:03:12.3255448Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-07-17T07:03:12.3297789Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-07-17T07:03:12.3338748Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-07-17T07:03:12.3379087Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-07-17T07:03:12.3422434Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-07-17T07:03:12.3463893Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-07-17T07:03:12.3504555Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-07-17T07:03:12.3548385Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-07-17T07:03:12.3592144Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-07-17T07:03:12.3633223Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-07-17T07:03:12.3677950Z Entering 'third_party/kleidiai' 2025-07-17T07:03:12.3721695Z Entering 'third_party/mimalloc' 2025-07-17T07:03:12.3763427Z Entering 'third_party/nlohmann' 2025-07-17T07:03:12.3808099Z Entering 'third_party/onnx' 2025-07-17T07:03:12.3868418Z Entering 'third_party/onnx/third_party/pybind11' 2025-07-17T07:03:12.3912238Z Entering 'third_party/opentelemetry-cpp' 2025-07-17T07:03:12.3956976Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-07-17T07:03:12.3997656Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-07-17T07:03:12.4038773Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-07-17T07:03:12.4080789Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-07-17T07:03:12.4123296Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-07-17T07:03:12.4163133Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-07-17T07:03:12.4203518Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-07-17T07:03:12.4242886Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-07-17T07:03:12.4285138Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-07-17T07:03:12.4329067Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-07-17T07:03:12.4390023Z Entering 'third_party/pocketfft' 2025-07-17T07:03:12.4432954Z Entering 'third_party/protobuf' 2025-07-17T07:03:12.4478861Z Entering 'third_party/protobuf/third_party/benchmark' 2025-07-17T07:03:12.4518613Z Entering 'third_party/protobuf/third_party/googletest' 2025-07-17T07:03:12.4561156Z Entering 'third_party/psimd' 2025-07-17T07:03:12.4605344Z Entering 'third_party/pthreadpool' 2025-07-17T07:03:12.4647591Z Entering 'third_party/pybind11' 2025-07-17T07:03:12.4690233Z Entering 'third_party/python-peachpy' 2025-07-17T07:03:12.4732066Z Entering 'third_party/sleef' 2025-07-17T07:03:12.4774639Z Entering 'third_party/tensorpipe' 2025-07-17T07:03:12.4816721Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-07-17T07:03:12.4860388Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-07-17T07:03:12.4904362Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-07-17T07:03:12.4945461Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T07:03:12.5002553Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T07:03:12.5057962Z ##[endgroup] 2025-07-17T07:03:12.5093472Z [command]/usr/bin/git log -1 --format=%H 2025-07-17T07:03:12.5116406Z a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:03:12.5306973Z Prepare all required actions 2025-07-17T07:03:12.5307660Z Getting action download info 2025-07-17T07:03:12.6400327Z ##[group]Run ./.github/actions/setup-linux 2025-07-17T07:03:12.6400663Z env: 2025-07-17T07:03:12.6400887Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:12.6401149Z ##[endgroup] 2025-07-17T07:03:12.6443966Z ##[group]Run set -euo pipefail 2025-07-17T07:03:12.6444320Z set -euo pipefail 2025-07-17T07:03:12.6444624Z function get_ec2_metadata() { 2025-07-17T07:03:12.6445004Z  # Pulled from instance metadata endpoint for EC2 2025-07-17T07:03:12.6445657Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-07-17T07:03:12.6446243Z  category=$1 2025-07-17T07:03:12.6446606Z  # If it is GCP runner (runner name contains gcp), do not run this 2025-07-17T07:03:12.6447066Z  runner_name_str=i-075a75ec57b65bb84 2025-07-17T07:03:12.6447480Z  if [[ -f /.inarc ]]; then 2025-07-17T07:03:12.6447844Z  echo "ARC Runner, no info on ec2 metadata" 2025-07-17T07:03:12.6448252Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2025-07-17T07:03:12.6448739Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2025-07-17T07:03:12.6449173Z  else 2025-07-17T07:03:12.6450058Z  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}" 2025-07-17T07:03:12.6451002Z  fi 2025-07-17T07:03:12.6451221Z } 2025-07-17T07:03:12.6451480Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-07-17T07:03:12.6451922Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-07-17T07:03:12.6452417Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-07-17T07:03:12.6452848Z echo "system info $(uname -a)" 2025-07-17T07:03:12.6462575Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:12.6462992Z env: 2025-07-17T07:03:12.6463221Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:12.6463478Z ##[endgroup] 2025-07-17T07:03:12.6628176Z ami-id: ami-05ffe3c48a9991133 2025-07-17T07:03:12.6734312Z instance-id: i-075a75ec57b65bb84 2025-07-17T07:03:12.6835419Z instance-type: c5.4xlarge 2025-07-17T07:03:12.6846649Z system info Linux ip-10-0-72-236.ec2.internal 6.1.141-155.222.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 17 10:29:47 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-07-17T07:03:12.6870622Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-07-17T07:03:12.6871598Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-07-17T07:03:12.6878483Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:12.6878898Z env: 2025-07-17T07:03:12.6879140Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:12.6879400Z ##[endgroup] 2025-07-17T07:03:12.6932469Z ##[group]Run if systemctl is-active --quiet docker; then 2025-07-17T07:03:12.6932933Z if systemctl is-active --quiet docker; then 2025-07-17T07:03:12.6933337Z  echo "Docker daemon is running..."; 2025-07-17T07:03:12.6933661Z else 2025-07-17T07:03:12.6934027Z  echo "Starting docker daemon..." && sudo systemctl start docker; 2025-07-17T07:03:12.6934513Z fi 2025-07-17T07:03:12.6940023Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:12.6940400Z env: 2025-07-17T07:03:12.6940628Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:12.6940894Z ##[endgroup] 2025-07-17T07:03:12.7016959Z Docker daemon is running... 2025-07-17T07:03:12.7098704Z ##[group]Run nick-fields/retry@v3.0.0 2025-07-17T07:03:12.7099354Z with: 2025-07-17T07:03:12.7099790Z shell: bash 2025-07-17T07:03:12.7100492Z timeout_minutes: 5 2025-07-17T07:03:12.7101186Z max_attempts: 3 2025-07-17T07:03:12.7101702Z retry_wait_seconds: 30 2025-07-17T07:03:12.7106942Z command: AWS_ACCOUNT_ID=$(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 2025-07-17T07:03:12.7111576Z polling_interval_seconds: 1 2025-07-17T07:03:12.7112094Z warning_on_retry: true 2025-07-17T07:03:12.7112588Z continue_on_error: false 2025-07-17T07:03:12.7113005Z env: 2025-07-17T07:03:12.7113370Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:12.7113880Z AWS_RETRY_MODE: standard 2025-07-17T07:03:12.7114330Z AWS_MAX_ATTEMPTS: 5 2025-07-17T07:03:12.7114604Z AWS_DEFAULT_REGION: us-east-1 2025-07-17T07:03:12.7114878Z ##[endgroup] 2025-07-17T07:03:13.9087094Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-07-17T07:03:13.9087733Z Configure a credential helper to remove this warning. See 2025-07-17T07:03:13.9088348Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-07-17T07:03:13.9088750Z 2025-07-17T07:03:13.9088840Z Login Succeeded 2025-07-17T07:03:14.8844123Z Command completed after 1 attempt(s). 2025-07-17T07:03:14.8914026Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-07-17T07:03:14.8914585Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-07-17T07:03:14.8915063Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-07-17T07:03:14.8923812Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:14.8924219Z env: 2025-07-17T07:03:14.8924452Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:14.8924707Z ##[endgroup] 2025-07-17T07:03:14.9019208Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-07-17T07:03:14.9019793Z # ignore expansion of "docker ps -q" since it could be empty 2025-07-17T07:03:14.9020238Z # shellcheck disable=SC2046 2025-07-17T07:03:14.9020571Z docker stop $(docker ps -q) || true 2025-07-17T07:03:14.9020931Z # Prune all of the docker images 2025-07-17T07:03:14.9021270Z docker system prune -af 2025-07-17T07:03:14.9026637Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:14.9027026Z env: 2025-07-17T07:03:14.9027252Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:14.9027506Z ##[endgroup] 2025-07-17T07:03:14.9463651Z "docker stop" requires at least 1 argument. 2025-07-17T07:03:14.9464044Z See 'docker stop --help'. 2025-07-17T07:03:14.9464241Z 2025-07-17T07:03:14.9464430Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-07-17T07:03:14.9464722Z 2025-07-17T07:03:14.9464847Z Stop one or more running containers 2025-07-17T07:03:14.9626503Z Total reclaimed space: 0B 2025-07-17T07:03:14.9657653Z ##[group]Run set +e 2025-07-17T07:03:14.9657937Z set +e 2025-07-17T07:03:14.9658178Z set -x 2025-07-17T07:03:14.9658409Z  2025-07-17T07:03:14.9658651Z PT_DOMAIN=download.pytorch.org 2025-07-17T07:03:14.9659258Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2025-07-17T07:03:14.9660051Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2025-07-17T07:03:14.9660608Z # one is returned at random 2025-07-17T07:03:14.9661004Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2025-07-17T07:03:14.9661397Z  2025-07-17T07:03:14.9661796Z if [ -z "${RESOLVED_IP}" ]; then 2025-07-17T07:03:14.9662238Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2025-07-17T07:03:14.9662888Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2025-07-17T07:03:14.9663300Z  2025-07-17T07:03:14.9663547Z  if [ -z "${RESOLVED_IP}" ]; then 2025-07-17T07:03:14.9663936Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2025-07-17T07:03:14.9664313Z  exit 1 2025-07-17T07:03:14.9664551Z  fi 2025-07-17T07:03:14.9664758Z fi 2025-07-17T07:03:14.9664972Z  2025-07-17T07:03:14.9665234Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2025-07-17T07:03:14.9665603Z  # Clean up any old records first 2025-07-17T07:03:14.9665975Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2025-07-17T07:03:14.9666311Z fi 2025-07-17T07:03:14.9666523Z  2025-07-17T07:03:14.9666833Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2025-07-17T07:03:14.9667247Z cat /etc/hosts 2025-07-17T07:03:14.9673385Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:14.9673761Z env: 2025-07-17T07:03:14.9673983Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:14.9674251Z ##[endgroup] 2025-07-17T07:03:14.9696584Z + PT_DOMAIN=download.pytorch.org 2025-07-17T07:03:14.9701865Z ++ dig -4 +short download.pytorch.org 2025-07-17T07:03:14.9702656Z ++ tail -n1 2025-07-17T07:03:15.0395023Z + RESOLVED_IP=18.160.10.28 2025-07-17T07:03:15.0395394Z + '[' -z 18.160.10.28 ']' 2025-07-17T07:03:15.0395688Z + grep -r download.pytorch.org /etc/hosts 2025-07-17T07:03:15.0409703Z + echo '18.160.10.28 download.pytorch.org' 2025-07-17T07:03:15.0410305Z + sudo tee -a /etc/hosts 2025-07-17T07:03:15.4137681Z 18.160.10.28 download.pytorch.org 2025-07-17T07:03:15.4158997Z + cat /etc/hosts 2025-07-17T07:03:15.4167967Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2025-07-17T07:03:15.4174105Z ::1 localhost6 localhost6.localdomain6 2025-07-17T07:03:15.4174514Z 18.160.10.28 download.pytorch.org 2025-07-17T07:03:15.4350341Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-07-17T07:03:15.4350820Z with: 2025-07-17T07:03:15.4351582Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:15.4352455Z use-custom-docker-registry: true 2025-07-17T07:03:15.4352782Z docker-build-dir: .ci/docker 2025-07-17T07:03:15.4353074Z docker-build-script: ./build.sh 2025-07-17T07:03:15.4353380Z working-directory: . 2025-07-17T07:03:15.4353740Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:15.4354139Z force-push: false 2025-07-17T07:03:15.4354375Z env: 2025-07-17T07:03:15.4354595Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:15.4354849Z ##[endgroup] 2025-07-17T07:03:15.4377907Z ##[group]Run set -ex 2025-07-17T07:03:15.4378224Z set -ex 2025-07-17T07:03:15.4378477Z  2025-07-17T07:03:15.4378930Z # If the docker build directory or the build script doesn't exist, the action will 2025-07-17T07:03:15.4379622Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-07-17T07:03:15.4380201Z # job could then download the pre-built image as usual 2025-07-17T07:03:15.4380921Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-07-17T07:03:15.4381600Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4381930Z else 2025-07-17T07:03:15.4382200Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4382667Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4383089Z  2025-07-17T07:03:15.4383650Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-07-17T07:03:15.4384466Z  exit 0 2025-07-17T07:03:15.4384695Z fi 2025-07-17T07:03:15.4384898Z  2025-07-17T07:03:15.4385250Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-07-17T07:03:15.4385875Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-07-17T07:03:15.4386438Z  # use it as it is, but first let's extract the tag 2025-07-17T07:03:15.4386927Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-07-17T07:03:15.4387458Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4387964Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4388386Z else 2025-07-17T07:03:15.4388645Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-07-17T07:03:15.4389043Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-07-17T07:03:15.4389460Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-07-17T07:03:15.4389795Z  fi 2025-07-17T07:03:15.4390265Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-07-17T07:03:15.4390965Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4391667Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4392401Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4392861Z fi 2025-07-17T07:03:15.4399673Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:15.4400066Z env: 2025-07-17T07:03:15.4400289Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:15.4400550Z REPO_NAME: pytorch 2025-07-17T07:03:15.4401473Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:15.4402338Z DOCKER_BUILD_DIR: .ci/docker 2025-07-17T07:03:15.4402636Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-07-17T07:03:15.4403017Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:15.4403434Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-07-17T07:03:15.4403738Z CUSTOM_TAG_PREFIX: 2025-07-17T07:03:15.4403974Z ##[endgroup] 2025-07-17T07:03:15.4429622Z + [[ -d .ci/docker ]] 2025-07-17T07:03:15.4430116Z + [[ -f .ci/docker/./build.sh ]] 2025-07-17T07:03:15.4430505Z + [[ true == \t\r\u\e ]] 2025-07-17T07:03:15.4430961Z + echo skip=false 2025-07-17T07:03:15.4432207Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d == *\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* ]] 2025-07-17T07:03:15.4437281Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:15.4438310Z ++ awk -F '[:,]' '{print $2}' 2025-07-17T07:03:15.4458163Z + DOCKER_TAG=pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:15.4459348Z + echo docker-tag=pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:15.4460562Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:15.4491158Z ##[group]Run set +e 2025-07-17T07:03:15.4491465Z set +e 2025-07-17T07:03:15.4491701Z set -x 2025-07-17T07:03:15.4491926Z  2025-07-17T07:03:15.4492127Z login() { 2025-07-17T07:03:15.4492623Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-07-17T07:03:15.4493164Z } 2025-07-17T07:03:15.4493499Z  2025-07-17T07:03:15.4493721Z retry () { 2025-07-17T07:03:15.4494000Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-07-17T07:03:15.4494312Z } 2025-07-17T07:03:15.4494526Z  2025-07-17T07:03:15.4494766Z retry login "${DOCKER_REGISTRY}" 2025-07-17T07:03:15.4495083Z  2025-07-17T07:03:15.4495404Z START_TIME=$(date +%s) 2025-07-17T07:03:15.4495714Z # Wait up to 120 minutes 2025-07-17T07:03:15.4496092Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-07-17T07:03:15.4496585Z  # Check if image already exists, if it does then skip building it 2025-07-17T07:03:15.4497103Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-07-17T07:03:15.4497475Z  exit 0 2025-07-17T07:03:15.4497701Z  fi 2025-07-17T07:03:15.4497919Z  2025-07-17T07:03:15.4498313Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-07-17T07:03:15.4499019Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-07-17T07:03:15.4499696Z  # latter, it will wait for the Docker images to become available before continuing 2025-07-17T07:03:15.4500238Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-07-17T07:03:15.4500653Z  # It's a Docker build job, let's build the image 2025-07-17T07:03:15.4501014Z  break 2025-07-17T07:03:15.4501255Z  else 2025-07-17T07:03:15.4501594Z  # It's a regular build job, wait for the image to become available 2025-07-17T07:03:15.4502027Z  sleep 300 2025-07-17T07:03:15.4502282Z  fi 2025-07-17T07:03:15.4502506Z done 2025-07-17T07:03:15.4502716Z  2025-07-17T07:03:15.4503082Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-07-17T07:03:15.4503681Z # be empty. The default action would be to continue rebuild the image 2025-07-17T07:03:15.4504319Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-07-17T07:03:15.4504795Z  # if we're on the base branch then use the parent commit 2025-07-17T07:03:15.4505220Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-07-17T07:03:15.4505550Z else 2025-07-17T07:03:15.4505878Z  # otherwise we're on a PR, so use the most recent base commit 2025-07-17T07:03:15.4506384Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-07-17T07:03:15.4506763Z fi 2025-07-17T07:03:15.4506972Z  2025-07-17T07:03:15.4507211Z if [[ -z "${MERGE_BASE}" ]]; then 2025-07-17T07:03:15.4507585Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4507916Z  2025-07-17T07:03:15.4508400Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-07-17T07:03:15.4508972Z  exit 0 2025-07-17T07:03:15.4509205Z fi 2025-07-17T07:03:15.4509413Z  2025-07-17T07:03:15.4509731Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-07-17T07:03:15.4510440Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-07-17T07:03:15.4511035Z  exit 1 2025-07-17T07:03:15.4511263Z fi 2025-07-17T07:03:15.4511476Z  2025-07-17T07:03:15.4511850Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-07-17T07:03:15.4512522Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-07-17T07:03:15.4513132Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-07-17T07:03:15.4513849Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-07-17T07:03:15.4514652Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-07-17T07:03:15.4515180Z fi 2025-07-17T07:03:15.4515404Z  2025-07-17T07:03:15.4515674Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-07-17T07:03:15.4521229Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:15.4521668Z env: 2025-07-17T07:03:15.4521895Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:15.4522211Z DOCKER_BUILD_DIR: .ci/docker 2025-07-17T07:03:15.4522562Z BASE_REVISION: a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:03:15.4523445Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:15.4524490Z DOCKER_TAG: pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:15.4525114Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:15.4525515Z DOCKER_PUSH: 2025-07-17T07:03:15.4525757Z ##[endgroup] 2025-07-17T07:03:15.4548001Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:15.4548455Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:15.4550569Z + aws ecr get-login-password --region us-east-1 2025-07-17T07:03:15.4551940Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:15.9555837Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-07-17T07:03:15.9556472Z Configure a credential helper to remove this warning. See 2025-07-17T07:03:15.9557073Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-07-17T07:03:15.9557497Z 2025-07-17T07:03:15.9557592Z Login Succeeded 2025-07-17T07:03:15.9570152Z ++ date +%s 2025-07-17T07:03:15.9580673Z + START_TIME=1752735795 2025-07-17T07:03:15.9583814Z ++ date +%s 2025-07-17T07:03:15.9593662Z + [[ 1752728595 -lt 1752735795 ]] 2025-07-17T07:03:15.9594815Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:16.1731488Z { 2025-07-17T07:03:16.1731828Z "schemaVersion": 2, 2025-07-17T07:03:16.1732340Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-07-17T07:03:16.1732800Z "config": { 2025-07-17T07:03:16.1733148Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-07-17T07:03:16.1733554Z "size": 29789, 2025-07-17T07:03:16.1733990Z "digest": "sha256:fc552ac054088d2469fbdea029aa8fa6904bc0b5b71dbfece3084d62acbe009f" 2025-07-17T07:03:16.1734488Z }, 2025-07-17T07:03:16.1734678Z "layers": [ 2025-07-17T07:03:16.1734898Z { 2025-07-17T07:03:16.1735293Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1735732Z "size": 30446997, 2025-07-17T07:03:16.1736148Z "digest": "sha256:66587c81b81a58d07e40c48d900a1517516bbf58c4378c687d89d645824f5e5f" 2025-07-17T07:03:16.1736653Z }, 2025-07-17T07:03:16.1736850Z { 2025-07-17T07:03:16.1737188Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1737621Z "size": 1554, 2025-07-17T07:03:16.1738035Z "digest": "sha256:7a5c9899d937455e8a64ab35b18396a208220f81db25272105fbafaa8794bc2e" 2025-07-17T07:03:16.1738517Z }, 2025-07-17T07:03:16.1738711Z { 2025-07-17T07:03:16.1739047Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1739461Z "size": 343996331, 2025-07-17T07:03:16.1739888Z "digest": 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"sha256:72f42db112acff3fe786e412852e40172547e2f0805d8bb3165250351607bcc6" 2025-07-17T07:03:16.1822823Z }, 2025-07-17T07:03:16.1823004Z { 2025-07-17T07:03:16.1823341Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1823766Z "size": 134, 2025-07-17T07:03:16.1824163Z "digest": "sha256:7c952e9bce00315c28853ab1776128c841cf6569a57847f459cba91f5a8acc8e" 2025-07-17T07:03:16.1824635Z }, 2025-07-17T07:03:16.1824829Z { 2025-07-17T07:03:16.1825149Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1825575Z "size": 32, 2025-07-17T07:03:16.1825991Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T07:03:16.1826467Z }, 2025-07-17T07:03:16.1826661Z { 2025-07-17T07:03:16.1826995Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1827423Z "size": 195, 2025-07-17T07:03:16.1827968Z "digest": "sha256:3b1502de0f3b198fb39afb20836194ba99700dc67ce7ad4c243e568453198d25" 2025-07-17T07:03:16.1828452Z }, 2025-07-17T07:03:16.1828647Z { 2025-07-17T07:03:16.1828962Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1829387Z "size": 116, 2025-07-17T07:03:16.1829799Z "digest": "sha256:173b45b41ce0878e815890c196d343fc210775ebe229cedf931b07f2bc460e27" 2025-07-17T07:03:16.1830266Z }, 2025-07-17T07:03:16.1830459Z { 2025-07-17T07:03:16.1830788Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1831201Z "size": 32, 2025-07-17T07:03:16.1831618Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T07:03:16.1832110Z }, 2025-07-17T07:03:16.1832290Z { 2025-07-17T07:03:16.1832620Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1833044Z "size": 128, 2025-07-17T07:03:16.1833455Z "digest": "sha256:50399975739a3670b93a14d30c63319a2d88a9f69f88c53da9233122cd897cfc" 2025-07-17T07:03:16.1833922Z }, 2025-07-17T07:03:16.1834118Z { 2025-07-17T07:03:16.1834452Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1834865Z "size": 777, 2025-07-17T07:03:16.1835290Z "digest": "sha256:34b7cc5cf1d9abe64f6305deb3656733eaf90f26eaa3f85cfbb2c9dd7e043e3d" 2025-07-17T07:03:16.1835783Z }, 2025-07-17T07:03:16.1836009Z { 2025-07-17T07:03:16.1836399Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1836826Z "size": 724, 2025-07-17T07:03:16.1837221Z "digest": "sha256:c3fd79404829cd24625740ba8c0595c401878e558aa17f440e34f121dd6facdf" 2025-07-17T07:03:16.1837697Z }, 2025-07-17T07:03:16.1837894Z { 2025-07-17T07:03:16.1838212Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1838642Z "size": 141, 2025-07-17T07:03:16.1839074Z "digest": "sha256:dffea587205ddaa02cc6d95ddd5de1d6dbf175b6a2dbc7dbe952b096fe96ba62" 2025-07-17T07:03:16.1839576Z }, 2025-07-17T07:03:16.1839864Z { 2025-07-17T07:03:16.1840200Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1840627Z "size": 32, 2025-07-17T07:03:16.1841034Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T07:03:16.1841523Z }, 2025-07-17T07:03:16.1841717Z { 2025-07-17T07:03:16.1842035Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1842459Z "size": 160, 2025-07-17T07:03:16.1842878Z "digest": "sha256:8d04c6fef71e3cf9be0786633cdb12ad20e580a221f50d7fa1fa5d30f3687514" 2025-07-17T07:03:16.1843351Z }, 2025-07-17T07:03:16.1843547Z { 2025-07-17T07:03:16.1843878Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1844305Z "size": 1012, 2025-07-17T07:03:16.1844719Z "digest": "sha256:56cdce5d93425b58fcc507ea1c93c64ca9a646f16afacc7d86ed7a3b54c06a2b" 2025-07-17T07:03:16.1845216Z }, 2025-07-17T07:03:16.1845416Z { 2025-07-17T07:03:16.1845739Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1846166Z "size": 724, 2025-07-17T07:03:16.1846579Z "digest": "sha256:c3fd79404829cd24625740ba8c0595c401878e558aa17f440e34f121dd6facdf" 2025-07-17T07:03:16.1847041Z }, 2025-07-17T07:03:16.1847237Z { 2025-07-17T07:03:16.1847571Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1847986Z "size": 134, 2025-07-17T07:03:16.1848403Z "digest": "sha256:060e0a2319133e77ebf1b7b23c81602e9b9d2c25f7f9ee43fde89b7a6a645d7a" 2025-07-17T07:03:16.1848890Z }, 2025-07-17T07:03:16.1849073Z { 2025-07-17T07:03:16.1849405Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1849832Z "size": 32, 2025-07-17T07:03:16.1850249Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T07:03:16.1850723Z }, 2025-07-17T07:03:16.1850918Z { 2025-07-17T07:03:16.1851319Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1851736Z "size": 159, 2025-07-17T07:03:16.1852163Z "digest": "sha256:3c6a2d65b5bbb8f68cf3618a693f7fb4accbe30e3aa5786873365b3eed8b8b1b" 2025-07-17T07:03:16.1852658Z }, 2025-07-17T07:03:16.1852842Z { 2025-07-17T07:03:16.1853176Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1853599Z "size": 1370, 2025-07-17T07:03:16.1853995Z "digest": "sha256:a7015d1f229ed3837db36de6f01b45e34074b67f082934d1609845314e238350" 2025-07-17T07:03:16.1854470Z }, 2025-07-17T07:03:16.1854665Z { 2025-07-17T07:03:16.1854982Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1855501Z "size": 32, 2025-07-17T07:03:16.1855923Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T07:03:16.1856414Z }, 2025-07-17T07:03:16.1856597Z { 2025-07-17T07:03:16.1856930Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1857360Z "size": 136, 2025-07-17T07:03:16.1857766Z "digest": "sha256:0ddb9567cfa96bb304d84caed95336e9802f53dc71b7b2255a1d8f6612fc6455" 2025-07-17T07:03:16.1858251Z }, 2025-07-17T07:03:16.1858444Z { 2025-07-17T07:03:16.1858761Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1859186Z "size": 380, 2025-07-17T07:03:16.1859602Z "digest": "sha256:08123ea660cb56e40bf8cfe8ddc3856fc67ee90548bba0b2f37f7b56a36785b5" 2025-07-17T07:03:16.1860071Z }, 2025-07-17T07:03:16.1860264Z { 2025-07-17T07:03:16.1860595Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1861016Z "size": 32, 2025-07-17T07:03:16.1861418Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T07:03:16.1861900Z }, 2025-07-17T07:03:16.1862093Z { 2025-07-17T07:03:16.1862410Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1862838Z "size": 104, 2025-07-17T07:03:16.1863317Z "digest": "sha256:06f07ca145d9a61b91372346682b9c0d458fe83ef704153799346502349aafe6" 2025-07-17T07:03:16.1863779Z }, 2025-07-17T07:03:16.1863978Z { 2025-07-17T07:03:16.1864313Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1864729Z "size": 407, 2025-07-17T07:03:16.1865159Z "digest": "sha256:a151adebebbf7bcaa2e64b35305f5def7cde3f60b683da7b9150e0794d72b355" 2025-07-17T07:03:16.1865661Z }, 2025-07-17T07:03:16.1865844Z { 2025-07-17T07:03:16.1866174Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1866600Z "size": 32, 2025-07-17T07:03:16.1867017Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T07:03:16.1867495Z }, 2025-07-17T07:03:16.1867690Z { 2025-07-17T07:03:16.1868018Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1868434Z "size": 109, 2025-07-17T07:03:16.1868856Z "digest": "sha256:a567163437c52a32d717b879e4e67e0d26700afb8fb1dcc6adf07cce1f378541" 2025-07-17T07:03:16.1869339Z }, 2025-07-17T07:03:16.1869521Z { 2025-07-17T07:03:16.1869856Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1870284Z "size": 1897, 2025-07-17T07:03:16.1870682Z "digest": "sha256:f70d840f762475b3bdb839fa2820789f2283b17049693adfd5bd8a28111a1636" 2025-07-17T07:03:16.1871160Z }, 2025-07-17T07:03:16.1871354Z { 2025-07-17T07:03:16.1871673Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1872284Z "size": 241722875, 2025-07-17T07:03:16.1872732Z "digest": "sha256:d7369ebc3a9abb1dfd0315f8d8b2acfe67465121ec4d4ca53a3912de4666d7b4" 2025-07-17T07:03:16.1873227Z }, 2025-07-17T07:03:16.1873410Z { 2025-07-17T07:03:16.1873743Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1874174Z "size": 106, 2025-07-17T07:03:16.1874579Z "digest": "sha256:4184c6a7fd64e170b5f6feff80108032071e259411230ad9b6285c51c16b3b1c" 2025-07-17T07:03:16.1875182Z }, 2025-07-17T07:03:16.1875377Z { 2025-07-17T07:03:16.1875698Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1876121Z "size": 165, 2025-07-17T07:03:16.1876530Z "digest": "sha256:22ddb070c1113f484657d52b520abe86a237739eb1e504061ac0919605df0563" 2025-07-17T07:03:16.1876989Z }, 2025-07-17T07:03:16.1877186Z { 2025-07-17T07:03:16.1877518Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1877940Z "size": 7944, 2025-07-17T07:03:16.1878350Z "digest": "sha256:e4b9b40818eba0d7a5c5dbbc6bf60a852ba56250ac95c5bd47336b812004f2d6" 2025-07-17T07:03:16.1878835Z }, 2025-07-17T07:03:16.1879013Z { 2025-07-17T07:03:16.1879340Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1879758Z "size": 8069, 2025-07-17T07:03:16.1880155Z "digest": "sha256:403c8a0d600099f8231b52cb057d5b98607787dda95ccbca1b9228642002e7e5" 2025-07-17T07:03:16.1880644Z }, 2025-07-17T07:03:16.1880832Z { 2025-07-17T07:03:16.1881146Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1881566Z "size": 303, 2025-07-17T07:03:16.1881968Z "digest": "sha256:6ce29e0f73ef2e1778e2252d8da8392c2bd06903f31860fbf08d26a8ca17c76e" 2025-07-17T07:03:16.1882435Z }, 2025-07-17T07:03:16.1882615Z { 2025-07-17T07:03:16.1882937Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1883356Z "size": 32, 2025-07-17T07:03:16.1883761Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T07:03:16.1884251Z }, 2025-07-17T07:03:16.1884437Z { 2025-07-17T07:03:16.1884754Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1885172Z "size": 108, 2025-07-17T07:03:16.1885579Z "digest": "sha256:8341c69d63c87382777fa1e7dfc5f846cc3428ee64adae6d44a82d1bed0efc49" 2025-07-17T07:03:16.1886048Z }, 2025-07-17T07:03:16.1886235Z { 2025-07-17T07:03:16.1886655Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1887072Z "size": 54145699, 2025-07-17T07:03:16.1887479Z "digest": "sha256:9fb68e35f66fa77f535422964bc64a23cbd493353936c0fc95a895e200cdfbfb" 2025-07-17T07:03:16.1887944Z }, 2025-07-17T07:03:16.1888125Z { 2025-07-17T07:03:16.1888438Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-07-17T07:03:16.1888845Z "size": 32, 2025-07-17T07:03:16.1889250Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-07-17T07:03:16.1889722Z } 2025-07-17T07:03:16.1889907Z ] 2025-07-17T07:03:16.1890092Z } 2025-07-17T07:03:16.1919436Z ##[group]Run set -eux 2025-07-17T07:03:16.1919718Z set -eux 2025-07-17T07:03:16.1920622Z 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 2025-07-17T07:03:16.1928215Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:16.1928587Z env: 2025-07-17T07:03:16.1928804Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:16.1929069Z ##[endgroup] 2025-07-17T07:03:16.1957052Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-07-17T07:03:16.1957603Z + jq --raw-output .SecretString 2025-07-17T07:03:16.1958748Z + jq -r .docker_hub_readonly_token 2025-07-17T07:03:16.1959942Z + docker login --username pytorchbot --password-stdin 2025-07-17T07:03:16.7729203Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-07-17T07:03:16.7729849Z Configure a credential helper to remove this warning. See 2025-07-17T07:03:16.7730441Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-07-17T07:03:16.7730856Z 2025-07-17T07:03:16.7753855Z Login Succeeded 2025-07-17T07:03:16.7845023Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*:} 2025-07-17T07:03:16.7845547Z tag=${ECR_DOCKER_IMAGE##*:} 2025-07-17T07:03:16.7845948Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2025-07-17T07:03:16.7851821Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:16.7852211Z env: 2025-07-17T07:03:16.7852424Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:16.7853219Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:16.7854051Z ##[endgroup] 2025-07-17T07:03:16.7880178Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:16.7933245Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-07-17T07:03:16.7933704Z with: 2025-07-17T07:03:16.7934437Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:16.7935469Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:16.7935871Z env: 2025-07-17T07:03:16.7936093Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:16.7936349Z ##[endgroup] 2025-07-17T07:03:16.7959196Z ##[group]Run set -x 2025-07-17T07:03:16.7959474Z set -x 2025-07-17T07:03:16.7959712Z set +e 2025-07-17T07:03:16.7959942Z  2025-07-17T07:03:16.7960147Z login() { 2025-07-17T07:03:16.7960641Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-07-17T07:03:16.7961182Z } 2025-07-17T07:03:16.7961381Z  2025-07-17T07:03:16.7961655Z retry () { 2025-07-17T07:03:16.7961991Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-07-17T07:03:16.7962430Z } 2025-07-17T07:03:16.7962719Z  2025-07-17T07:03:16.7963048Z retry login "${DOCKER_REGISTRY}" 2025-07-17T07:03:16.7963420Z  2025-07-17T07:03:16.7963936Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-07-17T07:03:16.7964621Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-07-17T07:03:16.7965005Z  2025-07-17T07:03:16.7965220Z set -e 2025-07-17T07:03:16.7965575Z # ignore output since only exit code is used for conditional 2025-07-17T07:03:16.7966075Z # only pull docker image if it's not available locally 2025-07-17T07:03:16.7966640Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-07-17T07:03:16.7967171Z  retry docker pull "${DOCKER_IMAGE}" 2025-07-17T07:03:16.7967504Z fi 2025-07-17T07:03:16.7972867Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:03:16.7973259Z env: 2025-07-17T07:03:16.7973491Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:03:16.7974270Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:16.7975268Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:16.7975673Z ##[endgroup] 2025-07-17T07:03:16.7997162Z + set +e 2025-07-17T07:03:16.7997755Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:16.7998245Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:16.8000554Z + aws ecr get-login-password --region us-east-1 2025-07-17T07:03:16.8001711Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-07-17T07:03:17.3127516Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-07-17T07:03:17.3128163Z Configure a credential helper to remove this warning. See 2025-07-17T07:03:17.3128746Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-07-17T07:03:17.3129163Z 2025-07-17T07:03:17.3129264Z Login Succeeded 2025-07-17T07:03:17.3145684Z ++ docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:17.3146635Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-07-17T07:03:17.5277687Z + IMAGE_SIZE=4469.723728179932 2025-07-17T07:03:17.5278090Z + echo 'Compressed size of image in MB: 4469.723728179932' 2025-07-17T07:03:17.5278463Z + set -e 2025-07-17T07:03:17.5278736Z Compressed size of image in MB: 4469.723728179932 2025-07-17T07:03:17.5279873Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:17.5401028Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:17.5402389Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:03:17.7585368Z pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d: Pulling from pytorch/ci-image 2025-07-17T07:03:17.7586437Z 66587c81b81a: Pulling fs layer 2025-07-17T07:03:17.7586726Z 7a5c9899d937: Pulling fs layer 2025-07-17T07:03:17.7587203Z 9d8fe31f4371: Pulling fs layer 2025-07-17T07:03:17.7587658Z 4ea72d4d995b: Pulling fs layer 2025-07-17T07:03:17.7588035Z 0b21d3c7919b: Pulling fs layer 2025-07-17T07:03:17.7588502Z ff0141e78db7: Pulling fs layer 2025-07-17T07:03:17.7588915Z a8aa8a9fa61b: Pulling fs layer 2025-07-17T07:03:17.7589210Z a1030ae92635: Pulling fs layer 2025-07-17T07:03:17.7589508Z 1bd9396864e4: Pulling fs layer 2025-07-17T07:03:17.7589797Z e80da71796c5: Pulling fs layer 2025-07-17T07:03:17.7590083Z c0c195959436: Pulling fs layer 2025-07-17T07:03:17.7590355Z c3fd79404829: Pulling fs layer 2025-07-17T07:03:17.7590648Z 6f5ba5923d9e: Pulling fs layer 2025-07-17T07:03:17.7590960Z cc5ad5bb5c8f: Pulling fs layer 2025-07-17T07:03:17.7591238Z 4f4fb700ef54: Pulling fs layer 2025-07-17T07:03:17.7591518Z a8aa8a9fa61b: Waiting 2025-07-17T07:03:17.7591780Z 287d90495ed7: Pulling fs layer 2025-07-17T07:03:17.7592056Z e1da75f4dceb: Pulling fs layer 2025-07-17T07:03:17.7592352Z dbbbaf55f1bc: Pulling fs layer 2025-07-17T07:03:17.7592647Z b5993da49da4: Pulling fs layer 2025-07-17T07:03:17.7592936Z 13afcd2a66bb: Pulling fs layer 2025-07-17T07:03:17.7593213Z 58af60b83765: Pulling fs layer 2025-07-17T07:03:17.7593490Z ff0141e78db7: Waiting 2025-07-17T07:03:17.7593749Z 696aad357da7: Pulling fs layer 2025-07-17T07:03:17.7594051Z e5249a336770: Pulling fs layer 2025-07-17T07:03:17.7594492Z 8d76da75d1af: Pulling fs layer 2025-07-17T07:03:17.7594787Z 95145717af0c: Pulling fs layer 2025-07-17T07:03:17.7595063Z f66e02cad216: Pulling fs layer 2025-07-17T07:03:17.7595357Z 23bfc6bfeaf4: Pulling fs layer 2025-07-17T07:03:17.7595647Z 714df0485f2f: Pulling fs layer 2025-07-17T07:03:17.7595924Z 4fe817dc2597: Pulling fs layer 2025-07-17T07:03:17.7596219Z 8b772aad6dc1: Pulling fs layer 2025-07-17T07:03:17.7596524Z 5eec907873a0: Pulling fs layer 2025-07-17T07:03:17.7596925Z f5b31abb16c4: Pulling fs layer 2025-07-17T07:03:17.7597199Z da73c7805e85: Pulling fs layer 2025-07-17T07:03:17.7597543Z a9fc0454e936: Pulling fs layer 2025-07-17T07:03:17.7597830Z fdda510353fb: Pulling fs layer 2025-07-17T07:03:17.7598154Z 72f42db112ac: Pulling fs layer 2025-07-17T07:03:17.7598438Z 7c952e9bce00: Pulling fs layer 2025-07-17T07:03:17.7598724Z 3b1502de0f3b: Pulling fs layer 2025-07-17T07:03:17.7598997Z 173b45b41ce0: Pulling fs layer 2025-07-17T07:03:17.7599272Z 4ea72d4d995b: Waiting 2025-07-17T07:03:17.7599545Z 50399975739a: Pulling fs layer 2025-07-17T07:03:17.7599818Z 34b7cc5cf1d9: Pulling fs layer 2025-07-17T07:03:17.7600112Z dffea587205d: Pulling fs layer 2025-07-17T07:03:17.7600403Z 8d04c6fef71e: Pulling fs layer 2025-07-17T07:03:17.7600684Z 56cdce5d9342: Pulling fs layer 2025-07-17T07:03:17.7600974Z 060e0a231913: Pulling fs layer 2025-07-17T07:03:17.7601454Z 3c6a2d65b5bb: Pulling fs layer 2025-07-17T07:03:17.7601741Z a7015d1f229e: Pulling fs layer 2025-07-17T07:03:17.7602016Z 0ddb9567cfa9: Pulling fs layer 2025-07-17T07:03:17.7602302Z 08123ea660cb: Pulling fs layer 2025-07-17T07:03:17.7602588Z 06f07ca145d9: Pulling fs layer 2025-07-17T07:03:17.7602864Z a151adebebbf: Pulling fs layer 2025-07-17T07:03:17.7603155Z a567163437c5: Pulling fs layer 2025-07-17T07:03:17.7603438Z f70d840f7624: Pulling fs layer 2025-07-17T07:03:17.7603712Z d7369ebc3a9a: Pulling fs layer 2025-07-17T07:03:17.7603998Z 4184c6a7fd64: Pulling fs layer 2025-07-17T07:03:17.7604312Z 22ddb070c111: Pulling fs layer 2025-07-17T07:03:17.7604722Z e4b9b40818eb: Pulling fs layer 2025-07-17T07:03:17.7605003Z 403c8a0d6000: Pulling fs layer 2025-07-17T07:03:17.7605297Z 6ce29e0f73ef: Pulling fs layer 2025-07-17T07:03:17.7605587Z 8341c69d63c8: Pulling fs layer 2025-07-17T07:03:17.7605863Z 9fb68e35f66f: Pulling fs layer 2025-07-17T07:03:17.7606144Z 58af60b83765: Waiting 2025-07-17T07:03:17.7606399Z 696aad357da7: Waiting 2025-07-17T07:03:17.7606628Z 0b21d3c7919b: Waiting 2025-07-17T07:03:17.7606869Z a1030ae92635: Waiting 2025-07-17T07:03:17.7607108Z 6f5ba5923d9e: Waiting 2025-07-17T07:03:17.7607336Z 8d76da75d1af: Waiting 2025-07-17T07:03:17.7607584Z dbbbaf55f1bc: Waiting 2025-07-17T07:03:17.7607828Z 8d04c6fef71e: Waiting 2025-07-17T07:03:17.7608057Z 95145717af0c: Waiting 2025-07-17T07:03:17.7608298Z cc5ad5bb5c8f: Waiting 2025-07-17T07:03:17.7608547Z e1da75f4dceb: Waiting 2025-07-17T07:03:17.7608780Z 1bd9396864e4: Waiting 2025-07-17T07:03:17.7609019Z 0ddb9567cfa9: Waiting 2025-07-17T07:03:17.7609262Z e80da71796c5: Waiting 2025-07-17T07:03:17.7609491Z 08123ea660cb: Waiting 2025-07-17T07:03:17.7609734Z 72f42db112ac: Waiting 2025-07-17T07:03:17.7609977Z 4f4fb700ef54: Waiting 2025-07-17T07:03:17.7610216Z 060e0a231913: Waiting 2025-07-17T07:03:17.7610444Z 56cdce5d9342: Waiting 2025-07-17T07:03:17.7610683Z 287d90495ed7: Waiting 2025-07-17T07:03:17.7610921Z 3c6a2d65b5bb: Waiting 2025-07-17T07:03:17.7611152Z e5249a336770: Waiting 2025-07-17T07:03:17.7611393Z 13afcd2a66bb: Waiting 2025-07-17T07:03:17.7611638Z 23bfc6bfeaf4: Waiting 2025-07-17T07:03:17.7611894Z 173b45b41ce0: Waiting 2025-07-17T07:03:17.7612262Z f70d840f7624: Waiting 2025-07-17T07:03:17.7612654Z 3b1502de0f3b: Waiting 2025-07-17T07:03:17.7613043Z c0c195959436: Waiting 2025-07-17T07:03:17.7613462Z c3fd79404829: Waiting 2025-07-17T07:03:17.7613897Z d7369ebc3a9a: Waiting 2025-07-17T07:03:17.7614300Z 714df0485f2f: Waiting 2025-07-17T07:03:17.7614677Z 4fe817dc2597: Waiting 2025-07-17T07:03:17.7615020Z 34b7cc5cf1d9: Waiting 2025-07-17T07:03:17.7615319Z 4184c6a7fd64: Waiting 2025-07-17T07:03:17.7615625Z 8b772aad6dc1: Waiting 2025-07-17T07:03:17.7615883Z 22ddb070c111: Waiting 2025-07-17T07:03:17.7616112Z 5eec907873a0: Waiting 2025-07-17T07:03:17.7616357Z f5b31abb16c4: Waiting 2025-07-17T07:03:17.7616599Z a9fc0454e936: Waiting 2025-07-17T07:03:17.7616832Z da73c7805e85: Waiting 2025-07-17T07:03:17.7617082Z fdda510353fb: Waiting 2025-07-17T07:03:17.7617339Z dffea587205d: Waiting 2025-07-17T07:03:17.7617567Z 50399975739a: Waiting 2025-07-17T07:03:17.7617806Z 9fb68e35f66f: Waiting 2025-07-17T07:03:17.7618054Z a567163437c5: Waiting 2025-07-17T07:03:17.7618295Z e4b9b40818eb: Waiting 2025-07-17T07:03:17.7618539Z 8341c69d63c8: Waiting 2025-07-17T07:03:17.7618770Z 403c8a0d6000: Waiting 2025-07-17T07:03:17.7619016Z 6ce29e0f73ef: Waiting 2025-07-17T07:03:17.7619263Z a7015d1f229e: Waiting 2025-07-17T07:03:17.7619496Z f66e02cad216: Waiting 2025-07-17T07:03:17.7619746Z 06f07ca145d9: Waiting 2025-07-17T07:03:17.7619997Z a151adebebbf: Waiting 2025-07-17T07:03:17.7620247Z 7c952e9bce00: Waiting 2025-07-17T07:03:17.8400241Z 7a5c9899d937: Verifying Checksum 2025-07-17T07:03:17.8400785Z 7a5c9899d937: Download complete 2025-07-17T07:03:17.9368356Z 4ea72d4d995b: Verifying Checksum 2025-07-17T07:03:17.9368860Z 4ea72d4d995b: Download complete 2025-07-17T07:03:18.1118052Z 66587c81b81a: Download complete 2025-07-17T07:03:18.1924448Z ff0141e78db7: Verifying Checksum 2025-07-17T07:03:18.1925066Z ff0141e78db7: Download complete 2025-07-17T07:03:18.2823980Z a8aa8a9fa61b: Verifying Checksum 2025-07-17T07:03:18.2824368Z a8aa8a9fa61b: Download complete 2025-07-17T07:03:18.3399429Z a1030ae92635: Verifying Checksum 2025-07-17T07:03:18.3399771Z a1030ae92635: Download complete 2025-07-17T07:03:18.4304098Z 1bd9396864e4: Verifying Checksum 2025-07-17T07:03:18.4304551Z 1bd9396864e4: Download complete 2025-07-17T07:03:18.5024704Z e80da71796c5: Download complete 2025-07-17T07:03:18.6226519Z c0c195959436: Download complete 2025-07-17T07:03:18.6397669Z 0b21d3c7919b: Verifying Checksum 2025-07-17T07:03:18.6398233Z 0b21d3c7919b: Download complete 2025-07-17T07:03:18.6851610Z c3fd79404829: Verifying Checksum 2025-07-17T07:03:18.6852200Z c3fd79404829: Download complete 2025-07-17T07:03:18.7377059Z 6f5ba5923d9e: Verifying Checksum 2025-07-17T07:03:18.7377419Z 6f5ba5923d9e: Download complete 2025-07-17T07:03:18.7469167Z 4f4fb700ef54: Download complete 2025-07-17T07:03:18.8116041Z 287d90495ed7: Download complete 2025-07-17T07:03:18.8862870Z e1da75f4dceb: Download complete 2025-07-17T07:03:18.9581560Z 66587c81b81a: Pull complete 2025-07-17T07:03:18.9694375Z dbbbaf55f1bc: Verifying Checksum 2025-07-17T07:03:18.9694769Z dbbbaf55f1bc: Download complete 2025-07-17T07:03:18.9806508Z 7a5c9899d937: Pull complete 2025-07-17T07:03:19.0732080Z b5993da49da4: Verifying Checksum 2025-07-17T07:03:19.0732452Z b5993da49da4: Download complete 2025-07-17T07:03:19.1552713Z 13afcd2a66bb: Download complete 2025-07-17T07:03:19.2189219Z 58af60b83765: Verifying Checksum 2025-07-17T07:03:19.2189715Z 58af60b83765: Download complete 2025-07-17T07:03:19.2929241Z 696aad357da7: Download complete 2025-07-17T07:03:19.4001213Z e5249a336770: Verifying Checksum 2025-07-17T07:03:19.4001762Z e5249a336770: Download complete 2025-07-17T07:03:19.4934596Z 8d76da75d1af: Verifying Checksum 2025-07-17T07:03:19.4934966Z 8d76da75d1af: Download complete 2025-07-17T07:03:19.5755236Z 95145717af0c: Verifying Checksum 2025-07-17T07:03:19.5755592Z 95145717af0c: Download complete 2025-07-17T07:03:19.6546500Z f66e02cad216: Verifying Checksum 2025-07-17T07:03:19.6546852Z f66e02cad216: Download complete 2025-07-17T07:03:19.7585426Z 23bfc6bfeaf4: Verifying Checksum 2025-07-17T07:03:19.7585866Z 23bfc6bfeaf4: Download complete 2025-07-17T07:03:21.2530890Z 9d8fe31f4371: Verifying Checksum 2025-07-17T07:03:21.2531269Z 9d8fe31f4371: Download complete 2025-07-17T07:03:21.3306702Z 4fe817dc2597: Download complete 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a7015d1f229e: Pull complete 2025-07-17T07:04:51.4641207Z 0ddb9567cfa9: Pull complete 2025-07-17T07:04:51.5834867Z 08123ea660cb: Pull complete 2025-07-17T07:04:51.7129030Z 06f07ca145d9: Pull complete 2025-07-17T07:04:51.7997801Z a151adebebbf: Pull complete 2025-07-17T07:04:52.2410845Z a567163437c5: Pull complete 2025-07-17T07:04:52.4182404Z f70d840f7624: Pull complete 2025-07-17T07:04:58.1716843Z d7369ebc3a9a: Pull complete 2025-07-17T07:04:58.4011451Z 4184c6a7fd64: Pull complete 2025-07-17T07:04:58.5908367Z 22ddb070c111: Pull complete 2025-07-17T07:04:58.8098725Z e4b9b40818eb: Pull complete 2025-07-17T07:04:58.9605818Z 403c8a0d6000: Pull complete 2025-07-17T07:04:59.0871295Z 6ce29e0f73ef: Pull complete 2025-07-17T07:04:59.4546190Z 8341c69d63c8: Pull complete 2025-07-17T07:05:01.0771665Z 9fb68e35f66f: Pull complete 2025-07-17T07:05:01.3579589Z Digest: sha256:298ca350eaba018a1adf58a70fad4c1b682d6f0738c0f0b4a6d27f040d02eb09 2025-07-17T07:05:01.4094466Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:05:01.4336277Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:05:01.4384866Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-07-17T07:05:01.4385872Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-07-17T07:05:01.4395587Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:05:01.4395988Z env: 2025-07-17T07:05:01.4396216Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:01.4396495Z ##[endgroup] 2025-07-17T07:05:01.4493122Z Prepare all required actions 2025-07-17T07:05:01.4611877Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-07-17T07:05:01.4612227Z with: 2025-07-17T07:05:01.4612826Z github-token: *** 2025-07-17T07:05:01.4613068Z env: 2025-07-17T07:05:01.4613289Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:01.4613545Z ##[endgroup] 2025-07-17T07:05:01.4672510Z ##[group]Run set -eux 2025-07-17T07:05:01.4672806Z set -eux 2025-07-17T07:05:01.4673272Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-07-17T07:05:01.4679115Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:05:01.4679511Z env: 2025-07-17T07:05:01.4679735Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:01.4680237Z GITHUB_TOKEN: *** 2025-07-17T07:05:01.4680474Z ##[endgroup] 2025-07-17T07:05:01.4705512Z + python3 .github/scripts/get_workflow_job_id.py 16337959907 i-075a75ec57b65bb84 2025-07-17T07:05:03.3315059Z Setting output job-id=46154839625 2025-07-17T07:05:03.3315734Z Setting output job-name=linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T07:05:03.3583994Z ##[group]Run python3 -m pip install psutil==5.9.1 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-07-17T07:05:03.3584856Z python3 -m pip install psutil==5.9.1 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-07-17T07:05:03.3585863Z python3 -m tools.stats.monitor --log-interval "$MONITOR_LOG_INTERVAL" --data-collect-interval "$MONITOR_DATA_COLLECT_INTERVAL" > usage_log.txt 2>&1 & 2025-07-17T07:05:03.3586735Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2025-07-17T07:05:03.3592844Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:05:03.3593237Z env: 2025-07-17T07:05:03.3593467Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:03.3593722Z JOB_ID: 46154839625 2025-07-17T07:05:03.3594117Z JOB_NAME: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T07:05:03.3594734Z WORKFLOW_NAME: pull 2025-07-17T07:05:03.3594985Z WORKFLOW_RUN_ID: 16337959907 2025-07-17T07:05:03.3595325Z MONITOR_LOG_INTERVAL: 5 2025-07-17T07:05:03.3595599Z MONITOR_DATA_COLLECT_INTERVAL: 1 2025-07-17T07:05:03.3595900Z ##[endgroup] 2025-07-17T07:05:03.8553090Z Defaulting to user installation because normal site-packages is not writeable 2025-07-17T07:05:04.2042249Z Collecting psutil==5.9.1 2025-07-17T07:05:04.2222485Z 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) 2025-07-17T07:05:04.3488458Z Collecting dataclasses_json==0.6.7 2025-07-17T07:05:04.3527350Z Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB) 2025-07-17T07:05:04.4023118Z Collecting nvidia-ml-py==11.525.84 2025-07-17T07:05:04.4061118Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2025-07-17T07:05:04.4712126Z Collecting typing-inspect<1,>=0.4.0 2025-07-17T07:05:04.4751049Z Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB) 2025-07-17T07:05:04.6277976Z Collecting marshmallow<4.0.0,>=3.18.0 2025-07-17T07:05:04.6314815Z Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB) 2025-07-17T07:05:04.7294496Z Collecting packaging>=17.0 2025-07-17T07:05:04.7332070Z Downloading packaging-25.0-py3-none-any.whl (66 kB) 2025-07-17T07:05:04.8030803Z Collecting mypy-extensions>=0.3.0 2025-07-17T07:05:04.8068330Z Downloading mypy_extensions-1.1.0-py3-none-any.whl (5.0 kB) 2025-07-17T07:05:04.8932762Z Collecting typing-extensions>=3.7.4 2025-07-17T07:05:04.8969775Z Downloading typing_extensions-4.14.1-py3-none-any.whl (43 kB) 2025-07-17T07:05:05.1201924Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json 2025-07-17T07:05:05.3938125Z Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.1 mypy-extensions-1.1.0 nvidia-ml-py-11.525.84 packaging-25.0 psutil-5.9.1 typing-extensions-4.14.1 typing-inspect-0.9.0 2025-07-17T07:05:05.5663427Z Prepare all required actions 2025-07-17T07:05:05.5663827Z Getting action download info 2025-07-17T07:05:05.6957786Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-07-17T07:05:05.9136328Z Download action repository 'actions/download-artifact@v4' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-07-17T07:05:06.2728270Z ##[group]Run ./.github/actions/download-build-artifacts 2025-07-17T07:05:06.2728647Z with: 2025-07-17T07:05:06.2728894Z name: linux-jammy-py3.13-clang12 2025-07-17T07:05:06.2729198Z s3-bucket: gha-artifacts 2025-07-17T07:05:06.2729465Z env: 2025-07-17T07:05:06.2729685Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:06.2729942Z ##[endgroup] 2025-07-17T07:05:06.2762544Z ##[group]Run seemethere/download-artifact-s3@v4 2025-07-17T07:05:06.2762896Z with: 2025-07-17T07:05:06.2763117Z name: linux-jammy-py3.13-clang12 2025-07-17T07:05:06.2763432Z s3-bucket: gha-artifacts 2025-07-17T07:05:06.2763756Z region: us-east-1 2025-07-17T07:05:06.2763986Z env: 2025-07-17T07:05:06.2764185Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:06.2764447Z ##[endgroup] 2025-07-17T07:05:06.7565153Z (node:44058) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-07-17T07:05:06.7565694Z 2025-07-17T07:05:06.7565904Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-07-17T07:05:06.7566471Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-07-17T07:05:06.7567039Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-07-17T07:05:07.0444189Z Found 1 objects with prefix pytorch/pytorch/16337959907/linux-jammy-py3.13-clang12/ 2025-07-17T07:05:07.0444943Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-07-17T07:05:13.4690176Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-07-17T07:05:13.4695610Z Artifact download has finished successfully 2025-07-17T07:05:13.4855017Z ##[group]Run unzip -o 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formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-07-17T07:05:22.2359919Z 2025-07-17T07:05:22.2360198Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-07-17T07:05:22.2360777Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-07-17T07:05:22.2361349Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-07-17T07:05:22.3365392Z Found 1 objects with prefix pytorch/pytorch/16337959907/td_results/ 2025-07-17T07:05:22.3366075Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-07-17T07:05:22.4008266Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-07-17T07:05:22.4013549Z Artifact download has finished successfully 2025-07-17T07:05:22.4230453Z ##[group]Run mkdir -p .additional_ci_files 2025-07-17T07:05:22.4230863Z mkdir -p .additional_ci_files 2025-07-17T07:05:22.4231317Z mv td_results.json .additional_ci_files/td_results.json || true 2025-07-17T07:05:22.4237238Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:05:22.4237634Z env: 2025-07-17T07:05:22.4237876Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:22.4238132Z ##[endgroup] 2025-07-17T07:05:22.7147963Z ##[group]Run .github/scripts/parse_ref.py 2025-07-17T07:05:22.7148373Z .github/scripts/parse_ref.py 2025-07-17T07:05:22.7154209Z shell: /usr/bin/bash -e {0} 2025-07-17T07:05:22.7154495Z env: 2025-07-17T07:05:22.7154726Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:22.7154984Z ##[endgroup] 2025-07-17T07:05:22.7677042Z Setting output branch=main 2025-07-17T07:05:22.7810845Z Prepare all required actions 2025-07-17T07:05:22.7811257Z Getting action download info 2025-07-17T07:05:22.9264572Z ##[group]Run ./.github/actions/filter-test-configs 2025-07-17T07:05:22.9264954Z with: 2025-07-17T07:05:22.9265632Z github-token: *** 2025-07-17T07:05:22.9268613Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]} 2025-07-17T07:05:22.9271748Z job-name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T07:05:22.9272380Z env: 2025-07-17T07:05:22.9272596Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:22.9272869Z ##[endgroup] 2025-07-17T07:05:22.9437431Z ##[group]Run nick-fields/retry@v3.0.0 2025-07-17T07:05:22.9437758Z with: 2025-07-17T07:05:22.9437960Z shell: bash 2025-07-17T07:05:22.9438198Z timeout_minutes: 10 2025-07-17T07:05:22.9438457Z max_attempts: 5 2025-07-17T07:05:22.9438693Z retry_wait_seconds: 30 2025-07-17T07:05:22.9439531Z 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 2025-07-17T07:05:22.9440422Z polling_interval_seconds: 1 2025-07-17T07:05:22.9440715Z warning_on_retry: true 2025-07-17T07:05:22.9440972Z continue_on_error: false 2025-07-17T07:05:22.9441235Z env: 2025-07-17T07:05:22.9441449Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:22.9441878Z GITHUB_TOKEN: *** 2025-07-17T07:05:22.9442113Z ##[endgroup] 2025-07-17T07:05:23.1098901Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2025-07-17T07:05:23.3424012Z Defaulting to user installation because normal site-packages is not writeable 2025-07-17T07:05:23.4836304Z Collecting requests==2.27.1 2025-07-17T07:05:23.4992687Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-07-17T07:05:23.6909237Z Collecting pyyaml==6.0.1 2025-07-17T07:05:23.6941759Z Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB) 2025-07-17T07:05:23.7429088Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2025-07-17T07:05:24.0779728Z Collecting charset-normalizer~=2.0.0 2025-07-17T07:05:24.0815335Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-07-17T07:05:24.1079091Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2025-07-17T07:05:24.1732583Z Collecting certifi>=2017.4.17 2025-07-17T07:05:24.1765260Z Downloading certifi-2025.7.14-py3-none-any.whl (162 kB) 2025-07-17T07:05:24.2895527Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2025-07-17T07:05:24.5854788Z Successfully installed certifi-2025.7.14 charset-normalizer-2.0.12 pyyaml-6.0.1 requests-2.27.1 2025-07-17T07:05:25.0191767Z Command completed after 1 attempt(s). 2025-07-17T07:05:25.0307751Z ##[group]Run set -x 2025-07-17T07:05:25.0308024Z set -x 2025-07-17T07:05:25.0308258Z  2025-07-17T07:05:25.0308641Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-07-17T07:05:25.0309145Z # in runner workspace 2025-07-17T07:05:25.0309709Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-07-17T07:05:25.0316512Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:05:25.0316889Z env: 2025-07-17T07:05:25.0317114Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:25.0317521Z ##[endgroup] 2025-07-17T07:05:25.0342419Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-07-17T07:05:25.0521781Z Setting output branch=main 2025-07-17T07:05:25.0648940Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-07-17T07:05:25.0649357Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-07-17T07:05:25.0649710Z echo "Job name: ${JOB_NAME}" 2025-07-17T07:05:25.0650012Z  2025-07-17T07:05:25.0650407Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-07-17T07:05:25.0650892Z # in runner workspace 2025-07-17T07:05:25.0651365Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-07-17T07:05:25.0651871Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-07-17T07:05:25.0652207Z  --job-name "${JOB_NAME}" \ 2025-07-17T07:05:25.0655298Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]}" \ 2025-07-17T07:05:25.0658315Z  --selected-test-configs "" \ 2025-07-17T07:05:25.0658649Z  --pr-number "${PR_NUMBER}" \ 2025-07-17T07:05:25.0658981Z  --tag "${TAG}" \ 2025-07-17T07:05:25.0659286Z  --event-name "${EVENT_NAME}" \ 2025-07-17T07:05:25.0659613Z  --schedule "${SCHEDULE}" \ 2025-07-17T07:05:25.0659936Z  --branch "${HEAD_BRANCH}" 2025-07-17T07:05:25.0665433Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:05:25.0665834Z env: 2025-07-17T07:05:25.0666051Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:25.0666551Z GITHUB_TOKEN: *** 2025-07-17T07:05:25.0666949Z JOB_NAME: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T07:05:25.0667400Z PR_NUMBER: 2025-07-17T07:05:25.0667629Z TAG: 2025-07-17T07:05:25.0667843Z EVENT_NAME: push 2025-07-17T07:05:25.0668092Z SCHEDULE: 2025-07-17T07:05:25.0668306Z HEAD_BRANCH: main 2025-07-17T07:05:25.0668542Z ##[endgroup] 2025-07-17T07:05:25.0692319Z Workflow: pull 2025-07-17T07:05:25.0704866Z Job name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T07:05:25.2579564Z Setting output keep-going=True 2025-07-17T07:05:25.2580256Z Setting output ci-verbose-test-logs=False 2025-07-17T07:05:25.2580962Z Setting output ci-test-showlocals=False 2025-07-17T07:05:25.2581637Z Setting output ci-no-test-timeout=False 2025-07-17T07:05:25.2582311Z Setting output ci-no-td=False 2025-07-17T07:05:25.2582912Z Setting output ci-td-distributed=False 2025-07-17T07:05:25.2583582Z Setting output is-unstable=False 2025-07-17T07:05:25.2584193Z Setting output reenabled-issues= 2025-07-17T07:05:25.2590842Z Setting output test-matrix={"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]} 2025-07-17T07:05:25.2597447Z Setting output is-test-matrix-empty=False 2025-07-17T07:05:25.2776081Z ##[group]Run echo "Filtered matrix:" 2025-07-17T07:05:25.2776453Z echo "Filtered matrix:" 2025-07-17T07:05:25.2779449Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "linux.2xlarge"}]}" 2025-07-17T07:05:25.2782431Z  2025-07-17T07:05:25.2782647Z echo 2025-07-17T07:05:25.2782920Z echo "Is the current job unstable? False" 2025-07-17T07:05:25.2783277Z  2025-07-17T07:05:25.2783490Z echo 2025-07-17T07:05:25.2783745Z echo "Is keep-going label set? True" 2025-07-17T07:05:25.2784074Z  2025-07-17T07:05:25.2784287Z echo 2025-07-17T07:05:25.2784533Z echo "Reenabled issues? " 2025-07-17T07:05:25.2790297Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:05:25.2790693Z env: 2025-07-17T07:05:25.2790922Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:25.2791184Z ##[endgroup] 2025-07-17T07:05:25.2814381Z Filtered matrix: 2025-07-17T07:05:25.2818443Z {include: [{config: default, shard: 1, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: linux.4xlarge}, {config: crossref, shard: 1, num_shards: 2, runner: linux.2xlarge}, {config: crossref, shard: 2, num_shards: 2, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: linux.2xlarge}, {config: einops, shard: 1, num_shards: 1, runner: linux.2xlarge}]} 2025-07-17T07:05:25.2821270Z 2025-07-17T07:05:25.2821388Z Is the current job unstable? False 2025-07-17T07:05:25.2821611Z 2025-07-17T07:05:25.2821721Z Is keep-going label set? True 2025-07-17T07:05:25.2821926Z 2025-07-17T07:05:25.2822020Z Reenabled issues? 2025-07-17T07:05:25.2927036Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-07-17T07:05:25.2927579Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-07-17T07:05:25.2933022Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:05:25.2933425Z env: 2025-07-17T07:05:25.2933654Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:25.2933913Z JOB_TIMEOUT: 600 2025-07-17T07:05:25.2934151Z ##[endgroup] 2025-07-17T07:05:25.3015934Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-07-17T07:05:25.3016486Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-07-17T07:05:25.3017080Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-07-17T07:05:25.3022302Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T07:05:25.3022699Z env: 2025-07-17T07:05:25.3022925Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:25.3023178Z ##[endgroup] 2025-07-17T07:05:25.3191708Z ##[group]Run set -x 2025-07-17T07:05:25.3192062Z set -x 2025-07-17T07:05:25.3192281Z  2025-07-17T07:05:25.3192542Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-07-17T07:05:25.3192951Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-07-17T07:05:25.3193352Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-07-17T07:05:25.3193730Z  TEST_COMMAND=.ci/onnx/test.sh 2025-07-17T07:05:25.3194043Z else 2025-07-17T07:05:25.3194301Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-07-17T07:05:25.3194608Z fi 2025-07-17T07:05:25.3194817Z  2025-07-17T07:05:25.3195092Z # Leaving 1GB for the runner and other things 2025-07-17T07:05:25.3195690Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo) 2025-07-17T07:05:25.3196615Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap 2025-07-17T07:05:25.3197365Z # comes from https://github.com/pytorch/test-infra/pull/6058 2025-07-17T07:05:25.3197912Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3)) 2025-07-17T07:05:25.3198352Z  2025-07-17T07:05:25.3198625Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-07-17T07:05:25.3198975Z  SHM_OPTS= 2025-07-17T07:05:25.3199232Z  JENKINS_USER= 2025-07-17T07:05:25.3199594Z  # ensure that docker container cleanly exits in 12 hours 2025-07-17T07:05:25.3200083Z  # if for some reason cleanup action doesn't stop container 2025-07-17T07:05:25.3200501Z  # when job is cancelled 2025-07-17T07:05:25.3200824Z  DOCKER_SHELL_CMD="sleep 12h" 2025-07-17T07:05:25.3201133Z else 2025-07-17T07:05:25.3201380Z  SHM_OPTS="--shm-size=${SHM_SIZE}" 2025-07-17T07:05:25.3201729Z  JENKINS_USER="--user jenkins" 2025-07-17T07:05:25.3202057Z  DOCKER_SHELL_CMD= 2025-07-17T07:05:25.3202318Z fi 2025-07-17T07:05:25.3202534Z  2025-07-17T07:05:25.3202880Z # detached container should get cleaned up by teardown_ec2_linux 2025-07-17T07:05:25.3203426Z # TODO: Stop building test binaries as part of the build phase 2025-07-17T07:05:25.3204063Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice 2025-07-17T07:05:25.3204619Z # shellcheck disable=SC2086,SC2090 2025-07-17T07:05:25.3204969Z container_name=$(docker run \ 2025-07-17T07:05:25.3205277Z  ${GPU_FLAG:-} \ 2025-07-17T07:05:25.3205588Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2025-07-17T07:05:25.3205954Z  -e BUILD_ENVIRONMENT \ 2025-07-17T07:05:25.3206249Z  -e PR_NUMBER \ 2025-07-17T07:05:25.3206530Z  -e GITHUB_ACTIONS \ 2025-07-17T07:05:25.3206828Z  -e GITHUB_REPOSITORY \ 2025-07-17T07:05:25.3207140Z  -e GITHUB_WORKFLOW \ 2025-07-17T07:05:25.3207424Z  -e GITHUB_JOB \ 2025-07-17T07:05:25.3207704Z  -e GITHUB_RUN_ID \ 2025-07-17T07:05:25.3207992Z  -e GITHUB_RUN_NUMBER \ 2025-07-17T07:05:25.3208289Z  -e GITHUB_RUN_ATTEMPT \ 2025-07-17T07:05:25.3208593Z  -e JOB_ID \ 2025-07-17T07:05:25.3208855Z  -e JOB_NAME \ 2025-07-17T07:05:25.3209110Z  -e BASE_SHA \ 2025-07-17T07:05:25.3209371Z  -e BRANCH \ 2025-07-17T07:05:25.3209624Z  -e SHA1 \ 2025-07-17T07:05:25.3209870Z  -e AWS_DEFAULT_REGION \ 2025-07-17T07:05:25.3210175Z  -e IN_WHEEL_TEST \ 2025-07-17T07:05:25.3210462Z  -e SHARD_NUMBER \ 2025-07-17T07:05:25.3210877Z  -e TEST_CONFIG \ 2025-07-17T07:05:25.3211151Z  -e NUM_TEST_SHARDS \ 2025-07-17T07:05:25.3211456Z  -e REENABLED_ISSUES \ 2025-07-17T07:05:25.3211769Z  -e CONTINUE_THROUGH_ERROR \ 2025-07-17T07:05:25.3212172Z  -e VERBOSE_TEST_LOGS \ 2025-07-17T07:05:25.3212490Z  -e TEST_SHOWLOCALS \ 2025-07-17T07:05:25.3212797Z  -e NO_TEST_TIMEOUT \ 2025-07-17T07:05:25.3213071Z  -e NO_TD \ 2025-07-17T07:05:25.3213337Z  -e TD_DISTRIBUTED \ 2025-07-17T07:05:25.3213634Z  -e PR_LABELS \ 2025-07-17T07:05:25.3213941Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-07-17T07:05:25.3214276Z  -e SCCACHE_BUCKET \ 2025-07-17T07:05:25.3214569Z  -e SCCACHE_REGION \ 2025-07-17T07:05:25.3214857Z  -e XLA_CUDA \ 2025-07-17T07:05:25.3215145Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2025-07-17T07:05:25.3215610Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-07-17T07:05:25.3216003Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-07-17T07:05:25.3216375Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2025-07-17T07:05:25.3216736Z  -e HUGGING_FACE_HUB_TOKEN \ 2025-07-17T07:05:25.3217093Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2025-07-17T07:05:25.3217432Z  -e DASHBOARD_TAG \ 2025-07-17T07:05:25.3217718Z  -e ARTIFACTS_FILE_SUFFIX \ 2025-07-17T07:05:25.3218098Z  --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \ 2025-07-17T07:05:25.3218533Z  --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \ 2025-07-17T07:05:25.3218950Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2025-07-17T07:05:25.3219376Z  --security-opt seccomp=unconfined \ 2025-07-17T07:05:25.3219722Z  --cap-add=SYS_PTRACE \ 2025-07-17T07:05:25.3220030Z  --ipc=host \ 2025-07-17T07:05:25.3220298Z  ${SHM_OPTS} \ 2025-07-17T07:05:25.3220550Z  --tty \ 2025-07-17T07:05:25.3220799Z  --detach \ 2025-07-17T07:05:25.3221075Z  --name="${container_name}" \ 2025-07-17T07:05:25.3221403Z  ${JENKINS_USER} \ 2025-07-17T07:05:25.3221754Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-07-17T07:05:25.3222178Z  -w /var/lib/jenkins/workspace \ 2025-07-17T07:05:25.3222513Z  "${DOCKER_IMAGE}" \ 2025-07-17T07:05:25.3222795Z  ${DOCKER_SHELL_CMD} 2025-07-17T07:05:25.3223074Z ) 2025-07-17T07:05:25.3223380Z # Propagate download.pytorch.org IP to container 2025-07-17T07:05:25.3224073Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2025-07-17T07:05:25.3224823Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2025-07-17T07:05:25.3225253Z  2025-07-17T07:05:25.3225530Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-07-17T07:05:25.3226125Z  docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt" 2025-07-17T07:05:25.3226675Z fi 2025-07-17T07:05:25.3226889Z  2025-07-17T07:05:25.3227403Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2025-07-17T07:05:25.3232718Z shell: /usr/bin/bash -e {0} 2025-07-17T07:05:25.3233000Z env: 2025-07-17T07:05:25.3233224Z GIT_DEFAULT_BRANCH: main 2025-07-17T07:05:25.3233536Z BUILD_ENVIRONMENT: linux-jammy-py3.13-clang12 2025-07-17T07:05:25.3233883Z PR_NUMBER: 2025-07-17T07:05:25.3234132Z GITHUB_REPOSITORY: pytorch/pytorch 2025-07-17T07:05:25.3234437Z GITHUB_WORKFLOW: pull 2025-07-17T07:05:25.3234693Z GITHUB_JOB: test 2025-07-17T07:05:25.3234941Z GITHUB_RUN_ID: 16337959907 2025-07-17T07:05:25.3235211Z GITHUB_RUN_NUMBER: 336806 2025-07-17T07:05:25.3235492Z GITHUB_RUN_ATTEMPT: 1 2025-07-17T07:05:25.3235750Z JOB_ID: 46154839625 2025-07-17T07:05:25.3236220Z JOB_NAME: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T07:05:25.3236677Z BRANCH: main 2025-07-17T07:05:25.3236947Z SHA1: a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:25.3237333Z BASE_SHA: a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:25.3237740Z TEST_CONFIG: default 2025-07-17T07:05:25.3237992Z SHARD_NUMBER: 1 2025-07-17T07:05:25.3238230Z NUM_TEST_SHARDS: 5 2025-07-17T07:05:25.3238464Z REENABLED_ISSUES: 2025-07-17T07:05:25.3238724Z CONTINUE_THROUGH_ERROR: True 2025-07-17T07:05:25.3239013Z VERBOSE_TEST_LOGS: False 2025-07-17T07:05:25.3239276Z TEST_SHOWLOCALS: False 2025-07-17T07:05:25.3239542Z NO_TEST_TIMEOUT: False 2025-07-17T07:05:25.3239799Z NO_TD: False 2025-07-17T07:05:25.3240021Z TD_DISTRIBUTED: False 2025-07-17T07:05:25.3240344Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2025-07-17T07:05:25.3240719Z SCCACHE_REGION: us-east-1 2025-07-17T07:05:25.3240990Z SHM_SIZE: 1g 2025-07-17T07:05:25.3241719Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:05:25.3242527Z XLA_CUDA: 2025-07-17T07:05:25.3242894Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2025-07-17T07:05:25.3243357Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-07-17T07:05:25.3243691Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-07-17T07:05:25.3244003Z DASHBOARD_TAG: 2025-07-17T07:05:25.3244428Z HUGGING_FACE_HUB_TOKEN: *** 2025-07-17T07:05:25.3244852Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2025-07-17T07:05:25.3245274Z ARTIFACTS_FILE_SUFFIX: test-default-1-5-linux.4xlarge_46154839625 2025-07-17T07:05:25.3245694Z ##[endgroup] 2025-07-17T07:05:25.3267605Z + [[ default == \m\u\l\t\i\g\p\u ]] 2025-07-17T07:05:25.3267983Z + [[ linux-jammy-py3.13-clang12 == *onnx* ]] 2025-07-17T07:05:25.3268336Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-07-17T07:05:25.3271012Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo 2025-07-17T07:05:25.3857143Z + TOTAL_AVAILABLE_MEMORY_IN_GB='29.641 ' 2025-07-17T07:05:25.3857547Z + TOTAL_MEMORY_WITH_SWAP=32 2025-07-17T07:05:25.3857868Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-07-17T07:05:25.3858230Z + SHM_OPTS=--shm-size=1g 2025-07-17T07:05:25.3858526Z + JENKINS_USER='--user jenkins' 2025-07-17T07:05:25.3858817Z + DOCKER_SHELL_CMD= 2025-07-17T07:05:25.3865421Z +++ nproc --ignore=2 2025-07-17T07:05:25.3986725Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=14 -e SCCACHE_BUCKET -e SCCACHE_REGION -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e ARTIFACTS_FILE_SUFFIX --memory=29g --memory-swap=32g --env-file=/tmp/github_env_16337959907 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 2025-07-17T07:05:44.0920827Z + container_name=a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T07:05:44.0924712Z + grep download.pytorch.org /etc/hosts 2025-07-17T07:05:44.0926868Z + docker exec -i a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 sudo bash -c '/bin/cat >> /etc/hosts' 2025-07-17T07:05:44.2116803Z + echo DOCKER_CONTAINER_ID=a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T07:05:44.2117719Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-07-17T07:05:44.2120369Z ++ echo dist/torch-2.9.0a0+gita38f433-cp313-cp313-linux_x86_64.whl 2025-07-17T07:05:44.2122954Z + docker exec -t a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 sh -c 'python3 -m pip install dist/torch-2.9.0a0+gita38f433-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2025-07-17T07:05:44.5765563Z Processing ./dist/torch-2.9.0a0+gita38f433-cp313-cp313-linux_x86_64.whl (from torch==2.9.0a0+gita38f433) 2025-07-17T07:05:45.1092793Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (3.18.0) 2025-07-17T07:05:45.1096755Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (4.14.1) 2025-07-17T07:05:45.1101865Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (80.9.0) 2025-07-17T07:05:45.1105679Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (1.13.3) 2025-07-17T07:05:45.1108681Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (2.8.8) 2025-07-17T07:05:45.1111662Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (3.1.6) 2025-07-17T07:05:45.1114707Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (2025.5.1) 2025-07-17T07:05:45.1127590Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (3.3.0) 2025-07-17T07:05:45.1137200Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from opt-einsum>=3.3->torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (2.1.2) 2025-07-17T07:05:45.1180084Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (1.3.0) 2025-07-17T07:05:45.1296565Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.9.0a0+gita38f433->torch==2.9.0a0+gita38f433) (3.0.2) 2025-07-17T07:05:45.3397665Z Installing collected packages: torch 2025-07-17T07:05:54.9234142Z Successfully installed torch-2.9.0a0+gita38f433 2025-07-17T07:05:54.9900579Z + export TERM=vt100 2025-07-17T07:05:54.9900865Z + TERM=vt100 2025-07-17T07:05:54.9903106Z ++ dirname .ci/pytorch/test.sh 2025-07-17T07:05:54.9910429Z + source .ci/pytorch/common.sh 2025-07-17T07:05:54.9913431Z +++ dirname .ci/pytorch/common.sh 2025-07-17T07:05:54.9920013Z ++ source .ci/pytorch/common_utils.sh 2025-07-17T07:05:54.9921199Z +++ declare -f -t trap_add 2025-07-17T07:05:54.9924899Z ++ set -ex -o pipefail 2025-07-17T07:05:54.9925276Z ++ [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-07-17T07:05:54.9925625Z ++ BUILD_TEST_LIBTORCH=0 2025-07-17T07:05:54.9928685Z ++ dirname .ci/pytorch/test.sh 2025-07-17T07:05:54.9935047Z + source .ci/pytorch/common-build.sh 2025-07-17T07:05:54.9936652Z ++ [[ linux-jammy-py3.13-clang12 != *win-* ]] 2025-07-17T07:05:54.9942517Z ++++ dirname .ci/pytorch/common-build.sh 2025-07-17T07:05:54.9949128Z +++ cd .ci/pytorch 2025-07-17T07:05:54.9949806Z +++ pwd -P 2025-07-17T07:05:54.9951593Z ++ script_dir=/var/lib/jenkins/workspace/.ci/pytorch 2025-07-17T07:05:54.9952028Z ++ [[ linux-jammy-py3.13-clang12 == *-pch* ]] 2025-07-17T07:05:54.9952645Z ++ which sccache 2025-07-17T07:05:54.9968200Z ++ [[ -z ossci-compiler-cache-circleci-v2 ]] 2025-07-17T07:05:54.9968567Z ++ sccache --stop-server 2025-07-17T07:05:54.9993555Z ++ true 2025-07-17T07:05:54.9993819Z ++ rm -f /var/lib/jenkins/sccache_error.log 2025-07-17T07:05:55.0001437Z ++ trap_add sccache_epilogue EXIT 2025-07-17T07:05:55.0001775Z ++ trap_add_cmd=sccache_epilogue 2025-07-17T07:05:55.0002069Z ++ shift 2025-07-17T07:05:55.0002287Z ++ for trap_add_name in "$@" 2025-07-17T07:05:55.0007680Z ++++ trap -p EXIT 2025-07-17T07:05:55.0010254Z +++ eval 'extract_trap_cmd ' 2025-07-17T07:05:55.0010598Z ++++ extract_trap_cmd 2025-07-17T07:05:55.0010850Z ++++ printf '%s\n' '' 2025-07-17T07:05:55.0011121Z +++ printf '%s\n' sccache_epilogue 2025-07-17T07:05:55.0012930Z ++ trap -- ' 2025-07-17T07:05:55.0013217Z sccache_epilogue' EXIT 2025-07-17T07:05:55.0013469Z ++ [[ -n 1 ]] 2025-07-17T07:05:55.0013874Z ++ echo 'Skipping sccache server initialization, setting environment variables' 2025-07-17T07:05:55.0014525Z Skipping sccache server initialization, setting environment variables 2025-07-17T07:05:55.0014990Z ++ export SCCACHE_IDLE_TIMEOUT=0 2025-07-17T07:05:55.0015359Z ++ SCCACHE_IDLE_TIMEOUT=0 2025-07-17T07:05:55.0015723Z ++ export SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-07-17T07:05:55.0016203Z ++ SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-07-17T07:05:55.0016611Z ++ export RUST_LOG=sccache::server=error 2025-07-17T07:05:55.0016943Z ++ RUST_LOG=sccache::server=error 2025-07-17T07:05:55.0017245Z ++ sccache --zero-stats 2025-07-17T07:05:55.1186353Z Statistics zeroed. 2025-07-17T07:05:55.1190697Z ++ which ccache 2025-07-17T07:05:55.1215395Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-07-17T07:05:55.1215826Z + [[ linux-jammy-py3.13-clang12 != *s390x* ]] 2025-07-17T07:05:55.1216189Z + [[ -d /var/lib/jenkins/workspace ]] 2025-07-17T07:05:55.1218481Z ++ stat -c %u /var/lib/jenkins/workspace 2025-07-17T07:05:55.1229202Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2025-07-17T07:05:55.1229598Z + trap_add cleanup_workspace EXIT 2025-07-17T07:05:55.1229969Z + trap_add_cmd=cleanup_workspace 2025-07-17T07:05:55.1230293Z + shift 2025-07-17T07:05:55.1230504Z + for trap_add_name in "$@" 2025-07-17T07:05:55.1236500Z +++ trap -p EXIT 2025-07-17T07:05:55.1239049Z ++ eval 'extract_trap_cmd trap -- '\'' 2025-07-17T07:05:55.1239418Z sccache_epilogue'\'' EXIT' 2025-07-17T07:05:55.1239705Z +++ extract_trap_cmd trap -- ' 2025-07-17T07:05:55.1239997Z sccache_epilogue' EXIT 2025-07-17T07:05:55.1240253Z +++ printf '%s\n' ' 2025-07-17T07:05:55.1240529Z sccache_epilogue' 2025-07-17T07:05:55.1240819Z ++ printf '%s\n' cleanup_workspace 2025-07-17T07:05:55.1241356Z + trap -- ' 2025-07-17T07:05:55.1241574Z sccache_epilogue 2025-07-17T07:05:55.1241882Z cleanup_workspace' EXIT 2025-07-17T07:05:55.1242201Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2025-07-17T07:05:55.7320330Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2025-07-17T07:05:55.7336125Z + echo 'Environment variables:' 2025-07-17T07:05:55.7336463Z Environment variables: 2025-07-17T07:05:55.7336722Z + env 2025-07-17T07:05:55.7344392Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-07-17T07:05:55.7344963Z CONTINUE_THROUGH_ERROR=True 2025-07-17T07:05:55.7345422Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-07-17T07:05:55.7345842Z HOSTNAME=a8f5572e1eb0 2025-07-17T07:05:55.7346511Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.7347151Z GITHUB_ACTION=__run_2 2025-07-17T07:05:55.7347431Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-07-17T07:05:55.7347747Z GITHUB_RUN_NUMBER=336806 2025-07-17T07:05:55.7348002Z TEST_CONFIG=default 2025-07-17T07:05:55.7348264Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-07-17T07:05:55.7348600Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-07-17T07:05:55.7348912Z SCCACHE_IDLE_TIMEOUT=0 2025-07-17T07:05:55.7349444Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-07-17T07:05:55.7349762Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-07-17T07:05:55.7350313Z GITHUB_REF_TYPE=branch 2025-07-17T07:05:55.7350654Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-07-17T07:05:55.7350966Z BASE_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:55.7351343Z XLA_CUDA= 2025-07-17T07:05:55.7351700Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-07-17T07:05:55.7352175Z HUGGING_FACE_HUB_TOKEN=*** 2025-07-17T07:05:55.7354004Z *** 2025-07-17T07:05:55.7354245Z GITHUB_REPOSITORY_ID=65600975 2025-07-17T07:05:55.7354525Z GITHUB_ACTIONS=true 2025-07-17T07:05:55.7354836Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-07-17T07:05:55.7355240Z SHA1=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:55.7355625Z GITHUB_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:55.7356143Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-07-17T07:05:55.7356632Z UCC_HOME=/usr 2025-07-17T07:05:55.7356867Z VERBOSE_TEST_LOGS=False 2025-07-17T07:05:55.7357126Z GITHUB_REF=refs/heads/main 2025-07-17T07:05:55.7357403Z SHARD_NUMBER=1 2025-07-17T07:05:55.7357645Z GITHUB_REF_PROTECTED=true 2025-07-17T07:05:55.7357901Z HOME=/var/lib/jenkins 2025-07-17T07:05:55.7358193Z GITHUB_API_URL=https://api.github.com 2025-07-17T07:05:55.7358534Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-07-17T07:05:55.7358827Z UCX_COMMIT= 2025-07-17T07:05:55.7359046Z USE_SYSTEM_NCCL=1 2025-07-17T07:05:55.7359287Z NUM_TEST_SHARDS=5 2025-07-17T07:05:55.7359507Z UCX_HOME=/usr 2025-07-17T07:05:55.7360098Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.7360911Z JOB_NAME=linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T07:05:55.7361691Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.7362519Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-07-17T07:05:55.7363044Z GITHUB_EVENT_NAME=push 2025-07-17T07:05:55.7363313Z DASHBOARD_TAG= 2025-07-17T07:05:55.7363544Z GITHUB_RUN_ID=16337959907 2025-07-17T07:05:55.7363818Z INSTALLED_OPENBLAS= 2025-07-17T07:05:55.7364466Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.7365186Z GITHUB_ACTOR=pytorchmergebot 2025-07-17T07:05:55.7365457Z PR_NUMBER= 2025-07-17T07:05:55.7365683Z DESIRED_CUDA= 2025-07-17T07:05:55.7365922Z GITHUB_RUN_ATTEMPT=1 2025-07-17T07:05:55.7366175Z ANACONDA_PYTHON_VERSION=3.13 2025-07-17T07:05:55.7366522Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-07-17T07:05:55.7366881Z TERM=vt100 2025-07-17T07:05:55.7367092Z INSTALLED_VISION=yes 2025-07-17T07:05:55.7367337Z BRANCH=main 2025-07-17T07:05:55.7367567Z SCCACHE_REGION=us-east-1 2025-07-17T07:05:55.7367836Z OPENSSL_ROOT_DIR=/opt/openssl 2025-07-17T07:05:55.7368129Z CUDA_PATH=/usr/local/cuda 2025-07-17T07:05:55.7368662Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-07-17T07:05:55.7369258Z GITHUB_SERVER_URL=https://github.com 2025-07-17T07:05:55.7369562Z UCC_COMMIT= 2025-07-17T07:05:55.7369781Z REENABLED_ISSUES= 2025-07-17T07:05:55.7370021Z DOCS= 2025-07-17T07:05:55.7370223Z SHLVL=1 2025-07-17T07:05:55.7370431Z MAX_JOBS=14 2025-07-17T07:05:55.7370641Z GITHUB_ACTOR_ID=97764156 2025-07-17T07:05:55.7370993Z GITHUB_WORKFLOW_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:55.7371389Z GITHUB_REF_NAME=main 2025-07-17T07:05:55.7371943Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-07-17T07:05:55.7372381Z GITHUB_JOB=test 2025-07-17T07:05:55.7372623Z NO_TEST_TIMEOUT=False 2025-07-17T07:05:55.7372889Z TD_DISTRIBUTED=False 2025-07-17T07:05:55.7373153Z GITHUB_REPOSITORY=pytorch/pytorch 2025-07-17T07:05:55.7373467Z GITHUB_RETENTION_DAYS=90 2025-07-17T07:05:55.7373742Z OPENSSL_DIR=/opt/openssl 2025-07-17T07:05:55.7374006Z GITHUB_ACTION_REPOSITORY= 2025-07-17T07:05:55.7374812Z 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 2025-07-17T07:05:55.7375910Z GITHUB_BASE_REF= 2025-07-17T07:05:55.7376134Z INSTALLED_ACL= 2025-07-17T07:05:55.7376582Z ARTIFACTS_FILE_SUFFIX=test-default-1-5-linux.4xlarge_46154839625 2025-07-17T07:05:55.7376997Z CI=true 2025-07-17T07:05:55.7377227Z GITHUB_REPOSITORY_OWNER=pytorch 2025-07-17T07:05:55.7377560Z RUST_LOG=sccache::server=error 2025-07-17T07:05:55.7377847Z JOB_ID=46154839625 2025-07-17T07:05:55.7378083Z GITHUB_HEAD_REF= 2025-07-17T07:05:55.7378309Z GITHUB_ACTION_REF= 2025-07-17T07:05:55.7378609Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-07-17T07:05:55.7378974Z TEST_SHOWLOCALS=False 2025-07-17T07:05:55.7379223Z GITHUB_WORKFLOW=pull 2025-07-17T07:05:55.7379496Z DEBIAN_FRONTEND=noninteractive 2025-07-17T07:05:55.7380145Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.7380791Z NO_TD=False 2025-07-17T07:05:55.7381029Z SKIP_SCCACHE_INITIALIZATION=1 2025-07-17T07:05:55.7381344Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-07-17T07:05:55.7381650Z _=/usr/bin/env 2025-07-17T07:05:55.7381994Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-07-17T07:05:55.8232755Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch 2025-07-17T07:05:55.8233536Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T07:05:55.8234142Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib 2025-07-17T07:05:55.8234767Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test 2025-07-17T07:05:55.8235241Z + BUILD_DIR=build 2025-07-17T07:05:55.8235502Z + BUILD_RENAMED_DIR=build_renamed 2025-07-17T07:05:55.8235794Z + BUILD_BIN_DIR=build/bin 2025-07-17T07:05:55.8236055Z + SHARD_NUMBER=1 2025-07-17T07:05:55.8236291Z + NUM_TEST_SHARDS=5 2025-07-17T07:05:55.8236543Z + export TORCH_SERIALIZATION_DEBUG=1 2025-07-17T07:05:55.8236890Z + TORCH_SERIALIZATION_DEBUG=1 2025-07-17T07:05:55.8237245Z + export VALGRIND=ON 2025-07-17T07:05:55.8237481Z + VALGRIND=ON 2025-07-17T07:05:55.8237744Z + [[ linux-jammy-py3.13-clang12 == *clang9* ]] 2025-07-17T07:05:55.8238117Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-07-17T07:05:55.8238461Z + [[ linux-jammy-py3.13-clang12 == *s390x* ]] 2025-07-17T07:05:55.8238784Z + [[ 0 == \1 ]] 2025-07-17T07:05:55.8239004Z + [[ True == \1 ]] 2025-07-17T07:05:55.8239270Z + [[ linux-jammy-py3.13-clang12 != *bazel* ]] 2025-07-17T07:05:55.8239609Z ++ realpath build/custom_test_artifacts 2025-07-17T07:05:55.8246039Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2025-07-17T07:05:55.8246556Z + [[ -n '' ]] 2025-07-17T07:05:55.8246790Z + echo 'Environment variables' 2025-07-17T07:05:55.8247091Z Environment variables 2025-07-17T07:05:55.8247344Z + env 2025-07-17T07:05:55.8460235Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-07-17T07:05:55.8460861Z CONTINUE_THROUGH_ERROR=True 2025-07-17T07:05:55.8461194Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-07-17T07:05:55.8461543Z HOSTNAME=a8f5572e1eb0 2025-07-17T07:05:55.8462267Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.8462911Z GITHUB_ACTION=__run_2 2025-07-17T07:05:55.8463184Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-07-17T07:05:55.8463509Z GITHUB_RUN_NUMBER=336806 2025-07-17T07:05:55.8463765Z TEST_CONFIG=default 2025-07-17T07:05:55.8464031Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-07-17T07:05:55.8464371Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-07-17T07:05:55.8464692Z SCCACHE_IDLE_TIMEOUT=0 2025-07-17T07:05:55.8465191Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-07-17T07:05:55.8465515Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-07-17T07:05:55.8465935Z GITHUB_REF_TYPE=branch 2025-07-17T07:05:55.8466283Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-07-17T07:05:55.8466976Z BASE_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:55.8467505Z XLA_CUDA= 2025-07-17T07:05:55.8467753Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-07-17T07:05:55.8468389Z HUGGING_FACE_HUB_TOKEN=*** 2025-07-17T07:05:55.8468721Z *** 2025-07-17T07:05:55.8469067Z GITHUB_REPOSITORY_ID=65600975 2025-07-17T07:05:55.8469349Z GITHUB_ACTIONS=true 2025-07-17T07:05:55.8469657Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-07-17T07:05:55.8470058Z SHA1=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:55.8470427Z GITHUB_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:55.8470963Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-07-17T07:05:55.8471444Z UCC_HOME=/usr 2025-07-17T07:05:55.8471673Z TORCH_SERIALIZATION_DEBUG=1 2025-07-17T07:05:55.8472178Z VERBOSE_TEST_LOGS=False 2025-07-17T07:05:55.8472448Z GITHUB_REF=refs/heads/main 2025-07-17T07:05:55.8472701Z SHARD_NUMBER=1 2025-07-17T07:05:55.8472943Z GITHUB_REF_PROTECTED=true 2025-07-17T07:05:55.8473219Z HOME=/var/lib/jenkins 2025-07-17T07:05:55.8473491Z GITHUB_API_URL=https://api.github.com 2025-07-17T07:05:55.8473831Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-07-17T07:05:55.8474130Z UCX_COMMIT= 2025-07-17T07:05:55.8474347Z USE_SYSTEM_NCCL=1 2025-07-17T07:05:55.8474576Z NUM_TEST_SHARDS=5 2025-07-17T07:05:55.8474807Z UCX_HOME=/usr 2025-07-17T07:05:55.8475400Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.8476196Z JOB_NAME=linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T07:05:55.8476972Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.8477812Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-07-17T07:05:55.8478320Z GITHUB_EVENT_NAME=push 2025-07-17T07:05:55.8478579Z DASHBOARD_TAG= 2025-07-17T07:05:55.8478817Z GITHUB_RUN_ID=16337959907 2025-07-17T07:05:55.8479089Z INSTALLED_OPENBLAS= 2025-07-17T07:05:55.8479711Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.8480427Z GITHUB_ACTOR=pytorchmergebot 2025-07-17T07:05:55.8480713Z PR_NUMBER= 2025-07-17T07:05:55.8480916Z DESIRED_CUDA= 2025-07-17T07:05:55.8481146Z GITHUB_RUN_ATTEMPT=1 2025-07-17T07:05:55.8481390Z VALGRIND=ON 2025-07-17T07:05:55.8481639Z ANACONDA_PYTHON_VERSION=3.13 2025-07-17T07:05:55.8482207Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-07-17T07:05:55.8482848Z TERM=vt100 2025-07-17T07:05:55.8483059Z INSTALLED_VISION=yes 2025-07-17T07:05:55.8483306Z BRANCH=main 2025-07-17T07:05:55.8483536Z SCCACHE_REGION=us-east-1 2025-07-17T07:05:55.8483805Z OPENSSL_ROOT_DIR=/opt/openssl 2025-07-17T07:05:55.8484095Z CUDA_PATH=/usr/local/cuda 2025-07-17T07:05:55.8484631Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-07-17T07:05:55.8485249Z GITHUB_SERVER_URL=https://github.com 2025-07-17T07:05:55.8485547Z UCC_COMMIT= 2025-07-17T07:05:55.8485766Z REENABLED_ISSUES= 2025-07-17T07:05:55.8485998Z DOCS= 2025-07-17T07:05:55.8486189Z SHLVL=1 2025-07-17T07:05:55.8486391Z MAX_JOBS=14 2025-07-17T07:05:55.8486621Z GITHUB_ACTOR_ID=97764156 2025-07-17T07:05:55.8486959Z GITHUB_WORKFLOW_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T07:05:55.8487353Z GITHUB_REF_NAME=main 2025-07-17T07:05:55.8487746Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-07-17T07:05:55.8488183Z GITHUB_JOB=test 2025-07-17T07:05:55.8488418Z NO_TEST_TIMEOUT=False 2025-07-17T07:05:55.8488675Z TD_DISTRIBUTED=False 2025-07-17T07:05:55.8488935Z GITHUB_REPOSITORY=pytorch/pytorch 2025-07-17T07:05:55.8489247Z GITHUB_RETENTION_DAYS=90 2025-07-17T07:05:55.8489518Z OPENSSL_DIR=/opt/openssl 2025-07-17T07:05:55.8489777Z GITHUB_ACTION_REPOSITORY= 2025-07-17T07:05:55.8490584Z 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 2025-07-17T07:05:55.8491586Z GITHUB_BASE_REF= 2025-07-17T07:05:55.8491823Z INSTALLED_ACL= 2025-07-17T07:05:55.8492161Z ARTIFACTS_FILE_SUFFIX=test-default-1-5-linux.4xlarge_46154839625 2025-07-17T07:05:55.8492669Z CI=true 2025-07-17T07:05:55.8492926Z GITHUB_REPOSITORY_OWNER=pytorch 2025-07-17T07:05:55.8493259Z RUST_LOG=sccache::server=error 2025-07-17T07:05:55.8493541Z JOB_ID=46154839625 2025-07-17T07:05:55.8493778Z GITHUB_HEAD_REF= 2025-07-17T07:05:55.8494021Z GITHUB_ACTION_REF= 2025-07-17T07:05:55.8494310Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-07-17T07:05:55.8494685Z TEST_SHOWLOCALS=False 2025-07-17T07:05:55.8494949Z GITHUB_WORKFLOW=pull 2025-07-17T07:05:55.8495287Z DEBIAN_FRONTEND=noninteractive 2025-07-17T07:05:55.8495939Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_651bc552-65e7-44f9-af43-6c333b969609 2025-07-17T07:05:55.8496597Z NO_TD=False 2025-07-17T07:05:55.8496828Z SKIP_SCCACHE_INITIALIZATION=1 2025-07-17T07:05:55.8497143Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-07-17T07:05:55.8497464Z _=/usr/bin/env 2025-07-17T07:05:55.8497688Z + echo 'Testing pytorch' 2025-07-17T07:05:55.8497954Z Testing pytorch 2025-07-17T07:05:55.8498200Z + export LANG=C.UTF-8 2025-07-17T07:05:55.8498437Z + LANG=C.UTF-8 2025-07-17T07:05:55.8498663Z + PR_NUMBER= 2025-07-17T07:05:55.8498902Z + [[ default == \d\e\f\a\u\l\t ]] 2025-07-17T07:05:55.8499197Z + export CUDA_VISIBLE_DEVICES=0 2025-07-17T07:05:55.8499495Z + CUDA_VISIBLE_DEVICES=0 2025-07-17T07:05:55.8499777Z + export HIP_VISIBLE_DEVICES=0 2025-07-17T07:05:55.8500070Z + HIP_VISIBLE_DEVICES=0 2025-07-17T07:05:55.8500334Z + [[ default == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-07-17T07:05:55.8500657Z + [[ default == \s\l\o\w ]] 2025-07-17T07:05:55.8500996Z + [[ linux-jammy-py3.13-clang12 == *slow-gradcheck* ]] 2025-07-17T07:05:55.8501386Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-07-17T07:05:55.8501752Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-07-17T07:05:55.8502115Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-07-17T07:05:55.8502439Z + [[ default == *crossref* ]] 2025-07-17T07:05:55.8502768Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-07-17T07:05:55.8503121Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-07-17T07:05:55.8516095Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-07-17T07:05:55.8516491Z + pip_install ninja==1.10.2 2025-07-17T07:05:55.8516868Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-07-17T07:05:55.8517361Z + python3 -m pip install --progress-bar off ninja==1.10.2 2025-07-17T07:05:56.5104975Z Collecting ninja==1.10.2 2025-07-17T07:05:56.5271262Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-07-17T07:05:56.5540781Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-07-17T07:05:56.7564707Z Installing collected packages: ninja 2025-07-17T07:05:56.7565123Z Attempting uninstall: ninja 2025-07-17T07:05:56.7598892Z Found existing installation: ninja 1.11.1.3 2025-07-17T07:05:56.7619754Z Uninstalling ninja-1.11.1.3: 2025-07-17T07:05:56.7699891Z Successfully uninstalled ninja-1.11.1.3 2025-07-17T07:05:56.8137447Z Successfully installed ninja-1.10.2 2025-07-17T07:05:56.8764404Z + 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 2025-07-17T07:05:56.8766100Z + 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 2025-07-17T07:05:56.8767249Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-07-17T07:05:56.8767622Z + [[ linux-jammy-py3.13-clang12 == *asan* ]] 2025-07-17T07:05:56.8768047Z + [[ linux-jammy-py3.13-clang12 == *-debug* ]] 2025-07-17T07:05:56.8768421Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-07-17T07:05:56.8769227Z + echo 'We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass' 2025-07-17T07:05:56.8769893Z We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass 2025-07-17T07:05:56.8770580Z + cd test 2025-07-17T07:05:56.8770964Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-07-17T07:05:58.1801515Z + [[ default == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-07-17T07:05:58.1801917Z + [[ default == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-07-17T07:05:58.1805373Z + [[ default == \l\e\g\a\c\y\_\n\v\i\d\i\a\_\d\r\i\v\e\r ]] 2025-07-17T07:05:58.1805896Z + DYNAMO_BENCHMARK_FLAGS=() 2025-07-17T07:05:58.1806251Z + [[ default == *pr_time_benchmarks* ]] 2025-07-17T07:05:58.1806591Z + [[ default == *dynamo_eager* ]] 2025-07-17T07:05:58.1806964Z + [[ default == *aot_eager* ]] 2025-07-17T07:05:58.1807248Z + [[ default == *aot_inductor* ]] 2025-07-17T07:05:58.1807620Z + [[ default == *max_autotune_inductor* ]] 2025-07-17T07:05:58.1807980Z + [[ default == *inductor* ]] 2025-07-17T07:05:58.1808325Z + [[ default == *dynamic* ]] 2025-07-17T07:05:58.1808600Z + [[ default == *cpu* ]] 2025-07-17T07:05:58.1808906Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2025-07-17T07:05:58.1965901Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-07-17T07:05:58.1966290Z + [[ linux-jammy-py3.13-clang12 == *-bazel-* ]] 2025-07-17T07:05:58.1968926Z + cd test 2025-07-17T07:05:58.1969525Z + python -c 'import torch; print(torch.__config__.show())' 2025-07-17T07:05:59.2372060Z PyTorch built with: 2025-07-17T07:05:59.2372596Z - GCC 4.2 2025-07-17T07:05:59.2372833Z - C++ Version: 201703 2025-07-17T07:05:59.2373107Z - clang 12.0.1 2025-07-17T07:05:59.2373669Z - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-07-17T07:05:59.2374443Z - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-07-17T07:05:59.2374889Z - OpenMP 201811 2025-07-17T07:05:59.2375234Z - LAPACK is enabled (usually provided by MKL) 2025-07-17T07:05:59.2375631Z - NNPACK is enabled 2025-07-17T07:05:59.2375910Z - CPU capability usage: AVX512 2025-07-17T07:05:59.2381493Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=a38f433be2e94a64b095a44ba39879d02d0c2316, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wsuggest-override -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Qunused-arguments -faligned-new -Werror -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.9.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, 2025-07-17T07:05:59.2387085Z 2025-07-17T07:05:59.4589898Z + cd test 2025-07-17T07:05:59.4590415Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-07-17T07:06:00.5052641Z ATen/Parallel: 2025-07-17T07:06:00.5053134Z at::get_num_threads() : 8 2025-07-17T07:06:00.5053430Z at::get_num_interop_threads() : 8 2025-07-17T07:06:00.5053737Z OpenMP 201811 2025-07-17T07:06:00.5053984Z omp_get_max_threads() : 8 2025-07-17T07:06:00.5054558Z Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-07-17T07:06:00.5055503Z mkl_get_max_threads() : 8 2025-07-17T07:06:00.5055907Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-07-17T07:06:00.5056382Z std::thread::hardware_concurrency() : 16 2025-07-17T07:06:00.5056836Z Environment variables: 2025-07-17T07:06:00.5057113Z OMP_NUM_THREADS : [not set] 2025-07-17T07:06:00.5057400Z MKL_NUM_THREADS : [not set] 2025-07-17T07:06:00.5057683Z ATen parallel backend: OpenMP 2025-07-17T07:06:00.5057887Z 2025-07-17T07:06:00.7209440Z + [[ default == *numpy_2* ]] 2025-07-17T07:06:00.7210142Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-07-17T07:06:00.7210829Z + [[ default == *backward* ]] 2025-07-17T07:06:00.7211254Z + [[ default == *xla* ]] 2025-07-17T07:06:00.7211514Z + [[ default == *executorch* ]] 2025-07-17T07:06:00.7211814Z + [[ default == \j\i\t\_\l\e\g\a\c\y ]] 2025-07-17T07:06:00.7212168Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-07-17T07:06:00.7212514Z + [[ default == distributed ]] 2025-07-17T07:06:00.7212845Z + [[ default == *operator_benchmark* ]] 2025-07-17T07:06:00.7213184Z + [[ default == *inductor_distributed* ]] 2025-07-17T07:06:00.7213509Z + [[ default == *inductor-halide* ]] 2025-07-17T07:06:00.7213842Z + [[ default == *inductor-triton-cpu* ]] 2025-07-17T07:06:00.7214209Z + [[ default == *inductor-micro-benchmark* ]] 2025-07-17T07:06:00.7214554Z + [[ default == *huggingface* ]] 2025-07-17T07:06:00.7214832Z + [[ default == *timm* ]] 2025-07-17T07:06:00.7215102Z + [[ default == cachebench ]] 2025-07-17T07:06:00.7215477Z + [[ default == verify_cachebench ]] 2025-07-17T07:06:00.7215780Z + [[ default == *torchbench* ]] 2025-07-17T07:06:00.7216090Z + [[ default == *inductor_cpp_wrapper* ]] 2025-07-17T07:06:00.7216423Z + [[ default == *inductor* ]] 2025-07-17T07:06:00.7216694Z + [[ default == *einops* ]] 2025-07-17T07:06:00.7216982Z + [[ default == *dynamo_wrapped* ]] 2025-07-17T07:06:00.7217317Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-07-17T07:06:00.7217623Z + [[ 1 == 1 ]] 2025-07-17T07:06:00.7217861Z + [[ 5 -gt 1 ]] 2025-07-17T07:06:00.7218124Z + test_lazy_tensor_meta_reference_disabled 2025-07-17T07:06:00.7218521Z + export TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1 2025-07-17T07:06:00.7218966Z + TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1 2025-07-17T07:06:00.7219423Z + echo 'Testing lazy tensor operations without meta reference' 2025-07-17T07:06:00.7219890Z Testing lazy tensor operations without meta reference 2025-07-17T07:06:00.7220373Z + python test/run_test.py --include lazy/test_ts_opinfo.py --verbose 2025-07-17T07:06:02.9883863Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:06:02.9885510Z import pkg_resources 2025-07-17T07:06:04.4206901Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-07-17T07:06:04.4752892Z Ignoring disabled issues: [''] 2025-07-17T07:06:04.5015028Z Found test times from artifacts 2025-07-17T07:06:04.6039306Z Found test times from artifacts 2025-07-17T07:06:04.6071624Z Running all tests 2025-07-17T07:06:04.6074703Z Running parallel tests on 3 processes 2025-07-17T07:06:04.6075365Z Name: tests to run (est. time: 0.43min) 2025-07-17T07:06:04.6075769Z Serial tests (0): 2025-07-17T07:06:04.6076066Z Parallel tests (1): 2025-07-17T07:06:04.6076390Z lazy/test_ts_opinfo 1/1 2025-07-17T07:06:04.6076736Z Name: excluded (est. time: 0.0min) 2025-07-17T07:06:04.6077256Z Serial tests (0): 2025-07-17T07:06:04.6077691Z Parallel tests (0): 2025-07-17T07:06:04.6078043Z Running lazy/test_ts_opinfo 1/1 ... [2025-07-17 07:06:04.607563] 2025-07-17T07:06:04.6078452Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:06:04.6080974Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_ts_opinfo.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:06:04.607861] 2025-07-17T07:06:08.6273619Z 2025-07-17T07:06:08.6275093Z lazy/test_ts_opinfo 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_ts_opinfo_1.1_5d83bfb2ecd72e22_.log 2025-07-17T07:06:08.6275913Z Running 0 items in this shard: 2025-07-17T07:06:08.6276121Z 2025-07-17T07:06:08.8794725Z Uploading artifacts took 0.25 seconds 2025-07-17T07:06:11.2804342Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:06:11.2807295Z import pkg_resources 2025-07-17T07:06:11.2809999Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:06:11.2812795Z import pkg_resources 2025-07-17T07:06:11.2833240Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:06:11.2836048Z import pkg_resources 2025-07-17T07:06:11.3973812Z Running lazy/test_ts_opinfo 1/1 ... [2025-07-17 07:06:11.397017] 2025-07-17T07:06:11.3974621Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:06:11.3976606Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_ts_opinfo.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:06:11.397363] 2025-07-17T07:06:26.7822055Z 2025-07-17T07:06:26.7822960Z lazy/test_ts_opinfo 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_ts_opinfo_1.1_e48750171099314d_.log 2025-07-17T07:06:26.7941998Z Running 292 items in this shard: test/lazy/test_ts_opinfo.py::TestLazyTensor::testConvolutionBackward, test/lazy/test_ts_opinfo.py::TestLazyTensor::test_tensor_ctr, test/lazy/test_ts_opinfo.py::TestLazyTensor::test_view_mark_step_preserved, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_block_diag_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diag_embed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_logsumexp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_min_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_narrow_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_new_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_norm_nuc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_block_diag_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diag_embed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_logsumexp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_min_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_narrow_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_new_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_norm_nuc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_correctness_with_reusing_ir_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy__softmax_backward_data_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_abs_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addcdiv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addcmul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_addmm_decomposed_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_alias_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_arange_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_as_strided_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_as_strided_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_baddbmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_bernoulli_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_bmm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_clamp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_clamp_min_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_constant_pad_nd_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cos_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_cumsum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_diagonal_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_diagonal_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_floor_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_no_rounding_mode_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_div_trunc_rounding_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_empty_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_empty_strided_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_eq_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_exp_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_expand_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_flip_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_floor_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_frac_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_gather_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_ge_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_grid_sampler_2d_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_gt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_le_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_log2_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_log_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_lt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_masked_fill_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_pool2d_with_indices_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_max_reduction_with_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_maximum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mean_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_min_binary_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_min_reduction_no_dim_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_minimum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mul_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_mv_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_batch_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_dropout_backward_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_native_layer_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_ne_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_neg_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_fro_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_norm_inf_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_permute_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_pow_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_reciprocal_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_remainder_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_repeat_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_rsqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_scatter_add_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_select_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sgn_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sigmoid_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_slice_scatter_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sort_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sqrt_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_squeeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_stack_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_std_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_std_unbiased_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sub_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_sum_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_t_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_tanh_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_topk_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_trace_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_transpose_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_tril_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_triu_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_trunc_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_unfold_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_uniform_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_unsqueeze_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyOpInfoCPU::test_dispatched_to_lazy_view_copy_cpu_float32, test/lazy/test_ts_opinfo.py::TestLazyDynamicOps::test_adaptiveavgpool3d_dynamic, test/lazy/test_ts_opinfo.py::TestLazyDynamicOps::test_nonzero_dynamic 2025-07-17T07:06:26.8057478Z 2025-07-17T07:06:27.5154297Z Running test batch 'tests to run' cost 22.91 seconds 2025-07-17T07:06:28.1462905Z 2025-07-17T07:06:28.1463323Z real 0m27.425s 2025-07-17T07:06:28.1463589Z user 1m28.498s 2025-07-17T07:06:28.1463815Z sys 0m4.618s 2025-07-17T07:06:28.1464151Z + export -n TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE 2025-07-17T07:06:28.1464594Z + test_without_numpy 2025-07-17T07:06:28.1467526Z ++ dirname .ci/pytorch/test.sh 2025-07-17T07:06:28.1486857Z + pushd .ci/pytorch 2025-07-17T07:06:28.1487150Z ~/workspace/.ci/pytorch ~/workspace 2025-07-17T07:06:28.1488268Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');from unittest import TestCase;import torch;x=torch.randn(3,3);TestCase().assertRaises(RuntimeError, lambda: x.numpy())' 2025-07-17T07:06:28.7749143Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/functional_tensor.py:279: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:82.) 2025-07-17T07:06:28.7750644Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-07-17T07:06:29.3292946Z + python -c 'import sys;sys.path.insert(0, '\''fake_numpy'\'');import torch;print(torch.tensor([torch.tensor(0.), torch.tensor(1.)]))' 2025-07-17T07:06:29.9371412Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_subclasses/functional_tensor.py:279: UserWarning: Failed to initialize NumPy: Sorry PyTorch, but our NumPy is in the other folder (Triggered internally at /var/lib/jenkins/workspace/torch/csrc/utils/tensor_numpy.cpp:82.) 2025-07-17T07:06:29.9373057Z cpu = _conversion_method_template(device=torch.device("cpu")) 2025-07-17T07:06:30.2941087Z tensor([0., 1.]) 2025-07-17T07:06:30.5095089Z + [[ default == *dynamo_wrapped* ]] 2025-07-17T07:06:30.5095508Z + popd 2025-07-17T07:06:30.5095710Z ~/workspace 2025-07-17T07:06:30.5095939Z + install_torchvision 2025-07-17T07:06:30.5096197Z + local orig_preload 2025-07-17T07:06:30.5096436Z + local commit 2025-07-17T07:06:30.5099039Z ++ get_pinned_commit vision 2025-07-17T07:06:30.5099357Z ++ cat .github/ci_commit_pins/vision.txt 2025-07-17T07:06:30.5121864Z + commit=966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T07:06:30.5122257Z + orig_preload= 2025-07-17T07:06:30.5122598Z + '[' -n '' ']' 2025-07-17T07:06:30.5123446Z + pip_install --no-use-pep517 git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T07:06:30.5124156Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-07-17T07:06:30.5125105Z + python3 -m pip install --progress-bar off --no-use-pep517 git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T07:06:30.8310914Z Collecting git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T07:06:30.8315325Z Cloning https://github.com/pytorch/vision.git (to revision 966da7e46f65d6d49df3e31214470a4fe5cc8e66) to /tmp/pip-req-build-765mvl7v 2025-07-17T07:06:30.8334058Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-765mvl7v 2025-07-17T07:06:32.4697190Z Running command git rev-parse -q --verify 'sha^966da7e46f65d6d49df3e31214470a4fe5cc8e66' 2025-07-17T07:06:32.4717288Z Running command git fetch -q https://github.com/pytorch/vision.git 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T07:06:32.6138149Z Running command git checkout -q 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T07:06:32.9069556Z Resolved https://github.com/pytorch/vision.git to commit 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-07-17T07:06:34.6938656Z Preparing metadata (setup.py) ... [?25l- \ | done 2025-07-17T07:06:34.6978574Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.22.0a0+966da7e) (2.1.2) 2025-07-17T07:06:34.6982165Z Requirement already satisfied: torch in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.22.0a0+966da7e) (2.9.0a0+gita38f433) 2025-07-17T07:06:34.6986598Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torchvision==0.22.0a0+966da7e) (11.0.0) 2025-07-17T07:06:34.7043556Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.22.0a0+966da7e) (3.18.0) 2025-07-17T07:06:34.7048179Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.22.0a0+966da7e) (4.14.1) 2025-07-17T07:06:34.7056108Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.22.0a0+966da7e) (80.9.0) 2025-07-17T07:06:34.7060708Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.22.0a0+966da7e) (1.13.3) 2025-07-17T07:06:34.7064080Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.22.0a0+966da7e) (2.8.8) 2025-07-17T07:06:34.7067526Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.22.0a0+966da7e) (3.1.6) 2025-07-17T07:06:34.7070890Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch->torchvision==0.22.0a0+966da7e) (2025.5.1) 2025-07-17T07:06:34.7085703Z 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==0.22.0a0+966da7e) (1.3.0) 2025-07-17T07:06:34.7198994Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch->torchvision==0.22.0a0+966da7e) (3.0.2) 2025-07-17T07:06:34.7286899Z Building wheels for collected packages: torchvision 2025-07-17T07:06:34.7335096Z  DEPRECATION: Building 'torchvision' using the legacy setup.py bdist_wheel mechanism, which will be removed in a future version. pip 25.3 will enforce this behaviour change. A possible replacement is to use the standardized build interface by setting the `--use-pep517` option, (possibly combined with `--no-build-isolation`), or adding a `pyproject.toml` file to the source tree of 'torchvision'. Discussion can be found at https://github.com/pypa/pip/issues/6334 2025-07-17T07:07:26.2354021Z  Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done 2025-07-17T07:07:26.2391718Z [?25h Created wheel for torchvision: filename=torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl size=1199177 sha256=752f95d065b8ecf3366fee52c7075adcdbb366884fb008367a8e679316bfdd68 2025-07-17T07:07:26.2393102Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/89/53/07/16d05bff0990e585bfa232438657da6e5e4f6d0cea71bd3e09 2025-07-17T07:07:26.2429523Z Successfully built torchvision 2025-07-17T07:07:26.3896448Z Installing collected packages: torchvision 2025-07-17T07:07:26.8513888Z Successfully installed torchvision-0.22.0a0+966da7e 2025-07-17T07:07:26.9461166Z + '[' -n '' ']' 2025-07-17T07:07:26.9461454Z + test_python_shard 1 2025-07-17T07:07:26.9461733Z + [[ -z 5 ]] 2025-07-17T07:07:26.9462388Z + python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --shard 1 5 --verbose --upload-artifacts-while-running 2025-07-17T07:07:29.1809000Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:07:29.1810670Z import pkg_resources 2025-07-17T07:07:30.6330789Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-07-17T07:07:30.6593562Z Found test times from artifacts 2025-07-17T07:07:30.7625353Z Found test times from artifacts 2025-07-17T07:07:30.7659179Z Running all tests 2025-07-17T07:07:30.8042111Z Running parallel tests on 3 processes 2025-07-17T07:07:30.8051348Z Name: tests to run (est. time: 92.02min) 2025-07-17T07:07:30.8051711Z Serial tests (48): 2025-07-17T07:07:30.8051989Z test_ci_sanity_check_fail 1/1 2025-07-17T07:07:30.8052337Z inductor/test_distributed_patterns 1/1 2025-07-17T07:07:30.8052660Z test_overrides 1/1 2025-07-17T07:07:30.8052934Z test_reductions 1/1 2025-07-17T07:07:30.8053191Z doctests 1/1 2025-07-17T07:07:30.8053431Z test_namedtuple_return_api 1/1 2025-07-17T07:07:30.8053730Z test_nn 1/1 2025-07-17T07:07:30.8053971Z test_spectral_ops 1/1 2025-07-17T07:07:30.8054257Z distributions/test_distributions 1/1 2025-07-17T07:07:30.8054605Z test_extension_utils 1/1 2025-07-17T07:07:30.8054892Z test_cpp_api_parity 1/1 2025-07-17T07:07:30.8055224Z inductor/test_cutlass_backend 1/1 2025-07-17T07:07:30.8055598Z test_cpp_extensions_open_device_registration 1/1 2025-07-17T07:07:30.8055986Z test_transformers_privateuse1 1/1 2025-07-17T07:07:30.8056305Z inductor/test_flex_attention 1/1 2025-07-17T07:07:30.8056623Z test_openreg 1/1 2025-07-17T07:07:30.8056869Z test_fx 1/1 2025-07-17T07:07:30.8057107Z test_show_pickle 1/1 2025-07-17T07:07:30.8057379Z inductor/test_benchmark_fusion 1/1 2025-07-17T07:07:30.8057710Z inductor/test_max_autotune 1/1 2025-07-17T07:07:30.8058010Z test_utils 1/1 2025-07-17T07:07:30.8058251Z test_multiprocessing 1/1 2025-07-17T07:07:30.8058536Z test_tensorexpr 1/1 2025-07-17T07:07:30.8058800Z test_jit_disabled 1/1 2025-07-17T07:07:30.8059077Z test_cpp_extensions_mtia_backend 1/1 2025-07-17T07:07:30.8059427Z test_cpp_extensions_stream_and_event 1/1 2025-07-17T07:07:30.8059756Z test_dispatch 1/1 2025-07-17T07:07:30.8060003Z test_fake_tensor 1/1 2025-07-17T07:07:30.8060276Z test_python_dispatch 1/1 2025-07-17T07:07:30.8060568Z test_autograd_fallback 1/1 2025-07-17T07:07:30.8060850Z test_cuda_nvml_based_avail 1/1 2025-07-17T07:07:30.8061161Z test_tensor_creation_ops 1/1 2025-07-17T07:07:30.8061499Z test_cuda_trace 1/1 2025-07-17T07:07:30.8062037Z test_autocast 1/1 2025-07-17T07:07:30.8062310Z dynamo/test_fake_distributed 1/1 2025-07-17T07:07:30.8062624Z test_cpp_extensions_jit 1/1 2025-07-17T07:07:30.8062926Z test_cuda_primary_ctx 1/1 2025-07-17T07:07:30.8063312Z test_torch 1/1 2025-07-17T07:07:30.8063543Z test_native_mha 1/1 2025-07-17T07:07:30.8063808Z test_sort_and_select 1/1 2025-07-17T07:07:30.8064107Z test_multiprocessing_spawn 1/1 2025-07-17T07:07:30.8064418Z nn/test_pooling 1/1 2025-07-17T07:07:30.8064673Z test_mobile_optimizer 1/1 2025-07-17T07:07:30.8064966Z nn/test_convolution 1/1 2025-07-17T07:07:30.8065247Z test_autoload_disable 1/1 2025-07-17T07:07:30.8065528Z test_autoload_enable 1/1 2025-07-17T07:07:30.8065825Z test_cpp_extensions_aot_ninja 1/1 2025-07-17T07:07:30.8066159Z test_cpp_extensions_aot_no_ninja 1/1 2025-07-17T07:07:30.8066472Z Parallel tests (135): 2025-07-17T07:07:30.8066753Z inductor/test_cudagraph_trees 1/1 2025-07-17T07:07:30.8067101Z dynamo/test_activation_checkpointing 1/1 2025-07-17T07:07:30.8067502Z inductor/test_torchinductor_codegen_dynamic_shapes 2/3 2025-07-17T07:07:30.8067904Z functorch/test_aotdispatch 5/6 2025-07-17T07:07:30.8068222Z functorch/test_aotdispatch 6/6 2025-07-17T07:07:30.8068513Z test_foreach 1/1 2025-07-17T07:07:30.8068798Z dynamo/cpython/3_13/test_generator_stop 1/1 2025-07-17T07:07:30.8069193Z dynamo/cpython/3_13/test_exception_variations 1/1 2025-07-17T07:07:30.8069585Z dynamo/cpython/3_13/test_int_literal 1/1 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1/1 2025-07-17T07:07:30.8080367Z functorch/test_ac_logging 1/1 2025-07-17T07:07:30.8080669Z test_custom_ops 1/1 2025-07-17T07:07:30.8080923Z test_ao_sparsity 1/1 2025-07-17T07:07:30.8081201Z torch_np/test_binary_ufuncs 1/1 2025-07-17T07:07:30.8081501Z test_hop_infra 1/1 2025-07-17T07:07:30.8081756Z torch_np/test_unary_ufuncs 1/1 2025-07-17T07:07:30.8082056Z dynamo/test_nops 1/1 2025-07-17T07:07:30.8082511Z inductor/test_move_constructors_to_cuda 1/1 2025-07-17T07:07:30.8082852Z backends/xeon/test_launch 1/1 2025-07-17T07:07:30.8083165Z inductor/test_triton_kernels 1/1 2025-07-17T07:07:30.8083488Z inductor/test_async_compile 1/1 2025-07-17T07:07:30.8083899Z torch_np/numpy_tests/linalg/test_linalg 1/1 2025-07-17T07:07:30.8084253Z dynamo/test_skip_non_tensor 1/1 2025-07-17T07:07:30.8084565Z dynamo/test_interop 1/1 2025-07-17T07:07:30.8084861Z inductor/test_torchinductor 1/2 2025-07-17T07:07:30.8085166Z inductor/test_metrics 1/1 2025-07-17T07:07:30.8085487Z inductor/test_torchinductor_opinfo 3/26 2025-07-17T07:07:30.8085845Z inductor/test_torchinductor_opinfo 5/26 2025-07-17T07:07:30.8086189Z inductor/test_torchinductor_opinfo 7/26 2025-07-17T07:07:30.8086552Z inductor/test_torchinductor_opinfo 18/26 2025-07-17T07:07:30.8086913Z inductor/test_torchinductor_opinfo 20/26 2025-07-17T07:07:30.8087259Z inductor/test_torchinductor_opinfo 22/26 2025-07-17T07:07:30.8087607Z inductor/test_cuda_repro 1/1 2025-07-17T07:07:30.8087916Z inductor/test_helion_kernels 1/1 2025-07-17T07:07:30.8088215Z torch_np/test_random 1/1 2025-07-17T07:07:30.8088497Z test_namedtensor 1/1 2025-07-17T07:07:30.8088773Z export/test_package 1/1 2025-07-17T07:07:30.8089056Z dynamo/test_config 1/1 2025-07-17T07:07:30.8089327Z inductor/test_codecache 1/1 2025-07-17T07:07:30.8089671Z torch_np/numpy_tests/core/test_scalarinherit 1/1 2025-07-17T07:07:30.8090042Z export/test_upgrader 1/1 2025-07-17T07:07:30.8090319Z dynamo/test_compile 1/1 2025-07-17T07:07:30.8090601Z dynamo/test_after_aot 1/1 2025-07-17T07:07:30.8090877Z test_hub 1/1 2025-07-17T07:07:30.8091118Z dynamo/test_autograd_function 1/1 2025-07-17T07:07:30.8091431Z export/test_db 1/1 2025-07-17T07:07:30.8091682Z test_optim 1/1 2025-07-17T07:07:30.8091943Z inductor/test_cooperative_reductions 1/1 2025-07-17T07:07:30.8092300Z inductor/test_cpu_select_algorithm 1/16 2025-07-17T07:07:30.8092657Z inductor/test_cpu_select_algorithm 5/16 2025-07-17T07:07:30.8092997Z inductor/test_cpu_select_algorithm 6/16 2025-07-17T07:07:30.8093352Z inductor/test_cpu_select_algorithm 16/16 2025-07-17T07:07:30.8093692Z inductor/test_halide 1/1 2025-07-17T07:07:30.8093988Z inductor/test_inplacing_pass 1/1 2025-07-17T07:07:30.8094290Z inductor/test_perf 1/1 2025-07-17T07:07:30.8094565Z test_legacy_vmap 1/1 2025-07-17T07:07:30.8094843Z nn/test_multihead_attention 1/1 2025-07-17T07:07:30.8095238Z inductor/test_binary_folding 1/1 2025-07-17T07:07:30.8095645Z inductor/test_torchinductor_codegen_config_overrides 1/1 2025-07-17T07:07:30.8096082Z torch_np/numpy_tests/core/test_numeric 1/1 2025-07-17T07:07:30.8096440Z dynamo/test_backward_higher_order_ops 1/1 2025-07-17T07:07:30.8096784Z dynamo/test_optimizers 1/1 2025-07-17T07:07:30.8097062Z test_weak 1/1 2025-07-17T07:07:30.8097309Z dynamo/test_graph_deduplication 1/1 2025-07-17T07:07:30.8097637Z dynamo/test_sets 1/1 2025-07-17T07:07:30.8097921Z distributions/test_constraints 1/1 2025-07-17T07:07:30.8098228Z test_tensorboard 1/1 2025-07-17T07:07:30.8098526Z inductor/test_split_cat_fx_aten_passes 1/1 2025-07-17T07:07:30.8098880Z dynamo/test_structured_trace 1/1 2025-07-17T07:07:30.8099202Z profiler/test_cpp_thread 1/1 2025-07-17T07:07:30.8099499Z dynamo/test_decorators 1/1 2025-07-17T07:07:30.8099795Z dynamo/test_debug_utils 1/1 2025-07-17T07:07:30.8100096Z functorch/test_ac_knapsack 1/1 2025-07-17T07:07:30.8100395Z functorch/test_ac 1/1 2025-07-17T07:07:30.8100672Z dynamo/test_base_hop 1/1 2025-07-17T07:07:30.8100955Z dynamo/test_modes 1/1 2025-07-17T07:07:30.8101228Z inductor/test_inductor_freezing 1/1 2025-07-17T07:07:30.8101552Z dynamo/test_profiler 1/1 2025-07-17T07:07:30.8101832Z dynamo/test_sources 1/1 2025-07-17T07:07:30.8102099Z inductor/test_config 1/1 2025-07-17T07:07:30.8102374Z test_datapipe 1/1 2025-07-17T07:07:30.8102740Z functorch/test_logging 1/1 2025-07-17T07:07:30.8103021Z dynamo/test_flat_apply 1/1 2025-07-17T07:07:30.8103313Z torch_np/test_indexing 1/1 2025-07-17T07:07:30.8103612Z dynamo/test_deviceguard 1/1 2025-07-17T07:07:30.8103958Z test_content_store 1/1 2025-07-17T07:07:30.8104240Z export/test_unflatten 1/1 2025-07-17T07:07:30.8104539Z lazy/test_step_closures 1/1 2025-07-17T07:07:30.8104852Z inductor/test_fused_attention 1/1 2025-07-17T07:07:30.8105160Z test_ops_gradients 1/2 2025-07-17T07:07:30.8105444Z inductor/test_padding 1/1 2025-07-17T07:07:30.8105737Z dynamo/test_torchrec 1/1 2025-07-17T07:07:30.8106019Z torch_np/test_reductions 1/1 2025-07-17T07:07:30.8106334Z profiler/test_profiler_tree 1/1 2025-07-17T07:07:30.8106651Z test_type_promotion 1/1 2025-07-17T07:07:30.8106933Z dynamo/test_python_autograd 1/1 2025-07-17T07:07:30.8107250Z test_jit_autocast 1/1 2025-07-17T07:07:30.8107541Z profiler/test_record_function 1/1 2025-07-17T07:07:30.8107844Z test_prims 1/1 2025-07-17T07:07:30.8108098Z lazy/test_reuse_ir 1/1 2025-07-17T07:07:30.8108390Z profiler/test_execution_trace 1/1 2025-07-17T07:07:30.8108700Z profiler/test_torch_tidy 1/1 2025-07-17T07:07:30.8109043Z test_rename_privateuse1_to_existing_device 1/1 2025-07-17T07:07:30.8109417Z Name: excluded (est. time: 0.0min) 2025-07-17T07:07:30.8109719Z Serial tests (0): 2025-07-17T07:07:30.8109952Z Parallel tests (0): 2025-07-17T07:07:30.8110333Z Running test_ci_sanity_check_fail 1/1 ... [2025-07-17 07:07:30.808547] 2025-07-17T07:07:30.8110772Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:07:30.8111844Z 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'] ... [2025-07-17 07:07:30.808850] 2025-07-17T07:07:42.9121742Z Uploading artifacts took 0.31 seconds 2025-07-17T07:07:42.9124253Z Running inductor/test_distributed_patterns 1/1 ... [2025-07-17 07:07:42.912270] 2025-07-17T07:07:42.9124762Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:07:42.9127933Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_distributed_patterns.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:07:42.912587] 2025-07-17T07:08:13.1182907Z 2025-07-17T07:08:13.1184245Z inductor/test_distributed_patterns 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_distributed_patterns_1.1_412e94590922a583_.log 2025-07-17T07:08:13.1196669Z Running 20 items in this shard: test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_aot_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_fake_distributed_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_intermediate_hook_with_closure, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_intermediate_hook_with_nested_closure, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_aot, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_eager, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_inductor, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_module_backward_hooks_multi_layers, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return3, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_nn_param_return4, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_nonzero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_cpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_storage_resize_zero_gpu, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_preserve_version_counter2, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter1, test/inductor/test_distributed_patterns.py::DistributedPatternTests::test_unsafe_set_version_counter2 2025-07-17T07:08:13.1206147Z 2025-07-17T07:08:13.1206347Z Running test_overrides 1/1 ... [2025-07-17 07:08:13.118594] 2025-07-17T07:08:13.1206742Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:08:13.1207807Z 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'] ... [2025-07-17 07:08:13.118913] 2025-07-17T07:08:20.0922159Z 2025-07-17T07:08:20.0923103Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_af441a98257745f4_.log 2025-07-17T07:08:20.1424254Z Running 1467 items in this shard: test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_H___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_T___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__backward_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__base___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__cdata___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__post_accumulate_grad_hooks___get__, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_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, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_module_load, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_msort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_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, 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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, 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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, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_erfcx, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_expit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_gammainc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_gammaincc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_gammaln, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_hermite_polynomial_h, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_logit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_modified_bessel_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_modified_bessel_i1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_modified_bessel_k0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_modified_bessel_k1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_multigammaln, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_ndtr, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_ndtri, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_psi, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_scaled_modified_bessel_k1, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_u, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_v, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_shifted_chebyshev_polynomial_w, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_spherical_bessel_j0, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlog1py, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__C__special_special_zeta, test/test_overrides.py::TestTorchFunctionOverride::test_torch__assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__conj_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__functional_assert_async, test/test_overrides.py::TestTorchFunctionOverride::test_torch__fw_primal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lobpcg_lobpcg, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_pca_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__lowrank_svd_lowrank, test/test_overrides.py::TestTorchFunctionOverride::test_torch__make_dual_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__native_batch_norm_legit, test/test_overrides.py::TestTorchFunctionOverride::test_torch__neg_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__reshape_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__rowwise_prune, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sparse_broadcast_to_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch__sym_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch__values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_linear_prepack, test/test_overrides.py::TestTorchFunctionOverride::test_torch__wrapped_quantized_linear_prepacked, test/test_overrides.py::TestTorchFunctionOverride::test_torch_abs, test/test_overrides.py::TestTorchFunctionOverride::test_torch_absolute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_torch_affine_grid_generator, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_all, test/test_overrides.py::TestTorchFunctionOverride::test_torch_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_angle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_any, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats_with_counts, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_update_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bilinear, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bucketize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ccol_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_torch_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_choose_qparams_optimized, test/test_overrides.py::TestTorchFunctionOverride::test_torch_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clone, test/test_overrides.py::TestTorchFunctionOverride::test_torch_col_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_column_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_combinations, test/test_overrides.py::TestTorchFunctionOverride::test_torch_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concatenate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_torch_constant_pad_nd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_tbc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_similarity, test/test_overrides.py::TestTorchFunctionOverride::test_torch_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cov, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch_crow_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumulative_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diff, test/test_overrides.py::TestTorchFunctionOverride::test_torch_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_empty_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_eq, test/test_overrides.py::TestTorchFunctionOverride::test_torch_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expand_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_channel_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_tensor_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_quantize_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_gemm_matrix_fp16, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_quantized_matrix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_torch_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frobenius_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_full_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_block_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_broadcast_tensors, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cartesian_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_chain_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_einsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_lu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_meshgrid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_stft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_tensordot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unravel_index, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fused_moving_avg_obs_fake_quant, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gather, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ge, test/test_overrides.py::TestTorchFunctionOverride::test_torch_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ger, test/test_overrides.py::TestTorchFunctionOverride::test_torch_get_device, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gradient, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, 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 2025-07-17T07:08:20.1914069Z 2025-07-17T07:08:20.1914274Z Running test_reductions 1/1 ... [2025-07-17 07:08:20.094059] 2025-07-17T07:08:20.1914694Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:08:20.1915806Z 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'] ... [2025-07-17 07:08:20.094359] 2025-07-17T07:11:23.5480920Z 2025-07-17T07:11:23.5482036Z test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_1.1_ed55dae191f5f783_.log 2025-07-17T07:11:23.7264322Z 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, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_amin_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_aminmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_argminmax_axis_with_dim_one_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_large_axis_cpu, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_argminmax_multiple_cpu_float32, 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test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_count_nonzero_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_cumprod_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_cumsum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_dim_arg_reduction_scalar_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_all_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_amax_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_amin_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_any_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_count_nonzero_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_linalg_vector_norm_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_dim_default__refs_prod_cpu, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_cpu_device_mismatch_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_dim_reduction_uint8_overflow_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_integer_upcast_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_out_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_argmax_argmix_kthvalue_dim_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_cpu, test/test_reductions.py::TestReductionsCPU::test_var_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_var_large_input_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_mean_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability2_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability_cpu, test/test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float64 2025-07-17T07:11:23.8982108Z 2025-07-17T07:11:23.8982310Z Running doctests 1/1 ... [2025-07-17 07:11:23.553129] 2025-07-17T07:11:23.8982877Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch') 2025-07-17T07:11:23.8983368Z Listing tests 2025-07-17T07:11:23.8984015Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1493. 2025-07-17T07:11:23.8984918Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.8985303Z 2025-07-17T07:11:23.8985463Z dim_order(ambiguity_check=False) -> tuple 2025-07-17T07:11:23.8985696Z 2025-07-17T07:11:23.8985933Z Returns the uniquely determined tuple of int describing the dim order or 2025-07-17T07:11:23.8986413Z physical layout of :attr:`self`. 2025-07-17T07:11:23.8986610Z 2025-07-17T07:11:23.8986882Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-07-17T07:11:23.8987428Z starting from the outermost to the innermost dimension. 2025-07-17T07:11:23.8987725Z 2025-07-17T07:11:23.8987909Z Note that the dim order may not always be uniquely determined. 2025-07-17T07:11:23.8988612Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-07-17T07:11:23.8989538Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-07-17T07:11:23.8990316Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-07-17T07:11:23.8991033Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-07-17T07:11:23.8991607Z Otherwise, it will raise TypeError. 2025-07-17T07:11:23.8991818Z 2025-07-17T07:11:23.8991916Z Args: 2025-07-17T07:11:23.8992352Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-07-17T07:11:23.8992809Z 2025-07-17T07:11:23.8992932Z Examples:: 2025-07-17T07:11:23.8993055Z 2025-07-17T07:11:23.8993180Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-07-17T07:11:23.8993483Z (0, 1, 2, 3) 2025-07-17T07:11:23.8993784Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-07-17T07:11:23.8994145Z (0, 2, 1, 3) 2025-07-17T07:11:23.8994496Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-07-17T07:11:23.8994905Z (0, 2, 3, 1) 2025-07-17T07:11:23.8995158Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-07-17T07:11:23.8995470Z (0, 1, 2, 3) 2025-07-17T07:11:23.8995681Z >>> try: 2025-07-17T07:11:23.8995998Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-07-17T07:11:23.8996407Z ... except RuntimeError as e: 2025-07-17T07:11:23.8996691Z ... print(e) 2025-07-17T07:11:23.8997165Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-07-17T07:11:23.8997722Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-07-17T07:11:23.8998148Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-07-17T07:11:23.8998613Z ... ) # It can be mapped to contiguous format 2025-07-17T07:11:23.8998944Z (0, 1, 2, 3) 2025-07-17T07:11:23.8999171Z >>> try: 2025-07-17T07:11:23.8999473Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-07-17T07:11:23.9000153Z ... except TypeError as e: 2025-07-17T07:11:23.9000439Z ... print(e) 2025-07-17T07:11:23.9000817Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-07-17T07:11:23.9001185Z 2025-07-17T07:11:23.9001921Z .. warning:: 2025-07-17T07:11:23.9002271Z The dim_order tensor API is experimental and subject to change. 2025-07-17T07:11:23.9002582Z 2025-07-17T07:11:23.9002850Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9003217Z 2025-07-17T07:11:23.9003702Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=446. 2025-07-17T07:11:23.9004567Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9005191Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-07-17T07:11:23.9005549Z 2025-07-17T07:11:23.9005746Z This is helpful when you want to visualize data over some 2025-07-17T07:11:23.9006192Z range of inputs. See below for a plotting example. 2025-07-17T07:11:23.9006473Z 2025-07-17T07:11:23.9006640Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-07-17T07:11:23.9007116Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-07-17T07:11:23.9007607Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-07-17T07:11:23.9008082Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-07-17T07:11:23.9008553Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-07-17T07:11:23.9008967Z to the result shape. 2025-07-17T07:11:23.9009147Z 2025-07-17T07:11:23.9009238Z .. note:: 2025-07-17T07:11:23.9009549Z 0D inputs are treated equivalently to 1D inputs of a 2025-07-17T07:11:23.9009929Z single element. 2025-07-17T07:11:23.9010102Z 2025-07-17T07:11:23.9010192Z .. warning:: 2025-07-17T07:11:23.9010539Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-07-17T07:11:23.9011010Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-07-17T07:11:23.9011289Z 2025-07-17T07:11:23.9011462Z In the future `torch.meshgrid` will transition to 2025-07-17T07:11:23.9011837Z `indexing='xy'` as the default. 2025-07-17T07:11:23.9012068Z 2025-07-17T07:11:23.9012263Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-07-17T07:11:23.9012748Z this issue with the goal of migrating to NumPy's behavior. 2025-07-17T07:11:23.9013041Z 2025-07-17T07:11:23.9013131Z .. seealso:: 2025-07-17T07:11:23.9013295Z 2025-07-17T07:11:23.9013466Z :func:`torch.cartesian_prod` has the same effect but it 2025-07-17T07:11:23.9013885Z collects the data in a tensor of vectors. 2025-07-17T07:11:23.9014126Z 2025-07-17T07:11:23.9014228Z Args: 2025-07-17T07:11:23.9014621Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-07-17T07:11:23.9015270Z treated as tensors of size :math:`(1,)` automatically 2025-07-17T07:11:23.9015557Z 2025-07-17T07:11:23.9015757Z indexing: (str, optional): the indexing mode, either "xy" 2025-07-17T07:11:23.9016210Z or "ij", defaults to "ij". See warning for future changes. 2025-07-17T07:11:23.9016509Z 2025-07-17T07:11:23.9016665Z If "xy" is selected, the first dimension corresponds 2025-07-17T07:11:23.9017108Z to the cardinality of the second input and the second 2025-07-17T07:11:23.9017570Z dimension corresponds to the cardinality of the first 2025-07-17T07:11:23.9017940Z input. 2025-07-17T07:11:23.9018105Z 2025-07-17T07:11:23.9018255Z If "ij" is selected, the dimensions are in the same 2025-07-17T07:11:23.9018657Z order as the cardinality of the inputs. 2025-07-17T07:11:23.9018985Z 2025-07-17T07:11:23.9019086Z Returns: 2025-07-17T07:11:23.9019388Z seq (sequence of Tensors): If the input has :math:`N` 2025-07-17T07:11:23.9019832Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-07-17T07:11:23.9020373Z output will also have :math:`N` tensors, where each tensor 2025-07-17T07:11:23.9020792Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-07-17T07:11:23.9021039Z 2025-07-17T07:11:23.9021132Z Example:: 2025-07-17T07:11:23.9021273Z 2025-07-17T07:11:23.9021397Z >>> x = torch.tensor([1, 2, 3]) 2025-07-17T07:11:23.9021719Z >>> y = torch.tensor([4, 5, 6]) 2025-07-17T07:11:23.9021954Z 2025-07-17T07:11:23.9022149Z Observe the element-wise pairings across the grid, (1, 4), 2025-07-17T07:11:23.9022598Z (1, 5), ..., (3, 6). This is the same thing as the 2025-07-17T07:11:23.9022954Z cartesian product. 2025-07-17T07:11:23.9023294Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-07-17T07:11:23.9023672Z >>> grid_x 2025-07-17T07:11:23.9023924Z tensor([[1, 1, 1], 2025-07-17T07:11:23.9024184Z [2, 2, 2], 2025-07-17T07:11:23.9024454Z [3, 3, 3]]) 2025-07-17T07:11:23.9024723Z >>> grid_y 2025-07-17T07:11:23.9024959Z tensor([[4, 5, 6], 2025-07-17T07:11:23.9025228Z [4, 5, 6], 2025-07-17T07:11:23.9025497Z [4, 5, 6]]) 2025-07-17T07:11:23.9025670Z 2025-07-17T07:11:23.9025834Z This correspondence can be seen when these grids are 2025-07-17T07:11:23.9026221Z stacked properly. 2025-07-17T07:11:23.9026613Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-07-17T07:11:23.9027067Z ... torch.cartesian_prod(x, y)) 2025-07-17T07:11:23.9027384Z True 2025-07-17T07:11:23.9027533Z 2025-07-17T07:11:23.9027707Z `torch.meshgrid` is commonly used to produce a grid for 2025-07-17T07:11:23.9028104Z plotting. 2025-07-17T07:11:23.9028382Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-07-17T07:11:23.9028761Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-07-17T07:11:23.9029139Z >>> import matplotlib.pyplot as plt 2025-07-17T07:11:23.9029494Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-07-17T07:11:23.9029859Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-07-17T07:11:23.9030238Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-07-17T07:11:23.9030622Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-07-17T07:11:23.9030974Z >>> ax = plt.axes(projection='3d') 2025-07-17T07:11:23.9031358Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-07-17T07:11:23.9031724Z >>> plt.show() 2025-07-17T07:11:23.9031889Z 2025-07-17T07:11:23.9032016Z .. image:: ../_static/img/meshgrid.png 2025-07-17T07:11:23.9032346Z :width: 512 2025-07-17T07:11:23.9032510Z 2025-07-17T07:11:23.9032593Z 2025-07-17T07:11:23.9032970Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9033351Z 2025-07-17T07:11:23.9033845Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=842. 2025-07-17T07:11:23.9034716Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9035473Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-07-17T07:11:23.9035987Z 2025-07-17T07:11:23.9036139Z Returns the unique elements of the input tensor. 2025-07-17T07:11:23.9036411Z 2025-07-17T07:11:23.9036707Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-07-17T07:11:23.9037340Z this function also eliminates non-consecutive duplicate values. 2025-07-17T07:11:23.9037734Z 2025-07-17T07:11:23.9037965Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-07-17T07:11:23.9038613Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-07-17T07:11:23.9039373Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-07-17T07:11:23.9039972Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-07-17T07:11:23.9040268Z 2025-07-17T07:11:23.9040358Z Args: 2025-07-17T07:11:23.9040600Z input (Tensor): the input tensor 2025-07-17T07:11:23.9041053Z sorted (bool): Whether to sort the unique elements in ascending order 2025-07-17T07:11:23.9041491Z before returning as output. 2025-07-17T07:11:23.9041928Z return_inverse (bool): Whether to also return the indices for where 2025-07-17T07:11:23.9042487Z elements in the original input ended up in the returned unique list. 2025-07-17T07:11:23.9043060Z return_counts (bool): Whether to also return the counts for each unique 2025-07-17T07:11:23.9043485Z element. 2025-07-17T07:11:23.9043850Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-07-17T07:11:23.9044393Z unique of the flattened input is returned. Otherwise, each of the 2025-07-17T07:11:23.9044917Z tensors indexed by the given dimension is treated as one of the 2025-07-17T07:11:23.9045455Z elements to apply the unique operation upon. See examples for more 2025-07-17T07:11:23.9045903Z details. Default: ``None`` 2025-07-17T07:11:23.9046111Z 2025-07-17T07:11:23.9046209Z Returns: 2025-07-17T07:11:23.9046617Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-07-17T07:11:23.9047035Z 2025-07-17T07:11:23.9047227Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-07-17T07:11:23.9047686Z - **inverse_indices** (*Tensor*): (optional) if 2025-07-17T07:11:23.9048120Z :attr:`return_inverse` is True, there will be an additional 2025-07-17T07:11:23.9048630Z returned tensor (same shape as input) representing the indices 2025-07-17T07:11:23.9049165Z for where elements in the original input map to in the output; 2025-07-17T07:11:23.9049676Z otherwise, this function will only return a single tensor. 2025-07-17T07:11:23.9050095Z - **counts** (*Tensor*): (optional) if 2025-07-17T07:11:23.9050510Z :attr:`return_counts` is True, there will be an additional 2025-07-17T07:11:23.9050998Z returned tensor (same shape as output or output.size(dim), 2025-07-17T07:11:23.9051485Z if dim was specified) representing the number of occurrences 2025-07-17T07:11:23.9051911Z for each unique value or tensor. 2025-07-17T07:11:23.9052145Z 2025-07-17T07:11:23.9052235Z Example:: 2025-07-17T07:11:23.9052371Z 2025-07-17T07:11:23.9052595Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-07-17T07:11:23.9053009Z >>> output 2025-07-17T07:11:23.9053250Z tensor([1, 2, 3]) 2025-07-17T07:11:23.9053416Z 2025-07-17T07:11:23.9053561Z >>> output, inverse_indices = torch.unique( 2025-07-17T07:11:23.9054036Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-07-17T07:11:23.9054492Z >>> output 2025-07-17T07:11:23.9054727Z tensor([1, 2, 3]) 2025-07-17T07:11:23.9054990Z >>> inverse_indices 2025-07-17T07:11:23.9055330Z tensor([0, 2, 1, 2]) 2025-07-17T07:11:23.9055517Z 2025-07-17T07:11:23.9055644Z >>> output, inverse_indices = torch.unique( 2025-07-17T07:11:23.9056134Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-07-17T07:11:23.9056574Z >>> output 2025-07-17T07:11:23.9056814Z tensor([1, 2, 3]) 2025-07-17T07:11:23.9057161Z >>> inverse_indices 2025-07-17T07:11:23.9057419Z tensor([[0, 2], 2025-07-17T07:11:23.9057670Z [1, 2]]) 2025-07-17T07:11:23.9057829Z 2025-07-17T07:11:23.9057941Z >>> a = torch.tensor([ 2025-07-17T07:11:23.9058307Z ... [ 2025-07-17T07:11:23.9058548Z ... [1, 1, 0, 0], 2025-07-17T07:11:23.9058831Z ... [1, 1, 0, 0], 2025-07-17T07:11:23.9059112Z ... [0, 0, 1, 1], 2025-07-17T07:11:23.9059375Z ... ], 2025-07-17T07:11:23.9059604Z ... [ 2025-07-17T07:11:23.9059838Z ... [0, 0, 1, 1], 2025-07-17T07:11:23.9060103Z ... [0, 0, 1, 1], 2025-07-17T07:11:23.9060379Z ... [1, 1, 1, 1], 2025-07-17T07:11:23.9060647Z ... ], 2025-07-17T07:11:23.9060858Z ... [ 2025-07-17T07:11:23.9061086Z ... [1, 1, 0, 0], 2025-07-17T07:11:23.9061363Z ... [1, 1, 0, 0], 2025-07-17T07:11:23.9061631Z ... [0, 0, 1, 1], 2025-07-17T07:11:23.9061902Z ... ], 2025-07-17T07:11:23.9062133Z ... ]) 2025-07-17T07:11:23.9062263Z 2025-07-17T07:11:23.9062478Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-07-17T07:11:23.9063034Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-07-17T07:11:23.9063506Z >>> # each other, so one of them will be removed. 2025-07-17T07:11:23.9063862Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-07-17T07:11:23.9064156Z tensor(True) 2025-07-17T07:11:23.9064437Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-07-17T07:11:23.9064778Z >>> a_unique_dim0 2025-07-17T07:11:23.9065033Z tensor([[[0, 0, 1, 1], 2025-07-17T07:11:23.9065306Z [0, 0, 1, 1], 2025-07-17T07:11:23.9065576Z [1, 1, 1, 1]], 2025-07-17T07:11:23.9065832Z [[1, 1, 0, 0], 2025-07-17T07:11:23.9066098Z [1, 1, 0, 0], 2025-07-17T07:11:23.9066373Z [0, 0, 1, 1]]]) 2025-07-17T07:11:23.9066549Z 2025-07-17T07:11:23.9066776Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-07-17T07:11:23.9067199Z >>> # `a_unique_dim0`: 2025-07-17T07:11:23.9067511Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-07-17T07:11:23.9067841Z tensor(True) 2025-07-17T07:11:23.9068104Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-07-17T07:11:23.9068434Z tensor(True) 2025-07-17T07:11:23.9068579Z 2025-07-17T07:11:23.9068796Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-07-17T07:11:23.9069294Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-07-17T07:11:23.9069708Z >>> # them will be removed. 2025-07-17T07:11:23.9070010Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-07-17T07:11:23.9070332Z tensor(True) 2025-07-17T07:11:23.9070578Z >>> torch.unique(a, dim=1) 2025-07-17T07:11:23.9070882Z tensor([[[0, 0, 1, 1], 2025-07-17T07:11:23.9071153Z [1, 1, 0, 0]], 2025-07-17T07:11:23.9071410Z [[1, 1, 1, 1], 2025-07-17T07:11:23.9071672Z [0, 0, 1, 1]], 2025-07-17T07:11:23.9072113Z [[0, 0, 1, 1], 2025-07-17T07:11:23.9072385Z [1, 1, 0, 0]]]) 2025-07-17T07:11:23.9072579Z 2025-07-17T07:11:23.9072789Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-07-17T07:11:23.9073295Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-07-17T07:11:23.9073753Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-07-17T07:11:23.9074153Z >>> # sub-tensors will be removed. 2025-07-17T07:11:23.9074483Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-07-17T07:11:23.9074786Z tensor(True) 2025-07-17T07:11:23.9075027Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-07-17T07:11:23.9075468Z tensor(True) 2025-07-17T07:11:23.9075732Z >>> torch.unique(a, dim=2) 2025-07-17T07:11:23.9076019Z tensor([[[0, 1], 2025-07-17T07:11:23.9076277Z [0, 1], 2025-07-17T07:11:23.9076530Z [1, 0]], 2025-07-17T07:11:23.9076862Z [[1, 0], 2025-07-17T07:11:23.9077098Z [1, 0], 2025-07-17T07:11:23.9077348Z [1, 1]], 2025-07-17T07:11:23.9077601Z [[0, 1], 2025-07-17T07:11:23.9077837Z [0, 1], 2025-07-17T07:11:23.9078090Z [1, 0]]]) 2025-07-17T07:11:23.9078337Z 2025-07-17T07:11:23.9078707Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9079079Z 2025-07-17T07:11:23.9079516Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=560. 2025-07-17T07:11:23.9080316Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9080709Z 2025-07-17T07:11:23.9080869Z Load a model from a github repo or a local directory. 2025-07-17T07:11:23.9081136Z 2025-07-17T07:11:23.9081374Z Note: Loading a model is the typical use case, but this can also be used to 2025-07-17T07:11:23.9081942Z for loading other objects such as tokenizers, loss functions, etc. 2025-07-17T07:11:23.9082266Z 2025-07-17T07:11:23.9082447Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-07-17T07:11:23.9082888Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-07-17T07:11:23.9083273Z ref (a tag or a branch). 2025-07-17T07:11:23.9083440Z 2025-07-17T07:11:23.9083621Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-07-17T07:11:23.9084012Z path to a local directory. 2025-07-17T07:11:23.9084186Z 2025-07-17T07:11:23.9084268Z Args: 2025-07-17T07:11:23.9084522Z repo_or_dir (str): If ``source`` is 'github', 2025-07-17T07:11:23.9085062Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-07-17T07:11:23.9085786Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-07-17T07:11:23.9086469Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-07-17T07:11:23.9087071Z If ``source`` is 'local' then it should be a path to a local directory. 2025-07-17T07:11:23.9087593Z model (str): the name of a callable (entrypoint) defined in the 2025-07-17T07:11:23.9088019Z repo/dir's ``hubconf.py``. 2025-07-17T07:11:23.9088413Z *args (optional): the corresponding args for callable ``model``. 2025-07-17T07:11:23.9088914Z source (str, optional): 'github' or 'local'. Specifies how 2025-07-17T07:11:23.9089385Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-07-17T07:11:23.9089893Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-07-17T07:11:23.9090453Z This parameter was introduced in v1.12 and helps ensuring that users 2025-07-17T07:11:23.9090938Z only run code from repos that they trust. 2025-07-17T07:11:23.9101375Z 2025-07-17T07:11:23.9101641Z - If ``False``, a prompt will ask the user whether the repo should 2025-07-17T07:11:23.9102073Z be trusted. 2025-07-17T07:11:23.9102410Z - If ``True``, the repo will be added to the trusted list and loaded 2025-07-17T07:11:23.9102850Z without requiring explicit confirmation. 2025-07-17T07:11:23.9103260Z - If ``"check"``, the repo will be checked against the list of 2025-07-17T07:11:23.9103745Z trusted repos in the cache. If it is not present in that list, the 2025-07-17T07:11:23.9104261Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-07-17T07:11:23.9104754Z - If ``None``: this will raise a warning, inviting the user to set 2025-07-17T07:11:23.9105232Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-07-17T07:11:23.9105843Z is only present for backward compatibility and will be removed in 2025-07-17T07:11:23.9106248Z v2.0. 2025-07-17T07:11:23.9106377Z 2025-07-17T07:11:23.9106586Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-07-17T07:11:23.9107168Z force_reload (bool, optional): whether to force a fresh download of 2025-07-17T07:11:23.9107679Z the github repo unconditionally. Does not have any effect if 2025-07-17T07:11:23.9108097Z ``source = 'local'``. Default is ``False``. 2025-07-17T07:11:23.9108528Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-07-17T07:11:23.9109042Z local caches. Note that the message about first download cannot be 2025-07-17T07:11:23.9109524Z muted. Does not have any effect if ``source = 'local'``. 2025-07-17T07:11:23.9109899Z Default is ``True``. 2025-07-17T07:11:23.9110354Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-07-17T07:11:23.9111052Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-07-17T07:11:23.9111720Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-07-17T07:11:23.9112290Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-07-17T07:11:23.9112788Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-07-17T07:11:23.9113124Z 2025-07-17T07:11:23.9113203Z Returns: 2025-07-17T07:11:23.9113496Z The output of the ``model`` callable when called with the given 2025-07-17T07:11:23.9113883Z ``*args`` and ``**kwargs``. 2025-07-17T07:11:23.9114068Z 2025-07-17T07:11:23.9114147Z Example: 2025-07-17T07:11:23.9114390Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T07:11:23.9114728Z >>> # from a github repo 2025-07-17T07:11:23.9114993Z >>> repo = "pytorch/vision" 2025-07-17T07:11:23.9115269Z >>> model = torch.hub.load( 2025-07-17T07:11:23.9115641Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-07-17T07:11:23.9116002Z ... ) 2025-07-17T07:11:23.9116208Z >>> # from a local directory 2025-07-17T07:11:23.9116512Z >>> path = "/some/local/path/pytorch/vision" 2025-07-17T07:11:23.9116832Z >>> # xdoctest: +SKIP 2025-07-17T07:11:23.9117235Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-07-17T07:11:23.9117593Z 2025-07-17T07:11:23.9117844Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9118204Z 2025-07-17T07:11:23.9118651Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=652. 2025-07-17T07:11:23.9119461Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9119840Z 2025-07-17T07:11:23.9120006Z Load a model from a local directory with a ``hubconf.py``. 2025-07-17T07:11:23.9120282Z 2025-07-17T07:11:23.9120368Z Args: 2025-07-17T07:11:23.9120651Z hubconf_dir (str): path to a local directory that contains a 2025-07-17T07:11:23.9121031Z ``hubconf.py``. 2025-07-17T07:11:23.9121359Z model (str): name of an entrypoint defined in the directory's 2025-07-17T07:11:23.9121741Z ``hubconf.py``. 2025-07-17T07:11:23.9122078Z *args (optional): the corresponding args for callable ``model``. 2025-07-17T07:11:23.9122594Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-07-17T07:11:23.9122920Z 2025-07-17T07:11:23.9123009Z Returns: 2025-07-17T07:11:23.9123275Z a single model with corresponding pretrained weights. 2025-07-17T07:11:23.9123558Z 2025-07-17T07:11:23.9123637Z Example: 2025-07-17T07:11:23.9123865Z >>> # xdoctest: +SKIP("stub local path") 2025-07-17T07:11:23.9124206Z >>> path = "/some/local/path/pytorch/vision" 2025-07-17T07:11:23.9124526Z >>> model = _load_local( 2025-07-17T07:11:23.9124781Z ... path, 2025-07-17T07:11:23.9125062Z ... "resnet50", 2025-07-17T07:11:23.9125337Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-07-17T07:11:23.9125667Z ... ) 2025-07-17T07:11:23.9125779Z 2025-07-17T07:11:23.9126088Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9126456Z 2025-07-17T07:11:23.9126936Z 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. 2025-07-17T07:11:23.9127796Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9128320Z Download object at the given URL to a local path. 2025-07-17T07:11:23.9128574Z 2025-07-17T07:11:23.9128657Z Args: 2025-07-17T07:11:23.9128881Z url (str): URL of the object to download 2025-07-17T07:11:23.9129340Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-07-17T07:11:23.9130016Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-07-17T07:11:23.9130556Z Default: None 2025-07-17T07:11:23.9130965Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-07-17T07:11:23.9131426Z Default: True 2025-07-17T07:11:23.9131591Z 2025-07-17T07:11:23.9131679Z Example: 2025-07-17T07:11:23.9131924Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T07:11:23.9132271Z >>> # xdoctest: +REQUIRES(POSIX) 2025-07-17T07:11:23.9132597Z >>> torch.hub.download_url_to_file( 2025-07-17T07:11:23.9133033Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-07-17T07:11:23.9133465Z ... "/tmp/temporary_file", 2025-07-17T07:11:23.9133757Z ... ) 2025-07-17T07:11:23.9133879Z 2025-07-17T07:11:23.9133962Z 2025-07-17T07:11:23.9134307Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9134684Z 2025-07-17T07:11:23.9135264Z 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. 2025-07-17T07:11:23.9136147Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9136668Z Loads the Torch serialized object at the given URL. 2025-07-17T07:11:23.9136939Z 2025-07-17T07:11:23.9137117Z If downloaded file is a zip file, it will be automatically 2025-07-17T07:11:23.9137505Z decompressed. 2025-07-17T07:11:23.9137644Z 2025-07-17T07:11:23.9137866Z If the object is already present in `model_dir`, it's deserialized and 2025-07-17T07:11:23.9138268Z returned. 2025-07-17T07:11:23.9138603Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-07-17T07:11:23.9139126Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-07-17T07:11:23.9139432Z 2025-07-17T07:11:23.9139510Z Args: 2025-07-17T07:11:23.9139750Z url (str): URL of the object to download 2025-07-17T07:11:23.9140178Z model_dir (str, optional): directory in which to save the object 2025-07-17T07:11:23.9140846Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-07-17T07:11:23.9141562Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-07-17T07:11:23.9142030Z Default: True 2025-07-17T07:11:23.9142520Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-07-17T07:11:23.9143173Z ``filename-.ext`` where ```` is the first eight or more 2025-07-17T07:11:23.9143734Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-07-17T07:11:23.9144274Z ensure unique names and to verify the contents of the file. 2025-07-17T07:11:23.9144672Z Default: False 2025-07-17T07:11:23.9145165Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-07-17T07:11:23.9146002Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-07-17T07:11:23.9146771Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-07-17T07:11:23.9147154Z 2025-07-17T07:11:23.9147250Z Example: 2025-07-17T07:11:23.9147501Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T07:11:23.9147891Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-07-17T07:11:23.9148372Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-07-17T07:11:23.9148776Z ... ) 2025-07-17T07:11:23.9148909Z 2025-07-17T07:11:23.9148985Z 2025-07-17T07:11:23.9149346Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9149714Z 2025-07-17T07:11:23.9150218Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=374. 2025-07-17T07:11:23.9151073Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:23.9151678Z Registers the function implementation as the fallback for the given key. 2025-07-17T07:11:23.9152031Z 2025-07-17T07:11:23.9152251Z This function only works for a library with global namespace ("_"). 2025-07-17T07:11:23.9152584Z 2025-07-17T07:11:23.9152666Z Args: 2025-07-17T07:11:23.9153067Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-07-17T07:11:23.9153570Z to register a fallthrough. 2025-07-17T07:11:23.9154107Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-07-17T07:11:23.9154698Z the dispatch key that the library was created with. 2025-07-17T07:11:23.9155348Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-07-17T07:11:23.9156159Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-07-17T07:11:23.9156604Z 2025-07-17T07:11:23.9156710Z Example:: 2025-07-17T07:11:23.9156851Z 2025-07-17T07:11:23.9156955Z >>> my_lib = Library("_", "IMPL") 2025-07-17T07:11:23.9157302Z >>> def fallback_kernel(op, *args, **kwargs): 2025-07-17T07:11:23.9157666Z >>> # Handle all autocast ops generically 2025-07-17T07:11:23.9157979Z >>> # ... 2025-07-17T07:11:23.9158276Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-07-17T07:11:23.9158612Z 2025-07-17T07:11:23.9159366Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-07-17T07:11:23.9160100Z 2025-07-17T07:11:23.9160226Z my_lib.fallback(fallback_kernel, "Autocast") 2025-07-17T07:11:23.9160547Z ^ 2025-07-17T07:11:23.9161124Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=933. 2025-07-17T07:11:23.9161951Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:23.9162539Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-07-17T07:11:23.9162885Z 2025-07-17T07:11:23.9163059Z Also sometimes known as a "meta kernel", "abstract impl". 2025-07-17T07:11:23.9163343Z 2025-07-17T07:11:23.9163592Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-07-17T07:11:23.9164162Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-07-17T07:11:23.9164733Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-07-17T07:11:23.9165223Z what the properties of the output Tensors are. 2025-07-17T07:11:23.9165472Z 2025-07-17T07:11:23.9165772Z The FakeTensor implementation has the same signature as the operator. 2025-07-17T07:11:23.9166316Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-07-17T07:11:23.9166850Z implementation, assume that all Tensor inputs to the operator are 2025-07-17T07:11:23.9167436Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-07-17T07:11:23.9167947Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-07-17T07:11:23.9168499Z The FakeTensor implementation must consist of only PyTorch operations 2025-07-17T07:11:23.9169033Z (and may not directly access the storage or data of any input or 2025-07-17T07:11:23.9169436Z intermediate Tensors). 2025-07-17T07:11:23.9169609Z 2025-07-17T07:11:23.9169753Z This API may be used as a decorator (see examples). 2025-07-17T07:11:23.9170017Z 2025-07-17T07:11:23.9170153Z For a detailed guide on custom ops, please see 2025-07-17T07:11:23.9170629Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-07-17T07:11:23.9170976Z 2025-07-17T07:11:23.9171064Z Args: 2025-07-17T07:11:23.9171394Z op_name: Operator name (along with the overload) or OpOverload object. 2025-07-17T07:11:23.9171993Z func: Fake tensor implementation. 2025-07-17T07:11:23.9172413Z lib (Optional[Library]): Library to register the fake tensor to. 2025-07-17T07:11:23.9172895Z allow_override: Flag controlling if we want to override an 2025-07-17T07:11:23.9173365Z existing registered fake impl. This is by default off, 2025-07-17T07:11:23.9173828Z and will error you're trying to register a fake impl to 2025-07-17T07:11:23.9174277Z an operator that already has a fake impl. This also only 2025-07-17T07:11:23.9174725Z applies if the custom operator was not created via 2025-07-17T07:11:23.9175235Z torch.library.custom_op, as overriding and existing fake 2025-07-17T07:11:23.9175659Z impl is already allowed. 2025-07-17T07:11:23.9175876Z 2025-07-17T07:11:23.9175960Z Examples: 2025-07-17T07:11:23.9176177Z >>> import torch 2025-07-17T07:11:23.9176439Z >>> import numpy as np 2025-07-17T07:11:23.9176710Z >>> from torch import Tensor 2025-07-17T07:11:23.9176991Z >>> 2025-07-17T07:11:23.9177299Z >>> # Example 1: an operator without data-dependent output shape 2025-07-17T07:11:23.9177816Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-07-17T07:11:23.9178349Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-07-17T07:11:23.9178858Z >>> raise NotImplementedError("Implementation goes here") 2025-07-17T07:11:23.9179224Z >>> 2025-07-17T07:11:23.9179497Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-07-17T07:11:23.9179872Z >>> def _(x, weight, bias): 2025-07-17T07:11:23.9180170Z >>> assert x.dim() == 2 2025-07-17T07:11:23.9180463Z >>> assert weight.dim() == 2 2025-07-17T07:11:23.9180775Z >>> assert bias.dim() == 1 2025-07-17T07:11:23.9181100Z >>> assert x.shape[1] == weight.shape[1] 2025-07-17T07:11:23.9181462Z >>> assert weight.shape[0] == bias.shape[0] 2025-07-17T07:11:23.9181807Z >>> assert x.device == weight.device 2025-07-17T07:11:23.9182113Z >>> 2025-07-17T07:11:23.9182340Z >>> return (x @ weight.t()) + bias 2025-07-17T07:11:23.9182631Z >>> 2025-07-17T07:11:23.9182914Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-07-17T07:11:23.9183295Z >>> x = torch.randn(2, 3) 2025-07-17T07:11:23.9183585Z >>> w = torch.randn(3, 3) 2025-07-17T07:11:23.9183879Z >>> b = torch.randn(3) 2025-07-17T07:11:23.9184207Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-07-17T07:11:23.9184527Z >>> 2025-07-17T07:11:23.9184866Z >>> assert y.shape == (2, 3) 2025-07-17T07:11:23.9185144Z >>> 2025-07-17T07:11:23.9185422Z >>> # Example 2: an operator with data-dependent output shape 2025-07-17T07:11:23.9185994Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-07-17T07:11:23.9186453Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-07-17T07:11:23.9186802Z >>> x_np = x.numpy(force=True) 2025-07-17T07:11:23.9187132Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-07-17T07:11:23.9187502Z >>> return torch.tensor(res, device=x.device) 2025-07-17T07:11:23.9187822Z >>> 2025-07-17T07:11:23.9188102Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-07-17T07:11:23.9188465Z >>> def _(x): 2025-07-17T07:11:23.9188758Z >>> # Number of nonzero-elements is data-dependent. 2025-07-17T07:11:23.9189172Z >>> # Since we cannot peek at the data in an fake impl, 2025-07-17T07:11:23.9189586Z >>> # we use the ctx object to construct a new symint that 2025-07-17T07:11:23.9189981Z >>> # represents the data-dependent size. 2025-07-17T07:11:23.9190328Z >>> ctx = torch.library.get_ctx() 2025-07-17T07:11:23.9190654Z >>> nnz = ctx.new_dynamic_size() 2025-07-17T07:11:23.9190969Z >>> shape = [nnz, x.dim()] 2025-07-17T07:11:23.9191317Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-07-17T07:11:23.9191658Z >>> return result 2025-07-17T07:11:23.9191904Z >>> 2025-07-17T07:11:23.9192193Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-07-17T07:11:23.9192546Z >>> 2025-07-17T07:11:23.9192764Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-07-17T07:11:23.9193216Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-07-17T07:11:23.9193681Z >>> trace.print_readable() 2025-07-17T07:11:23.9193948Z >>> 2025-07-17T07:11:23.9194298Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-07-17T07:11:23.9194640Z 2025-07-17T07:11:23.9194731Z 2025-07-17T07:11:23.9195368Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-07-17T07:11:23.9196024Z 2025-07-17T07:11:23.9196108Z _._ = None 2025-07-17T07:11:23.9196313Z ^ 2025-07-17T07:11:23.9196934Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1068. 2025-07-17T07:11:23.9197804Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9198332Z Register a backward formula for this custom op. 2025-07-17T07:11:23.9198584Z 2025-07-17T07:11:23.9198801Z In order for an operator to work with autograd, you need to register 2025-07-17T07:11:23.9199212Z a backward formula: 2025-07-17T07:11:23.9199589Z 1. You must tell us how to compute gradients during the backward pass 2025-07-17T07:11:23.9200038Z by providing us a "backward" function. 2025-07-17T07:11:23.9200480Z 2. If you need any values from the forward to compute gradients, you can 2025-07-17T07:11:23.9200951Z use `setup_context` to save values for backward. 2025-07-17T07:11:23.9201217Z 2025-07-17T07:11:23.9201444Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-07-17T07:11:23.9201988Z - ``grads`` is one or more gradients. The number of gradients matches 2025-07-17T07:11:23.9202416Z the number of outputs of the operator. 2025-07-17T07:11:23.9202860Z The ``ctx`` object is `the same ctx object `_ used by 2025-07-17T07:11:23.9203443Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-07-17T07:11:23.9203966Z same as :meth:`torch.autograd.Function.backward`. 2025-07-17T07:11:23.9204233Z 2025-07-17T07:11:23.9204443Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-07-17T07:11:23.9205072Z Please save quantities needed for backward onto the ``ctx`` object via 2025-07-17T07:11:23.9205661Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-07-17T07:11:23.9206265Z or assigning them as attributes of ``ctx``. If your custom op has 2025-07-17T07:11:23.9206788Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-07-17T07:11:23.9207318Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-07-17T07:11:23.9207628Z 2025-07-17T07:11:23.9207855Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-07-17T07:11:23.9208408Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-07-17T07:11:23.9208992Z not depend on or mutate global state. If you need a non-traceable backward, 2025-07-17T07:11:23.9209567Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-07-17T07:11:23.9209907Z 2025-07-17T07:11:23.9210140Z If you need different autograd behavior on different devices, then we 2025-07-17T07:11:23.9210698Z recommend creating two different custom operators, one for each device 2025-07-17T07:11:23.9211269Z that needs different behavior, and switching between them at runtime. 2025-07-17T07:11:23.9211605Z 2025-07-17T07:11:23.9211694Z Examples: 2025-07-17T07:11:23.9211903Z >>> import torch 2025-07-17T07:11:23.9212160Z >>> import numpy as np 2025-07-17T07:11:23.9212439Z >>> from torch import Tensor 2025-07-17T07:11:23.9212706Z >>> 2025-07-17T07:11:23.9213025Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-07-17T07:11:23.9213451Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-07-17T07:11:23.9213765Z >>> x_np = x.cpu().numpy() 2025-07-17T07:11:23.9214060Z >>> y_np = np.sin(x_np) 2025-07-17T07:11:23.9214400Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-07-17T07:11:23.9214740Z >>> 2025-07-17T07:11:23.9215003Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-07-17T07:11:23.9215411Z >>> x, = inputs 2025-07-17T07:11:23.9215675Z >>> ctx.save_for_backward(x) 2025-07-17T07:11:23.9215952Z >>> 2025-07-17T07:11:23.9216170Z >>> def backward(ctx, grad): 2025-07-17T07:11:23.9216468Z >>> x, = ctx.saved_tensors 2025-07-17T07:11:23.9216757Z >>> return grad * x.cos() 2025-07-17T07:11:23.9217023Z >>> 2025-07-17T07:11:23.9217253Z >>> torch.library.register_autograd( 2025-07-17T07:11:23.9217642Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-07-17T07:11:23.9218010Z ... ) 2025-07-17T07:11:23.9218206Z >>> 2025-07-17T07:11:23.9218423Z >>> x = torch.randn(3, requires_grad=True) 2025-07-17T07:11:23.9218746Z >>> y = numpy_sin(x) 2025-07-17T07:11:23.9219083Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-07-17T07:11:23.9219486Z >>> assert torch.allclose(grad_x, x.cos()) 2025-07-17T07:11:23.9219785Z >>> 2025-07-17T07:11:23.9220010Z >>> # Example with a keyword-only arg 2025-07-17T07:11:23.9220430Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-07-17T07:11:23.9220885Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-07-17T07:11:23.9221243Z >>> x_np = x.cpu().numpy() 2025-07-17T07:11:23.9221537Z >>> y_np = x_np * val 2025-07-17T07:11:23.9221866Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-07-17T07:11:23.9222206Z >>> 2025-07-17T07:11:23.9222540Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-07-17T07:11:23.9223007Z >>> ctx.val = keyword_only_inputs["val"] 2025-07-17T07:11:23.9223313Z >>> 2025-07-17T07:11:23.9223531Z >>> def backward(ctx, grad): 2025-07-17T07:11:23.9223823Z >>> return grad * ctx.val 2025-07-17T07:11:23.9224150Z >>> 2025-07-17T07:11:23.9224384Z >>> torch.library.register_autograd( 2025-07-17T07:11:23.9224906Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-07-17T07:11:23.9225268Z ... ) 2025-07-17T07:11:23.9225534Z >>> 2025-07-17T07:11:23.9225769Z >>> x = torch.randn(3, requires_grad=True) 2025-07-17T07:11:23.9226091Z >>> y = numpy_mul(x, val=3.14) 2025-07-17T07:11:23.9226455Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-07-17T07:11:23.9226910Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-07-17T07:11:23.9227194Z 2025-07-17T07:11:23.9227275Z 2025-07-17T07:11:23.9227620Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9227992Z 2025-07-17T07:11:23.9228448Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1482. 2025-07-17T07:11:23.9229277Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9229853Z Given an operator and some sample arguments, tests if the operator is 2025-07-17T07:11:23.9230279Z registered correctly. 2025-07-17T07:11:23.9230459Z 2025-07-17T07:11:23.9230671Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-07-17T07:11:23.9231255Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-07-17T07:11:23.9231837Z and these APIs require that the functions you pass them satisfy certain 2025-07-17T07:11:23.9232421Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-07-17T07:11:23.9232922Z ``opcheck`` tests these metadata and properties. 2025-07-17T07:11:23.9233176Z 2025-07-17T07:11:23.9233304Z Concretely, we test the following: 2025-07-17T07:11:23.9233519Z 2025-07-17T07:11:23.9233696Z - test_schema: If the schema matches the implementation of 2025-07-17T07:11:23.9234221Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-07-17T07:11:23.9234779Z then we check the implementation mutates the Tensor. If the schema 2025-07-17T07:11:23.9235287Z specifies that we return a new Tensor, then we check that the 2025-07-17T07:11:23.9235823Z implementation returns a new Tensor (instead of an existing one or 2025-07-17T07:11:23.9236271Z a view of an existing one). 2025-07-17T07:11:23.9236674Z - test_autograd_registration: If the operator supports training 2025-07-17T07:11:23.9237178Z (autograd): we check that its autograd formula is registered via 2025-07-17T07:11:23.9237705Z torch.library.register_autograd or a manual registration to one 2025-07-17T07:11:23.9238250Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-07-17T07:11:23.9238696Z registrations may lead to undefined behavior. 2025-07-17T07:11:23.9239132Z - test_faketensor: If the operator has a FakeTensor kernel 2025-07-17T07:11:23.9239604Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-07-17T07:11:23.9240121Z but not sufficient) for the operator to work with PyTorch compilation 2025-07-17T07:11:23.9240664Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-07-17T07:11:23.9241196Z (also sometimes known as a meta kernel) was registered for the 2025-07-17T07:11:23.9241702Z operator and that it is correct. This test takes the result of 2025-07-17T07:11:23.9242195Z running the operator on real tensors and the result of running 2025-07-17T07:11:23.9242702Z the operator on FakeTensors and checks that they have the same 2025-07-17T07:11:23.9243168Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-07-17T07:11:23.9243640Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-07-17T07:11:23.9244125Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-07-17T07:11:23.9244631Z This checks that the outputs (and gradients, if applicable) are the 2025-07-17T07:11:23.9245191Z same under eager-mode PyTorch and torch.compile. 2025-07-17T07:11:23.9245637Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-07-17T07:11:23.9246164Z other things it tests are that the operator supports 2025-07-17T07:11:23.9246671Z functionalization and that the backward pass (if it exists) also 2025-07-17T07:11:23.9247128Z supports FakeTensor and functionalization. 2025-07-17T07:11:23.9247386Z 2025-07-17T07:11:23.9247582Z For best results, please call ``opcheck`` multiple times with a 2025-07-17T07:11:23.9248076Z representative set of inputs. If your operator supports 2025-07-17T07:11:23.9248608Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-07-17T07:11:23.9249176Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-07-17T07:11:23.9249678Z use ``opcheck`` with inputs on all supported devices. 2025-07-17T07:11:23.9249950Z 2025-07-17T07:11:23.9250047Z Args: 2025-07-17T07:11:23.9250347Z op: The operator. Must either be a function decorated with 2025-07-17T07:11:23.9250847Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-07-17T07:11:23.9251401Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-07-17T07:11:23.9251849Z args: The args to the operator 2025-07-17T07:11:23.9252168Z kwargs: The kwargs to the operator 2025-07-17T07:11:23.9252576Z test_utils: Tests that we should run. Default: all of them. 2025-07-17T07:11:23.9253019Z Example: ("test_schema", "test_faketensor") 2025-07-17T07:11:23.9253451Z raise_exception: If we should raise an exception on the first 2025-07-17T07:11:23.9253932Z error. If False, we will return a dict with information 2025-07-17T07:11:23.9254335Z on if each test passed or not. 2025-07-17T07:11:23.9254799Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-07-17T07:11:23.9255367Z If specified ``atol`` must also be specified. 2025-07-17T07:11:23.9255812Z If omitted, default values based on the ``dtype`` are selected 2025-07-17T07:11:23.9256302Z (see the table in :func:`torch.testing.assert_close`). 2025-07-17T07:11:23.9256811Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-07-17T07:11:23.9257326Z If specified ``rtol`` must also be specified. 2025-07-17T07:11:23.9257775Z If omitted, default values based on the ``dtype`` are selected 2025-07-17T07:11:23.9258261Z (see the table in :func:`torch.testing.assert_close`). 2025-07-17T07:11:23.9258541Z 2025-07-17T07:11:23.9258635Z .. warning:: 2025-07-17T07:11:23.9258788Z 2025-07-17T07:11:23.9259006Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-07-17T07:11:23.9259560Z opcheck tests if your usage of torch.library APIs is correct while 2025-07-17T07:11:23.9260114Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-07-17T07:11:23.9260661Z mathematically correct. Use both to test custom ops that support 2025-07-17T07:11:23.9261118Z gradient computation. 2025-07-17T07:11:23.9261309Z 2025-07-17T07:11:23.9261411Z Example: 2025-07-17T07:11:23.9261540Z 2025-07-17T07:11:23.9261678Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:23.9262140Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-07-17T07:11:23.9262610Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-07-17T07:11:23.9262981Z >>> x_np = x.numpy(force=True) 2025-07-17T07:11:23.9263289Z >>> z_np = x_np * y 2025-07-17T07:11:23.9263617Z >>> return torch.from_numpy(z_np).to(x.device) 2025-07-17T07:11:23.9263959Z >>> 2025-07-17T07:11:23.9264184Z >>> @numpy_mul.register_fake 2025-07-17T07:11:23.9264488Z >>> def _(x, y): 2025-07-17T07:11:23.9264830Z >>> return torch.empty_like(x) 2025-07-17T07:11:23.9265127Z >>> 2025-07-17T07:11:23.9265382Z >>> def setup_context(ctx, inputs, output): 2025-07-17T07:11:23.9265722Z >>> y, = inputs 2025-07-17T07:11:23.9266023Z >>> ctx.y = y 2025-07-17T07:11:23.9266277Z >>> 2025-07-17T07:11:23.9266506Z >>> def backward(ctx, grad): 2025-07-17T07:11:23.9266806Z >>> return grad * ctx.y, None 2025-07-17T07:11:23.9267106Z >>> 2025-07-17T07:11:23.9267446Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-07-17T07:11:23.9267874Z >>> 2025-07-17T07:11:23.9268085Z >>> sample_inputs = [ 2025-07-17T07:11:23.9268377Z >>> (torch.randn(3), 3.14), 2025-07-17T07:11:23.9268717Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-07-17T07:11:23.9269098Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-07-17T07:11:23.9269560Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-07-17T07:11:23.9269958Z >>> ] 2025-07-17T07:11:23.9270159Z >>> 2025-07-17T07:11:23.9270393Z >>> for args in sample_inputs: 2025-07-17T07:11:23.9270747Z >>> torch.library.opcheck(numpy_mul, args) 2025-07-17T07:11:23.9270992Z 2025-07-17T07:11:23.9271072Z 2025-07-17T07:11:23.9271439Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9271948Z 2025-07-17T07:11:23.9274774Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1285. 2025-07-17T07:11:23.9275651Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9276621Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-07-17T07:11:23.9277377Z 2025-07-17T07:11:23.9277722Z Loads an object saved with :func:`torch.save` from a file. 2025-07-17T07:11:23.9278106Z 2025-07-17T07:11:23.9278341Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-07-17T07:11:23.9278930Z which underlie tensors, specially. They are first deserialized on the 2025-07-17T07:11:23.9279504Z CPU and are then moved to the device they were saved from. If this fails 2025-07-17T07:11:23.9280064Z (e.g. because the run time system doesn't have certain devices), an exception 2025-07-17T07:11:23.9280659Z is raised. However, storages can be dynamically remapped to an alternative 2025-07-17T07:11:23.9281190Z set of devices using the :attr:`map_location` argument. 2025-07-17T07:11:23.9281469Z 2025-07-17T07:11:23.9281710Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-07-17T07:11:23.9282305Z storage with two arguments: storage and location. The storage argument 2025-07-17T07:11:23.9282879Z will be the initial deserialization of the storage, residing on the CPU. 2025-07-17T07:11:23.9283452Z Each serialized storage has a location tag associated with it which 2025-07-17T07:11:23.9283989Z identifies the device it was saved from, and this tag is the second 2025-07-17T07:11:23.9284581Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-07-17T07:11:23.9285185Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-07-17T07:11:23.9285740Z :attr:`map_location` should return either ``None`` or a storage. If 2025-07-17T07:11:23.9286307Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-07-17T07:11:23.9286935Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-07-17T07:11:23.9287552Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-07-17T07:11:23.9287917Z 2025-07-17T07:11:23.9288149Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-07-17T07:11:23.9288877Z a device tag, it indicates the location where all tensors should be loaded. 2025-07-17T07:11:23.9289240Z 2025-07-17T07:11:23.9289497Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-07-17T07:11:23.9290162Z appearing in the file (keys), to ones that specify where to put the 2025-07-17T07:11:23.9290577Z storages (values). 2025-07-17T07:11:23.9290745Z 2025-07-17T07:11:23.9290963Z User extensions can register their own location tags and tagging and 2025-07-17T07:11:23.9291573Z deserialization methods using :func:`torch.serialization.register_package`. 2025-07-17T07:11:23.9291953Z 2025-07-17T07:11:23.9292208Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-07-17T07:11:23.9292570Z 2025-07-17T07:11:23.9292653Z Args: 2025-07-17T07:11:23.9293101Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-07-17T07:11:23.9293725Z or a string or os.PathLike object containing a file name 2025-07-17T07:11:23.9294331Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-07-17T07:11:23.9294874Z locations 2025-07-17T07:11:23.9295356Z pickle_module: module used for unpickling metadata and objects (has to 2025-07-17T07:11:23.9295884Z match the :attr:`pickle_module` used to serialize file) 2025-07-17T07:11:23.9296375Z weights_only: Indicates whether unpickler should be restricted to 2025-07-17T07:11:23.9296875Z loading only tensors, primitive types, dictionaries 2025-07-17T07:11:23.9297381Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-07-17T07:11:23.9297852Z See :ref:`weights-only` for more details. 2025-07-17T07:11:23.9298440Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-07-17T07:11:23.9299241Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-07-17T07:11:23.9300051Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-07-17T07:11:23.9300840Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-07-17T07:11:23.9301624Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-07-17T07:11:23.9302232Z will be lazily loaded when their data is accessed. 2025-07-17T07:11:23.9302747Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-07-17T07:11:23.9303329Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-07-17T07:11:23.9303777Z :attr:`errors=...`. 2025-07-17T07:11:23.9303958Z 2025-07-17T07:11:23.9304071Z .. warning:: 2025-07-17T07:11:23.9304424Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-07-17T07:11:23.9304993Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-07-17T07:11:23.9305600Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-07-17T07:11:23.9306249Z during unpickling. Never load data that could have come from an untrusted 2025-07-17T07:11:23.9306916Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-07-17T07:11:23.9307331Z 2025-07-17T07:11:23.9307419Z .. note:: 2025-07-17T07:11:23.9307820Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-07-17T07:11:23.9308471Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-07-17T07:11:23.9309118Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-07-17T07:11:23.9309600Z 2025-07-17T07:11:23.9309689Z .. note:: 2025-07-17T07:11:23.9310077Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-07-17T07:11:23.9310670Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-07-17T07:11:23.9311264Z when loading files saved by Python 2 in Python 3. If this default 2025-07-17T07:11:23.9311862Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-07-17T07:11:23.9312489Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-07-17T07:11:23.9313097Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-07-17T07:11:23.9313681Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-07-17T07:11:23.9314044Z 2025-07-17T07:11:23.9314129Z Example: 2025-07-17T07:11:23.9314397Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-07-17T07:11:23.9314774Z >>> torch.load("tensors.pt", weights_only=True) 2025-07-17T07:11:23.9315144Z # Load all tensors onto the CPU 2025-07-17T07:11:23.9315456Z >>> torch.load( 2025-07-17T07:11:23.9315703Z ... "tensors.pt", 2025-07-17T07:11:23.9316015Z ... map_location=torch.device("cpu"), 2025-07-17T07:11:23.9316358Z ... weights_only=True, 2025-07-17T07:11:23.9316627Z ... ) 2025-07-17T07:11:23.9316903Z # Load all tensors onto the CPU, using a function 2025-07-17T07:11:23.9317254Z >>> torch.load( 2025-07-17T07:11:23.9317510Z ... "tensors.pt", 2025-07-17T07:11:23.9317816Z ... map_location=lambda storage, loc: storage, 2025-07-17T07:11:23.9318172Z ... weights_only=True, 2025-07-17T07:11:23.9318457Z ... ) 2025-07-17T07:11:23.9318684Z # Load all tensors onto GPU 1 2025-07-17T07:11:23.9318990Z >>> torch.load( 2025-07-17T07:11:23.9319251Z ... "tensors.pt", 2025-07-17T07:11:23.9319581Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-07-17T07:11:23.9319973Z ... weights_only=True, 2025-07-17T07:11:23.9320286Z ... ) # type: ignore[attr-defined] 2025-07-17T07:11:23.9320613Z # Map tensors from GPU 1 to GPU 0 2025-07-17T07:11:23.9320932Z >>> torch.load( 2025-07-17T07:11:23.9321186Z ... "tensors.pt", 2025-07-17T07:11:23.9321486Z ... map_location={"cuda:1": "cuda:0"}, 2025-07-17T07:11:23.9321812Z ... weights_only=True, 2025-07-17T07:11:23.9322094Z ... ) 2025-07-17T07:11:23.9322342Z # Load tensor from io.BytesIO object 2025-07-17T07:11:23.9322811Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-07-17T07:11:23.9323308Z >>> with open("tensor.pt", "rb") as f: 2025-07-17T07:11:23.9323653Z ... buffer = io.BytesIO(f.read()) 2025-07-17T07:11:23.9323991Z >>> torch.load(buffer, weights_only=False) 2025-07-17T07:11:23.9324388Z # Load a module with 'ascii' encoding for unpickling 2025-07-17T07:11:23.9324908Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-07-17T07:11:23.9325479Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-07-17T07:11:23.9325873Z 2025-07-17T07:11:23.9326242Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9326610Z 2025-07-17T07:11:23.9784728Z msg = Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py line=1848. 2025-07-17T07:11:23.9785762Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:23.9786361Z Computes the minimum storage size to hold the given tensor geometry. 2025-07-17T07:11:23.9786710Z 2025-07-17T07:11:23.9786797Z Example 2025-07-17T07:11:23.9787011Z ======= 2025-07-17T07:11:23.9787134Z 2025-07-17T07:11:23.9787378Z This is the size of a newly allocated tensor's storage, in units of elements 2025-07-17T07:11:23.9787878Z 2025-07-17T07:11:23.9787982Z >>> t = torch.empty((10, 20)) 2025-07-17T07:11:23.9788429Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-07-17T07:11:23.9788953Z 200 2025-07-17T07:11:23.9789070Z 2025-07-17T07:11:23.9789172Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:23.9789508Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-07-17T07:11:23.9789888Z >>> size = compute_required_storage_length( 2025-07-17T07:11:23.9790312Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-07-17T07:11:23.9790637Z ... ) 2025-07-17T07:11:23.9790862Z >>> size == t.storage().size() 2025-07-17T07:11:23.9791143Z True 2025-07-17T07:11:23.9791256Z 2025-07-17T07:11:23.9791452Z A valid tensor may have a larger storage size, but never smaller 2025-07-17T07:11:23.9791775Z 2025-07-17T07:11:23.9791882Z >>> slice = torch.empty(100)[20:40] 2025-07-17T07:11:23.9792207Z >>> slice.storage().size() 2025-07-17T07:11:23.9792464Z 100 2025-07-17T07:11:23.9792589Z 2025-07-17T07:11:23.9792699Z >>> compute_required_storage_length( 2025-07-17T07:11:23.9793089Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-07-17T07:11:23.9793459Z ... ) 2025-07-17T07:11:23.9793653Z 40 2025-07-17T07:11:23.9793777Z 2025-07-17T07:11:23.9793855Z 2025-07-17T07:11:23.9794218Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:23.9794587Z 2025-07-17T07:11:24.0309080Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=66. 2025-07-17T07:11:24.0309990Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:24.0310621Z Check if the current accelerator is available at runtime: it was build, all the 2025-07-17T07:11:24.0311215Z required drivers are available and at least one device is visible. 2025-07-17T07:11:24.0311699Z See :ref:`accelerator` for details. 2025-07-17T07:11:24.0311972Z 2025-07-17T07:11:24.0312057Z Returns: 2025-07-17T07:11:24.0312470Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-07-17T07:11:24.0312867Z 2025-07-17T07:11:24.0313145Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-07-17T07:11:24.0313764Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-07-17T07:11:24.0314402Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-07-17T07:11:24.0314930Z :ref:`multiprocessing-poison-fork-note`. 2025-07-17T07:11:24.0315182Z 2025-07-17T07:11:24.0315283Z Example:: 2025-07-17T07:11:24.0315414Z 2025-07-17T07:11:24.0315682Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T07:11:24.0316170Z 2025-07-17T07:11:24.0316864Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-07-17T07:11:24.0317546Z 2025-07-17T07:11:24.0317812Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T07:11:24.0318300Z ^ 2025-07-17T07:11:24.0325651Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=212. 2025-07-17T07:11:24.0326563Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:24.0327130Z Wait for all kernels in all streams on the given device to complete. 2025-07-17T07:11:24.0327468Z 2025-07-17T07:11:24.0327554Z Args: 2025-07-17T07:11:24.0328003Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-07-17T07:11:24.0328687Z the current :ref:`accelerator` device type. If not given, 2025-07-17T07:11:24.0329369Z use :func:`torch.accelerator.current_device_index` by default. 2025-07-17T07:11:24.0329712Z 2025-07-17T07:11:24.0330100Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-07-17T07:11:24.0330554Z 2025-07-17T07:11:24.0330646Z Example:: 2025-07-17T07:11:24.0330777Z 2025-07-17T07:11:24.0330933Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:24.0331446Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T07:11:24.0331989Z >>> start_event = torch.Event(enable_timing=True) 2025-07-17T07:11:24.0332386Z >>> end_event = torch.Event(enable_timing=True) 2025-07-17T07:11:24.0332751Z >>> start_event.record() 2025-07-17T07:11:24.0333176Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-07-17T07:11:24.0333651Z >>> sum = torch.sum(tensor) 2025-07-17T07:11:24.0333967Z >>> end_event.record() 2025-07-17T07:11:24.0334267Z >>> torch.accelerator.synchronize() 2025-07-17T07:11:24.0334672Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-07-17T07:11:24.0335040Z 2025-07-17T07:11:24.0335761Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-07-17T07:11:24.0336447Z 2025-07-17T07:11:24.0336705Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T07:11:24.0337183Z ^ 2025-07-17T07:11:24.0561802Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=434. 2025-07-17T07:11:24.0562683Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:24.0563163Z Retrieves the CUDA runtime API module. 2025-07-17T07:11:24.0563402Z 2025-07-17T07:11:24.0563407Z 2025-07-17T07:11:24.0563658Z This function initializes the CUDA runtime environment if it is not already 2025-07-17T07:11:24.0564265Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-07-17T07:11:24.0564850Z runtime API module provides access to various CUDA runtime functions. 2025-07-17T07:11:24.0565196Z 2025-07-17T07:11:24.0565279Z Args: 2025-07-17T07:11:24.0565493Z ``None`` 2025-07-17T07:11:24.0565632Z 2025-07-17T07:11:24.0565728Z Returns: 2025-07-17T07:11:24.0565994Z module: The CUDA runtime API module (_cudart). 2025-07-17T07:11:24.0566258Z 2025-07-17T07:11:24.0566341Z Raises: 2025-07-17T07:11:24.0566699Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-07-17T07:11:24.0567420Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-07-17T07:11:24.0567902Z 2025-07-17T07:11:24.0568039Z Example of CUDA operations with profiling: 2025-07-17T07:11:24.0568382Z >>> import torch 2025-07-17T07:11:24.0568684Z >>> from torch.cuda import cudart, check_error 2025-07-17T07:11:24.0569013Z >>> import os 2025-07-17T07:11:24.0569253Z >>> 2025-07-17T07:11:24.0569496Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-07-17T07:11:24.0569795Z >>> 2025-07-17T07:11:24.0570060Z >>> def perform_cuda_operations_with_streams(): 2025-07-17T07:11:24.0570432Z >>> stream = torch.cuda.Stream() 2025-07-17T07:11:24.0570780Z >>> with torch.cuda.stream(stream): 2025-07-17T07:11:24.0571133Z >>> x = torch.randn(100, 100, device='cuda') 2025-07-17T07:11:24.0571503Z >>> y = torch.randn(100, 100, device='cuda') 2025-07-17T07:11:24.0571974Z >>> z = torch.mul(x, y) 2025-07-17T07:11:24.0572270Z >>> return z 2025-07-17T07:11:24.0572522Z >>> 2025-07-17T07:11:24.0572757Z >>> torch.cuda.synchronize() 2025-07-17T07:11:24.0573242Z >>> print("====== Start nsys profiling ======") 2025-07-17T07:11:24.0573630Z >>> check_error(cudart().cudaProfilerStart()) 2025-07-17T07:11:24.0574031Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-07-17T07:11:24.0574501Z >>> result = perform_cuda_operations_with_streams() 2025-07-17T07:11:24.0574902Z >>> print("CUDA operations completed.") 2025-07-17T07:11:24.0575402Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-07-17T07:11:24.0575822Z >>> print("====== End nsys profiling ======") 2025-07-17T07:11:24.0576064Z 2025-07-17T07:11:24.0576268Z To run this example and save the profiling information, execute: 2025-07-17T07:11:24.0576970Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-07-17T07:11:24.0577452Z 2025-07-17T07:11:24.0577709Z This command profiles the CUDA operations in the provided script and saves 2025-07-17T07:11:24.0578285Z the profiling information to a file named `trace_name.prof`. 2025-07-17T07:11:24.0578826Z The `--profile-from-start off` option ensures that profiling starts only 2025-07-17T07:11:24.0579339Z after the `cudaProfilerStart` call in the script. 2025-07-17T07:11:24.0579840Z The `--csv` and `--print-summary` options format the profiling output as a 2025-07-17T07:11:24.0580315Z CSV file and print a summary, respectively. 2025-07-17T07:11:24.0580818Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-07-17T07:11:24.0581345Z overwrite of the output file if it already exists. 2025-07-17T07:11:24.0581699Z 2025-07-17T07:11:24.0582473Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2025-07-17T07:11:24.0583282Z 2025-07-17T07:11:24.0583635Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-07-17T07:11:24.0584197Z ^ 2025-07-17T07:11:24.0692576Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2025-07-17T07:11:24.0693703Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:24.0694093Z 2025-07-17T07:11:24.0694327Z Append the given callback function to this ``Future``, which will be run 2025-07-17T07:11:24.0694894Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-07-17T07:11:24.0695526Z the same ``Future``, but the order in which they will be executed cannot 2025-07-17T07:11:24.0696037Z be guaranteed (to enforce a certain order consider chaining: 2025-07-17T07:11:24.0696548Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-07-17T07:11:24.0697084Z is the reference to this ``Future``. The callback function can use the 2025-07-17T07:11:24.0697626Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-07-17T07:11:24.0698170Z already completed, the given callback will be run immediately inline. 2025-07-17T07:11:24.0698525Z 2025-07-17T07:11:24.0698725Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-07-17T07:11:24.0699260Z callback might be invoked while the async kernels that are populating 2025-07-17T07:11:24.0699836Z those tensors haven't yet finished executing on the device. However, the 2025-07-17T07:11:24.0700384Z callback will be invoked with some dedicated streams set as current 2025-07-17T07:11:24.0700919Z (fetched from a global pool) which will be synchronized with those 2025-07-17T07:11:24.0701469Z kernels. Hence any operation performed by the callback on these tensors 2025-07-17T07:11:24.0702020Z will be scheduled on the device after the kernels complete. In other 2025-07-17T07:11:24.0702552Z words, as long as the callback doesn't switch streams, it can safely 2025-07-17T07:11:24.0703257Z manipulate the result without any additional synchronization. This is 2025-07-17T07:11:24.0703912Z similar to the non-blocking behavior of :meth:`wait`. 2025-07-17T07:11:24.0704182Z 2025-07-17T07:11:24.0704395Z Similarly, if the callback returns a value that contains tensors that 2025-07-17T07:11:24.0704988Z reside on a GPU, it can do so even if the kernels that are producing 2025-07-17T07:11:24.0705525Z these tensors are still running on the device, as long as the callback 2025-07-17T07:11:24.0706054Z didn't change streams during its execution. If one wants to change 2025-07-17T07:11:24.0706589Z streams, one must be careful to re-synchronize them with the original 2025-07-17T07:11:24.0707138Z streams, that is, those that were current when the callback was invoked. 2025-07-17T07:11:24.0707469Z 2025-07-17T07:11:24.0707564Z Args: 2025-07-17T07:11:24.0707874Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-07-17T07:11:24.0708308Z the only argument. 2025-07-17T07:11:24.0708530Z 2025-07-17T07:11:24.0708627Z Returns: 2025-07-17T07:11:24.0708903Z A new ``Future`` object that holds the return value of the 2025-07-17T07:11:24.0709367Z ``callback`` and will be marked as completed when the given 2025-07-17T07:11:24.0709771Z ``callback`` finishes. 2025-07-17T07:11:24.0709944Z 2025-07-17T07:11:24.0710140Z .. note:: Note that if the callback function throws, either 2025-07-17T07:11:24.0710625Z through the original future being completed with an exception and 2025-07-17T07:11:24.0711151Z calling ``fut.wait()``, or through other code in the callback, the 2025-07-17T07:11:24.0711676Z future returned by ``then`` will be marked appropriately with the 2025-07-17T07:11:24.0712189Z encountered error. However, if this callback later completes 2025-07-17T07:11:24.0712720Z additional futures, those futures are not marked as completed with 2025-07-17T07:11:24.0713264Z an error and the user is responsible for handling completion/waiting 2025-07-17T07:11:24.0713709Z on those futures independently. 2025-07-17T07:11:24.0713917Z 2025-07-17T07:11:24.0714007Z Example:: 2025-07-17T07:11:24.0714141Z 2025-07-17T07:11:24.0714287Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-07-17T07:11:24.0714647Z >>> def callback(fut): 2025-07-17T07:11:24.0714953Z ... print(f"RPC return value is {fut.wait()}.") 2025-07-17T07:11:24.0715320Z >>> fut = torch.futures.Future() 2025-07-17T07:11:24.0715705Z >>> # The inserted callback will print the return value when 2025-07-17T07:11:24.0716103Z >>> # receiving the response from "worker1" 2025-07-17T07:11:24.0716449Z >>> cb_fut = fut.then(callback) 2025-07-17T07:11:24.0716759Z >>> chain_cb_fut = cb_fut.then( 2025-07-17T07:11:24.0717113Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-07-17T07:11:24.0717451Z ... ) 2025-07-17T07:11:24.0717668Z >>> fut.set_result(5) 2025-07-17T07:11:24.0717938Z RPC return value is 5. 2025-07-17T07:11:24.0718197Z Chained cb done. None 2025-07-17T07:11:24.0718381Z 2025-07-17T07:11:24.0718633Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:24.0719002Z 2025-07-17T07:11:24.0719579Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=211. 2025-07-17T07:11:24.0720497Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:24.0720877Z 2025-07-17T07:11:24.0721082Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-07-17T07:11:24.0721628Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-07-17T07:11:24.0722082Z cannot be marked completed twice. 2025-07-17T07:11:24.0722283Z 2025-07-17T07:11:24.0722515Z If the result contains tensors that reside on GPUs, this method can be 2025-07-17T07:11:24.0723046Z called even if the asynchronous kernels that are populating those 2025-07-17T07:11:24.0723590Z tensors haven't yet completed running on the device, provided that the 2025-07-17T07:11:24.0724226Z streams on which those kernels were enqueued are set as the current ones 2025-07-17T07:11:24.0724771Z when this method is called. Put simply, it's safe to call this method 2025-07-17T07:11:24.0725381Z immediately after launching those kernels, without any additional 2025-07-17T07:11:24.0725950Z synchronization, as long as one doesn't change streams in between. This 2025-07-17T07:11:24.0726519Z method will record events on all the relevant current streams and will 2025-07-17T07:11:24.0727047Z use them to ensure proper scheduling for all the consumers of this 2025-07-17T07:11:24.0727452Z ``Future``. 2025-07-17T07:11:24.0727575Z 2025-07-17T07:11:24.0727673Z Args: 2025-07-17T07:11:24.0727942Z result (object): the result object of this ``Future``. 2025-07-17T07:11:24.0728227Z 2025-07-17T07:11:24.0728317Z Example:: 2025-07-17T07:11:24.0728447Z 2025-07-17T07:11:24.0728591Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-07-17T07:11:24.0728952Z >>> import threading 2025-07-17T07:11:24.0729198Z >>> import time 2025-07-17T07:11:24.0729454Z >>> def slow_set_future(fut, value): 2025-07-17T07:11:24.0729771Z ... time.sleep(0.5) 2025-07-17T07:11:24.0730043Z ... fut.set_result(value) 2025-07-17T07:11:24.0730357Z >>> fut = torch.futures.Future() 2025-07-17T07:11:24.0730671Z >>> t = threading.Thread( 2025-07-17T07:11:24.0730947Z ... target=slow_set_future, 2025-07-17T07:11:24.0731259Z ... args=(fut, torch.ones(2) * 3) 2025-07-17T07:11:24.0731562Z ... ) 2025-07-17T07:11:24.0731754Z >>> t.start() 2025-07-17T07:11:24.0731994Z >>> print(fut.wait()) 2025-07-17T07:11:24.0732254Z tensor([3., 3.]) 2025-07-17T07:11:24.0732480Z >>> t.join() 2025-07-17T07:11:24.0732628Z 2025-07-17T07:11:24.0732879Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:24.0733260Z 2025-07-17T07:11:24.0797369Z msg = Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=145. 2025-07-17T07:11:24.0798293Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:24.0798916Z Compiles compute shader from source and allows one to invoke kernels 2025-07-17T07:11:24.0799394Z defined there from the comfort of Python runtime 2025-07-17T07:11:24.0799756Z Example:: 2025-07-17T07:11:24.0799887Z 2025-07-17T07:11:24.0800033Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-07-17T07:11:24.0800408Z >>> lib = torch.mps.compile_shader( 2025-07-17T07:11:24.0801043Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-07-17T07:11:24.0801625Z ... ) 2025-07-17T07:11:24.0801885Z >>> x = torch.zeros(16, device="mps") 2025-07-17T07:11:24.0802217Z >>> lib.full(x, 3.14) 2025-07-17T07:11:24.0802473Z 2025-07-17T07:11:24.0802851Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:24.0803220Z 2025-07-17T07:11:24.0971160Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2025-07-17T07:11:24.0972162Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:24.0972701Z Return the sum of each row of the given sparse tensor. 2025-07-17T07:11:24.0972989Z 2025-07-17T07:11:24.0973217Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-07-17T07:11:24.0973778Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-07-17T07:11:24.0974309Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-07-17T07:11:24.0974785Z returns a dense tensor instead of a sparse tensor. 2025-07-17T07:11:24.0975063Z 2025-07-17T07:11:24.0975361Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-07-17T07:11:24.0976109Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-07-17T07:11:24.0976420Z 2025-07-17T07:11:24.0976656Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-07-17T07:11:24.0977299Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-07-17T07:11:24.0977674Z 2025-07-17T07:11:24.0977762Z Args: 2025-07-17T07:11:24.0978012Z input (Tensor): the input sparse tensor 2025-07-17T07:11:24.0978519Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-07-17T07:11:24.0979022Z over all dims. 2025-07-17T07:11:24.0979457Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-07-17T07:11:24.0979974Z Default: dtype of :attr:`input`. 2025-07-17T07:11:24.0980196Z 2025-07-17T07:11:24.0980293Z Example:: 2025-07-17T07:11:24.0980431Z 2025-07-17T07:11:24.0980515Z >>> nnz = 3 2025-07-17T07:11:24.0980756Z >>> dims = [5, 5, 2, 3] 2025-07-17T07:11:24.0981098Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-07-17T07:11:24.0981550Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-07-17T07:11:24.0981990Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-07-17T07:11:24.0982332Z >>> size = torch.Size(dims) 2025-07-17T07:11:24.0982667Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:24.0983050Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-07-17T07:11:24.0983380Z >>> S 2025-07-17T07:11:24.0983611Z tensor(indices=tensor([[2, 0, 3], 2025-07-17T07:11:24.0983931Z [2, 4, 1]]), 2025-07-17T07:11:24.0984279Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-07-17T07:11:24.0984643Z [ 0.3411, 0.0918, -0.2312]], 2025-07-17T07:11:24.0984867Z 2025-07-17T07:11:24.0984975Z [[ 0.5348, 0.0634, -2.0494], 2025-07-17T07:11:24.0985316Z [-0.7125, -1.0646, 2.1844]], 2025-07-17T07:11:24.0985540Z 2025-07-17T07:11:24.0985659Z [[ 0.1276, 0.1874, -0.6334], 2025-07-17T07:11:24.0985987Z [-1.9682, -0.5340, 0.7483]]]), 2025-07-17T07:11:24.0986363Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-07-17T07:11:24.0986630Z 2025-07-17T07:11:24.0986823Z # when sum over only part of sparse_dims, return a sparse tensor 2025-07-17T07:11:24.0987249Z >>> torch.sparse.sum(S, [1, 3]) 2025-07-17T07:11:24.0987569Z tensor(indices=tensor([[0, 2, 3]]), 2025-07-17T07:11:24.0987904Z values=tensor([[-1.4512, 0.4073], 2025-07-17T07:11:24.0988237Z [-0.8901, 0.2017], 2025-07-17T07:11:24.0988546Z [-0.3183, -1.7539]]), 2025-07-17T07:11:24.0988903Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-07-17T07:11:24.0989156Z 2025-07-17T07:11:24.0989320Z # when sum over all sparse dim, return a dense tensor 2025-07-17T07:11:24.0989699Z # with summed dims squeezed 2025-07-17T07:11:24.0990005Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-07-17T07:11:24.0990334Z tensor([-2.6596, -1.1450]) 2025-07-17T07:11:24.0990614Z 2025-07-17T07:11:24.0990968Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:24.0991355Z 2025-07-17T07:11:24.8737404Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39. 2025-07-17T07:11:24.8738291Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:24.8738686Z 2025-07-17T07:11:24.8738906Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-07-17T07:11:24.8739492Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-07-17T07:11:24.8740284Z pushes the map into PyTorch operations called by ``func``, effectively 2025-07-17T07:11:24.8740740Z vectorizing those operations. 2025-07-17T07:11:24.8740937Z 2025-07-17T07:11:24.8741150Z vmap is useful for handling batch dimensions: one can write a function 2025-07-17T07:11:24.8741788Z ``func`` that runs on examples and then lift it to a function that can 2025-07-17T07:11:24.8742329Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-07-17T07:11:24.8742831Z compute batched gradients when composed with autograd. 2025-07-17T07:11:24.8743110Z 2025-07-17T07:11:24.8743225Z .. note:: 2025-07-17T07:11:24.8743537Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-07-17T07:11:24.8743972Z convenience. Use whichever one you'd like. 2025-07-17T07:11:24.8744210Z 2025-07-17T07:11:24.8744290Z Args: 2025-07-17T07:11:24.8744618Z func (function): A Python function that takes one or more arguments. 2025-07-17T07:11:24.8745057Z Must return one or more Tensors. 2025-07-17T07:11:24.8745487Z in_dims (int or nested structure): Specifies which dimension of the 2025-07-17T07:11:24.8745975Z inputs should be mapped over. ``in_dims`` should have a 2025-07-17T07:11:24.8746468Z structure like the inputs. If the ``in_dim`` for a particular 2025-07-17T07:11:24.8746968Z input is None, then that indicates there is no map dimension. 2025-07-17T07:11:24.8747419Z Default: 0. 2025-07-17T07:11:24.8747765Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-07-17T07:11:24.8748386Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-07-17T07:11:24.8748854Z it should have one element per output. Default: 0. 2025-07-17T07:11:24.8749297Z randomness (str): Specifies whether the randomness in this 2025-07-17T07:11:24.8749810Z vmap should be the same or different across batches. If 'different', 2025-07-17T07:11:24.8750349Z the randomness for each batch will be different. If 'same', the 2025-07-17T07:11:24.8750882Z randomness will be the same across batches. If 'error', any calls to 2025-07-17T07:11:24.8751433Z random functions will error. Default: 'error'. WARNING: this flag 2025-07-17T07:11:24.8751974Z only applies to random PyTorch operations and does not apply to 2025-07-17T07:11:24.8752436Z Python's random module or numpy randomness. 2025-07-17T07:11:24.8752905Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-07-17T07:11:24.8753479Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-07-17T07:11:24.8754085Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-07-17T07:11:24.8754733Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-07-17T07:11:24.8755135Z 2025-07-17T07:11:24.8755219Z Returns: 2025-07-17T07:11:24.8755533Z Returns a new "batched" function. It takes the same inputs as 2025-07-17T07:11:24.8766573Z ``func``, except each input has an extra dimension at the index 2025-07-17T07:11:24.8767101Z specified by ``in_dims``. It takes returns the same outputs as 2025-07-17T07:11:24.8767628Z ``func``, except each output has an extra dimension at the index 2025-07-17T07:11:24.8768032Z specified by ``out_dims``. 2025-07-17T07:11:24.8768236Z 2025-07-17T07:11:24.8768323Z .. warning: 2025-07-17T07:11:24.8768660Z :func:`vmap` works best with functional-style code. Please do not 2025-07-17T07:11:24.8769160Z perform any side-effects in ``func``, with the exception of 2025-07-17T07:11:24.8769709Z in-place PyTorch operations. Examples of side-effects include mutating 2025-07-17T07:11:24.8770304Z Python data structures and assigning values to variables not captured 2025-07-17T07:11:24.8770744Z in ``func``. 2025-07-17T07:11:24.8770884Z 2025-07-17T07:11:24.8771120Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-07-17T07:11:24.8772020Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-07-17T07:11:24.8772588Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-07-17T07:11:24.8772926Z 2025-07-17T07:11:24.8773049Z >>> torch.dot # [D], [D] -> [] 2025-07-17T07:11:24.8773589Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-07-17T07:11:24.8774048Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-07-17T07:11:24.8774397Z >>> batched_dot(x, y) 2025-07-17T07:11:24.8774565Z 2025-07-17T07:11:24.8774795Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-07-17T07:11:24.8775356Z model authoring experience. 2025-07-17T07:11:24.8775539Z 2025-07-17T07:11:24.8775663Z >>> batch_size, feature_size = 3, 5 2025-07-17T07:11:24.8776047Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-07-17T07:11:24.8776430Z >>> 2025-07-17T07:11:24.8776655Z >>> def model(feature_vec): 2025-07-17T07:11:24.8776972Z >>> # Very simple linear model with activation 2025-07-17T07:11:24.8777343Z >>> return feature_vec.dot(weights).relu() 2025-07-17T07:11:24.8777670Z >>> 2025-07-17T07:11:24.8777940Z >>> examples = torch.randn(batch_size, feature_size) 2025-07-17T07:11:24.8778319Z >>> result = torch.vmap(model)(examples) 2025-07-17T07:11:24.8778558Z 2025-07-17T07:11:24.8778807Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-07-17T07:11:24.8779421Z or impossible to batch. One example is higher-order gradient computation. 2025-07-17T07:11:24.8779995Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-07-17T07:11:24.8780570Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-07-17T07:11:24.8781168Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-07-17T07:11:24.8781775Z we can vectorize the whole computation, computing the Jacobian in a single 2025-07-17T07:11:24.8782226Z call to ``autograd.grad``. 2025-07-17T07:11:24.8782417Z 2025-07-17T07:11:24.8782503Z >>> # Setup 2025-07-17T07:11:24.8782729Z >>> N = 5 2025-07-17T07:11:24.8782946Z >>> f = lambda x: x**2 2025-07-17T07:11:24.8783244Z >>> x = torch.randn(N, requires_grad=True) 2025-07-17T07:11:24.8783573Z >>> y = f(x) 2025-07-17T07:11:24.8783797Z >>> I_N = torch.eye(N) 2025-07-17T07:11:24.8784056Z >>> 2025-07-17T07:11:24.8784282Z >>> # Sequential approach 2025-07-17T07:11:24.8784679Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-07-17T07:11:24.8785133Z >>> for v in I_N.unbind()] 2025-07-17T07:11:24.8785482Z >>> jacobian = torch.stack(jacobian_rows) 2025-07-17T07:11:24.8785801Z >>> 2025-07-17T07:11:24.8786022Z >>> # vectorized gradient computation 2025-07-17T07:11:24.8786344Z >>> def get_vjp(v): 2025-07-17T07:11:24.8786632Z >>> return torch.autograd.grad(y, x, v) 2025-07-17T07:11:24.8786975Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-07-17T07:11:24.8787214Z 2025-07-17T07:11:24.8787479Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-07-17T07:11:24.8787868Z 2025-07-17T07:11:24.8787985Z >>> torch.dot # [D], [D] -> [] 2025-07-17T07:11:24.8788288Z >>> batched_dot = torch.vmap( 2025-07-17T07:11:24.8788597Z ... torch.vmap(torch.dot) 2025-07-17T07:11:24.8788910Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-07-17T07:11:24.8789278Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-07-17T07:11:24.8789645Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-07-17T07:11:24.8789895Z 2025-07-17T07:11:24.8790135Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-07-17T07:11:24.8790657Z the dimension that each inputs are batched along as 2025-07-17T07:11:24.8790918Z 2025-07-17T07:11:24.8791021Z >>> torch.dot # [N], [N] -> [] 2025-07-17T07:11:24.8791447Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-07-17T07:11:24.8792056Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-07-17T07:11:24.8792396Z >>> batched_dot( 2025-07-17T07:11:24.8792627Z ... x, y 2025-07-17T07:11:24.8793029Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-07-17T07:11:24.8793341Z 2025-07-17T07:11:24.8793613Z If there are multiple inputs each of which is batched along different dimensions, 2025-07-17T07:11:24.8794185Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-07-17T07:11:24.8794514Z 2025-07-17T07:11:24.8794618Z >>> torch.dot # [D], [D] -> [] 2025-07-17T07:11:24.8795059Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-07-17T07:11:24.8795551Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-07-17T07:11:24.8795864Z >>> batched_dot( 2025-07-17T07:11:24.8796107Z ... x, y 2025-07-17T07:11:24.8796441Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-07-17T07:11:24.8796755Z 2025-07-17T07:11:24.8796988Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-07-17T07:11:24.8797464Z matching the shape of the input: 2025-07-17T07:11:24.8797664Z 2025-07-17T07:11:24.8797811Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-07-17T07:11:24.8798189Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-07-17T07:11:24.8798510Z >>> input = {"x": x, "y": y} 2025-07-17T07:11:24.8798880Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-07-17T07:11:24.8799273Z >>> batched_dot(input) 2025-07-17T07:11:24.8799443Z 2025-07-17T07:11:24.8799715Z By default, the output is batched along the first dimension. However, it can be batched 2025-07-17T07:11:24.8800243Z along any dimension by using ``out_dims`` 2025-07-17T07:11:24.8800485Z 2025-07-17T07:11:24.8800580Z >>> f = lambda x: x**2 2025-07-17T07:11:24.8800851Z >>> x = torch.randn(2, 5) 2025-07-17T07:11:24.8801151Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-07-17T07:11:24.8801498Z >>> batched_pow(x) # [5, 2] 2025-07-17T07:11:24.8801684Z 2025-07-17T07:11:24.8801989Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-07-17T07:11:24.8802487Z accept kwargs 2025-07-17T07:11:24.8802629Z 2025-07-17T07:11:24.8802726Z >>> x = torch.randn([2, 5]) 2025-07-17T07:11:24.8803013Z >>> def fn(x, scale=4.): 2025-07-17T07:11:24.8803274Z >>> return x * scale 2025-07-17T07:11:24.8803529Z >>> 2025-07-17T07:11:24.8803758Z >>> batched_pow = torch.vmap(fn) 2025-07-17T07:11:24.8804109Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-07-17T07:11:24.8804576Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-07-17T07:11:24.8804932Z 2025-07-17T07:11:24.8805034Z .. note:: 2025-07-17T07:11:24.8805384Z vmap does not provide general autobatching or handle variable-length 2025-07-17T07:11:24.8805818Z sequences out of the box. 2025-07-17T07:11:24.8806020Z 2025-07-17T07:11:24.8806271Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:24.8806653Z 2025-07-17T07:11:24.8807182Z msg = Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=306. 2025-07-17T07:11:24.8808046Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:24.8808669Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-07-17T07:11:24.8809218Z input(s) specified by ``argnums``. This operator can be nested to 2025-07-17T07:11:24.8809652Z compute higher-order gradients. 2025-07-17T07:11:24.8809865Z 2025-07-17T07:11:24.8809961Z Args: 2025-07-17T07:11:24.8810285Z func (Callable): A Python function that takes one or more arguments. 2025-07-17T07:11:24.8810880Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-07-17T07:11:24.8811610Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-07-17T07:11:24.8812109Z ``(output, aux)``. 2025-07-17T07:11:24.8812563Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-07-17T07:11:24.8813225Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-07-17T07:11:24.8813776Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-07-17T07:11:24.8814283Z auxiliary objects: ``(output, aux)``. Default: False. 2025-07-17T07:11:24.8814561Z 2025-07-17T07:11:24.8814648Z Returns: 2025-07-17T07:11:24.8815061Z Function to compute gradients with respect to its inputs. By default, the output of 2025-07-17T07:11:24.8815798Z the function is the gradient tensor(s) with respect to the first argument. 2025-07-17T07:11:24.8816419Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-07-17T07:11:24.8817073Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-07-17T07:11:24.8817598Z respect to each ``argnums`` value is returned. 2025-07-17T07:11:24.8817851Z 2025-07-17T07:11:24.8817973Z Example of using ``grad``: 2025-07-17T07:11:24.8818157Z 2025-07-17T07:11:24.8818256Z >>> # xdoctest: +SKIP 2025-07-17T07:11:24.8818551Z >>> from torch.func import grad 2025-07-17T07:11:24.8818869Z >>> x = torch.randn([]) 2025-07-17T07:11:24.8819165Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-07-17T07:11:24.8819530Z >>> assert torch.allclose(cos_x, x.cos()) 2025-07-17T07:11:24.8819850Z >>> 2025-07-17T07:11:24.8820070Z >>> # Second-order gradients 2025-07-17T07:11:24.8820426Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-07-17T07:11:24.8820837Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-07-17T07:11:24.8821080Z 2025-07-17T07:11:24.8821343Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-07-17T07:11:24.8821717Z 2025-07-17T07:11:24.8821815Z >>> # xdoctest: +SKIP 2025-07-17T07:11:24.8822121Z >>> from torch.func import grad, vmap 2025-07-17T07:11:24.8822472Z >>> batch_size, feature_size = 3, 5 2025-07-17T07:11:24.8822766Z >>> 2025-07-17T07:11:24.8823005Z >>> def model(weights, feature_vec): 2025-07-17T07:11:24.8823365Z >>> # Very simple linear model with activation 2025-07-17T07:11:24.8823725Z >>> assert feature_vec.dim() == 1 2025-07-17T07:11:24.8824069Z >>> return feature_vec.dot(weights).relu() 2025-07-17T07:11:24.8824396Z >>> 2025-07-17T07:11:24.8824658Z >>> def compute_loss(weights, example, target): 2025-07-17T07:11:24.8825009Z >>> y = model(weights, example) 2025-07-17T07:11:24.8825367Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-07-17T07:11:24.8825701Z >>> 2025-07-17T07:11:24.8825996Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-07-17T07:11:24.8826444Z >>> examples = torch.randn(batch_size, feature_size) 2025-07-17T07:11:24.8826834Z >>> targets = torch.randn(batch_size) 2025-07-17T07:11:24.8827176Z >>> inputs = (weights, examples, targets) 2025-07-17T07:11:24.8827649Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-07-17T07:11:24.8828097Z ... *inputs 2025-07-17T07:11:24.8828339Z ... ) 2025-07-17T07:11:24.8828463Z 2025-07-17T07:11:24.8828639Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-07-17T07:11:24.8828942Z 2025-07-17T07:11:24.8829038Z >>> # xdoctest: +SKIP 2025-07-17T07:11:24.8829336Z >>> from torch.func import grad 2025-07-17T07:11:24.8829651Z >>> def my_loss_func(y, y_pred): 2025-07-17T07:11:24.8830002Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-07-17T07:11:24.8830365Z >>> loss = loss_per_sample.mean() 2025-07-17T07:11:24.8830819Z >>> return loss, (y_pred, loss_per_sample) 2025-07-17T07:11:24.8831245Z >>> 2025-07-17T07:11:24.8831525Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-07-17T07:11:24.8831979Z >>> y_true = torch.rand(4) 2025-07-17T07:11:24.8832301Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-07-17T07:11:24.8832662Z >>> out = fn(y_true, y_preds) 2025-07-17T07:11:24.8833121Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-07-17T07:11:24.8833486Z 2025-07-17T07:11:24.8833583Z .. note:: 2025-07-17T07:11:24.8833887Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-07-17T07:11:24.8834181Z 2025-07-17T07:11:24.8834326Z Case 1: Using ``torch.no_grad`` inside a function: 2025-07-17T07:11:24.8834581Z 2025-07-17T07:11:24.8834695Z >>> # xdoctest: +SKIP 2025-07-17T07:11:24.8834968Z >>> def f(x): 2025-07-17T07:11:24.8835243Z >>> with torch.no_grad(): 2025-07-17T07:11:24.8835554Z >>> c = x ** 2 2025-07-17T07:11:24.8835833Z >>> return x - c 2025-07-17T07:11:24.8836030Z 2025-07-17T07:11:24.8836230Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-07-17T07:11:24.8836556Z 2025-07-17T07:11:24.8836738Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-07-17T07:11:24.8837036Z 2025-07-17T07:11:24.8837144Z >>> # xdoctest: +SKIP 2025-07-17T07:11:24.8837434Z >>> with torch.no_grad(): 2025-07-17T07:11:24.8837737Z >>> grad(f)(x) 2025-07-17T07:11:24.8837911Z 2025-07-17T07:11:24.8838143Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-07-17T07:11:24.8838700Z outer one. This is because ``grad`` is a "function transform": its result 2025-07-17T07:11:24.8839262Z should not depend on the result of a context manager outside of ``f``. 2025-07-17T07:11:24.8839614Z 2025-07-17T07:11:24.8839694Z 2025-07-17T07:11:24.8840064Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:24.8840429Z 2025-07-17T07:11:26.4096952Z msg = Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py line=396. 2025-07-17T07:11:26.4097970Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:26.4098527Z Register a FakeTensor implementation for this custom op. 2025-07-17T07:11:26.4098821Z 2025-07-17T07:11:26.4099070Z This is necessary to get the operator to work efficiently with torch.compile. 2025-07-17T07:11:26.4099457Z 2025-07-17T07:11:26.4099679Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-07-17T07:11:26.4100256Z specifies the behavior of this operator on Tensors that carry no data. 2025-07-17T07:11:26.4100779Z Given some input Tensors with certain properties 2025-07-17T07:11:26.4101299Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-07-17T07:11:26.4101796Z the output Tensors are. 2025-07-17T07:11:26.4101985Z 2025-07-17T07:11:26.4102211Z Please see :func:`torch.library.impl_abstract` for more details. 2025-07-17T07:11:26.4102531Z 2025-07-17T07:11:26.4102630Z Args: 2025-07-17T07:11:26.4102929Z fn (Callable): The function to register as the FakeTensor 2025-07-17T07:11:26.4103327Z implementation. 2025-07-17T07:11:26.4103511Z 2025-07-17T07:11:26.4103613Z Examples: 2025-07-17T07:11:26.4103842Z >>> import torch 2025-07-17T07:11:26.4104128Z >>> import numpy as np 2025-07-17T07:11:26.4104440Z >>> from torch import Tensor 2025-07-17T07:11:26.4104733Z >>> 2025-07-17T07:11:26.4105064Z >>> # Example 1: an operator without data-dependent output shape 2025-07-17T07:11:26.4105831Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-07-17T07:11:26.4106347Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-07-17T07:11:26.4106771Z >>> return (x @ weight.t()) + bias 2025-07-17T07:11:26.4107187Z >>> 2025-07-17T07:11:26.4107439Z >>> @linear.register_fake 2025-07-17T07:11:26.4107744Z >>> def _(x, weight, bias): 2025-07-17T07:11:26.4108061Z >>> assert x.dim() == 2 2025-07-17T07:11:26.4108386Z >>> assert weight.dim() == 2 2025-07-17T07:11:26.4108705Z >>> assert bias.dim() == 1 2025-07-17T07:11:26.4109054Z >>> assert x.shape[1] == weight.shape[1] 2025-07-17T07:11:26.4109430Z >>> assert weight.shape[0] == bias.shape[0] 2025-07-17T07:11:26.4109793Z >>> assert x.device == weight.device 2025-07-17T07:11:26.4110182Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-07-17T07:11:26.4110538Z >>> 2025-07-17T07:11:26.4110776Z >>> x = torch.randn(2, 2) 2025-07-17T07:11:26.4111081Z >>> weight = torch.randn(2, 2) 2025-07-17T07:11:26.4111405Z >>> bias = torch.randn(2) 2025-07-17T07:11:26.4111754Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T07:11:26.4112193Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-07-17T07:11:26.4112644Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T07:11:26.4113146Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-07-17T07:11:26.4113593Z >>> 2025-07-17T07:11:26.4113909Z >>> # Example 2: an operator with data-dependent output shape 2025-07-17T07:11:26.4114414Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-07-17T07:11:26.4114858Z >>> def nonzero(x: Tensor) -> Tensor: 2025-07-17T07:11:26.4115192Z >>> x_np = x.cpu().numpy() 2025-07-17T07:11:26.4115551Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-07-17T07:11:26.4115946Z >>> return torch.tensor(res, device=x.device) 2025-07-17T07:11:26.4116273Z >>> 2025-07-17T07:11:26.4116525Z >>> @nonzero.register_fake 2025-07-17T07:11:26.4116833Z >>> def _(x): 2025-07-17T07:11:26.4117139Z >>> # Number of nonzero-elements is data-dependent. 2025-07-17T07:11:26.4117580Z >>> # Since we cannot peek at the data in an abstract impl, 2025-07-17T07:11:26.4118030Z >>> # we use the ctx object to construct a new symint that 2025-07-17T07:11:26.4118439Z >>> # represents the data-dependent size. 2025-07-17T07:11:26.4118795Z >>> ctx = torch.library.get_ctx() 2025-07-17T07:11:26.4119156Z >>> nnz = ctx.new_dynamic_size() 2025-07-17T07:11:26.4119493Z >>> shape = [nnz, x.dim()] 2025-07-17T07:11:26.4119850Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-07-17T07:11:26.4120223Z >>> return result 2025-07-17T07:11:26.4120499Z >>> 2025-07-17T07:11:26.4120751Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-07-17T07:11:26.4121118Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T07:11:26.4121525Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-07-17T07:11:26.4121936Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T07:11:26.4122317Z >>> assert torch.allclose(out, x.nonzero()) 2025-07-17T07:11:26.4122573Z 2025-07-17T07:11:26.4122657Z 2025-07-17T07:11:26.4123336Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-07-17T07:11:26.4123980Z 2025-07-17T07:11:26.4124075Z _._ = None 2025-07-17T07:11:26.4124270Z ^ 2025-07-17T07:11:26.4216698Z msg = Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_profile.py line=94. 2025-07-17T07:11:26.4217864Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.4218250Z 2025-07-17T07:11:26.4218582Z Registers a fake kernel based on the given operator profiles. This fake 2025-07-17T07:11:26.4219173Z kernel registration will override any existing fake kernel registrations. 2025-07-17T07:11:26.4219550Z 2025-07-17T07:11:26.4219764Z The input is a dictionary mapping operator names to a set of operator 2025-07-17T07:11:26.4220332Z profiles, which we will use to generate fake kernels. The operator profiles 2025-07-17T07:11:26.4220879Z are a record of the input and output tensor metadata. Based on this 2025-07-17T07:11:26.4221446Z information we will match a given input to the recorded profile, and return 2025-07-17T07:11:26.4222037Z an output with the same metadata as in the recorded profile. If a profile 2025-07-17T07:11:26.4222533Z doesn't exist then an exception will be thrown. 2025-07-17T07:11:26.4222781Z 2025-07-17T07:11:26.4223012Z The fake kernel generation is considered unsafe because it relies on the 2025-07-17T07:11:26.4223598Z rigid, pre-defined operator profiles that do not account for potential 2025-07-17T07:11:26.4224217Z variations in output behavior. Specifically, the generated kernels assume a 2025-07-17T07:11:26.4224847Z fixed relationship between input and output ranks. However, in reality, it's 2025-07-17T07:11:26.4225455Z possible that data-dependent operations may produce outputs of different 2025-07-17T07:11:26.4226046Z ranks even when given inputs of the same rank. The generated fake kernels 2025-07-17T07:11:26.4226605Z are inflexible and unable to accommodate these nuances, making them 2025-07-17T07:11:26.4227039Z potentially unsafe. 2025-07-17T07:11:26.4227191Z 2025-07-17T07:11:26.4227275Z Args: 2025-07-17T07:11:26.4227611Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-07-17T07:11:26.4228159Z name to a set of operator profiles from which we will generate fake 2025-07-17T07:11:26.4228558Z kernels. 2025-07-17T07:11:26.4228707Z 2025-07-17T07:11:26.4228793Z Examples: 2025-07-17T07:11:26.4228913Z 2025-07-17T07:11:26.4229096Z >>> # Example: Registering an op-profile from draft-export 2025-07-17T07:11:26.4229460Z >>> import torch 2025-07-17T07:11:26.4229777Z >>> from torch.export._draft_export import draft_export 2025-07-17T07:11:26.4230142Z >>> 2025-07-17T07:11:26.4230448Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-07-17T07:11:26.4230863Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-07-17T07:11:26.4231196Z >>> return x + y 2025-07-17T07:11:26.4231444Z >>> 2025-07-17T07:11:26.4231660Z >>> class M(torch.nn.Module): 2025-07-17T07:11:26.4231965Z >>> def forward(self, a, b): 2025-07-17T07:11:26.4232318Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-07-17T07:11:26.4232668Z >>> return res 2025-07-17T07:11:26.4232920Z >>> 2025-07-17T07:11:26.4233207Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-07-17T07:11:26.4233570Z >>> 2025-07-17T07:11:26.4233990Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-07-17T07:11:26.4234539Z >>> decomp = ep.run_decompositions() 2025-07-17T07:11:26.4234763Z 2025-07-17T07:11:26.4234767Z 2025-07-17T07:11:26.4235034Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.4235403Z 2025-07-17T07:11:26.4269899Z 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. 2025-07-17T07:11:26.4270877Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.4271523Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-07-17T07:11:26.4272128Z 2025-07-17T07:11:26.4272997Z This is a more structured way of using triton kernels with PyTorch. 2025-07-17T07:11:26.4273574Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-07-17T07:11:26.4274300Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-07-17T07:11:26.4274781Z that is simpler; 2025-07-17T07:11:26.4275197Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-07-17T07:11:26.4275800Z want to create an operator that behaves like PyTorch built-in operators. 2025-07-17T07:11:26.4276371Z For example, you may use a ``torch.library`` wrapper API to define the 2025-07-17T07:11:26.4276924Z behavior of the triton kernel when passed a tensor subclass or under 2025-07-17T07:11:26.4277395Z a TorchDispatchMode. 2025-07-17T07:11:26.4277568Z 2025-07-17T07:11:26.4277820Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-07-17T07:11:26.4278491Z when the implementation 2025-07-17T07:11:26.4278988Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-07-17T07:11:26.4279501Z custom operators as opaque (:func:`torch.compile` and 2025-07-17T07:11:26.4280017Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-07-17T07:11:26.4280605Z makes the implementation visible to these subsystems, allowing them 2025-07-17T07:11:26.4281069Z to optimize the triton kernel(s). 2025-07-17T07:11:26.4281282Z 2025-07-17T07:11:26.4281471Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-07-17T07:11:26.4282008Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-07-17T07:11:26.4282551Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-07-17T07:11:26.4282857Z 2025-07-17T07:11:26.4282954Z Args: 2025-07-17T07:11:26.4283305Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-07-17T07:11:26.4283873Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-07-17T07:11:26.4284380Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-07-17T07:11:26.4284900Z To avoid name collisions, please use your project name as the namespace; 2025-07-17T07:11:26.4285479Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-07-17T07:11:26.4286088Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-07-17T07:11:26.4286730Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-07-17T07:11:26.4287349Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-07-17T07:11:26.4287928Z schema (None | str): A schema string for the operator. If None 2025-07-17T07:11:26.4288444Z (recommended) we'll infer a schema for the operator from its type 2025-07-17T07:11:26.4288981Z annotations. We recommend letting us infer a schema unless you 2025-07-17T07:11:26.4289417Z have a specific reason not to. 2025-07-17T07:11:26.4289796Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-07-17T07:11:26.4290060Z 2025-07-17T07:11:26.4290162Z Example:: 2025-07-17T07:11:26.4290298Z 2025-07-17T07:11:26.4290438Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:26.4290787Z >>> import torch 2025-07-17T07:11:26.4291106Z >>> from torch.library import triton_op, wrap_triton 2025-07-17T07:11:26.4291465Z >>> 2025-07-17T07:11:26.4291677Z >>> import triton 2025-07-17T07:11:26.4291969Z >>> from triton import language as tl 2025-07-17T07:11:26.4292287Z >>> 2025-07-17T07:11:26.4292492Z >>> @triton.jit 2025-07-17T07:11:26.4292749Z >>> def add_kernel( 2025-07-17T07:11:26.4293020Z >>> in_ptr0, 2025-07-17T07:11:26.4293264Z >>> in_ptr1, 2025-07-17T07:11:26.4293522Z >>> out_ptr, 2025-07-17T07:11:26.4293911Z >>> n_elements, 2025-07-17T07:11:26.4294184Z >>> BLOCK_SIZE: "tl.constexpr", 2025-07-17T07:11:26.4294494Z >>> ): 2025-07-17T07:11:26.4294739Z >>> pid = tl.program_id(axis=0) 2025-07-17T07:11:26.4295130Z >>> block_start = pid * BLOCK_SIZE 2025-07-17T07:11:26.4295601Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-07-17T07:11:26.4295987Z >>> mask = offsets < n_elements 2025-07-17T07:11:26.4296332Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-07-17T07:11:26.4296711Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-07-17T07:11:26.4297055Z >>> output = x + y 2025-07-17T07:11:26.4297392Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-07-17T07:11:26.4297731Z >>> 2025-07-17T07:11:26.4297995Z >>> @triton_op("mylib::add", mutates_args={}) 2025-07-17T07:11:26.4298434Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-07-17T07:11:26.4298847Z >>> output = torch.empty_like(x) 2025-07-17T07:11:26.4299195Z >>> n_elements = output.numel() 2025-07-17T07:11:26.4299509Z >>> 2025-07-17T07:11:26.4299722Z >>> def grid(meta): 2025-07-17T07:11:26.4300089Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-07-17T07:11:26.4300473Z >>> 2025-07-17T07:11:26.4300791Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-07-17T07:11:26.4301276Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-07-17T07:11:26.4301686Z >>> return output 2025-07-17T07:11:26.4301958Z >>> 2025-07-17T07:11:26.4302168Z >>> @torch.compile 2025-07-17T07:11:26.4302434Z >>> def f(x, y): 2025-07-17T07:11:26.4302697Z >>> return add(x, y) 2025-07-17T07:11:26.4302957Z >>> 2025-07-17T07:11:26.4303196Z >>> x = torch.randn(3, device="cuda") 2025-07-17T07:11:26.4303543Z >>> y = torch.randn(3, device="cuda") 2025-07-17T07:11:26.4303838Z >>> 2025-07-17T07:11:26.4304052Z >>> z = f(x, y) 2025-07-17T07:11:26.4304323Z >>> assert torch.allclose(z, x + y) 2025-07-17T07:11:26.4304542Z 2025-07-17T07:11:26.4304636Z 2025-07-17T07:11:26.4304995Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.4305376Z 2025-07-17T07:11:26.4305910Z 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. 2025-07-17T07:11:26.4306804Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.4307382Z Allows capture of a triton kernel into a graph via make_fx or 2025-07-17T07:11:26.4307791Z non-strict ``torch.export``. 2025-07-17T07:11:26.4308005Z 2025-07-17T07:11:26.4308190Z These technologies perform Dispatcher-based tracing (via 2025-07-17T07:11:26.4308700Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-07-17T07:11:26.4309230Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-07-17T07:11:26.4309653Z can actually be traced into a graph. 2025-07-17T07:11:26.4309888Z 2025-07-17T07:11:26.4310102Z Please use this API together with :func:`torch.library.triton_op`. 2025-07-17T07:11:26.4310430Z 2025-07-17T07:11:26.4310529Z Examples: 2025-07-17T07:11:26.4310659Z 2025-07-17T07:11:26.4310755Z >>> # xdoctest: +SKIP 2025-07-17T07:11:26.4311032Z >>> import torch 2025-07-17T07:11:26.4311295Z >>> import triton 2025-07-17T07:11:26.4311584Z >>> from triton import language as tl 2025-07-17T07:11:26.4311986Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-07-17T07:11:26.4312413Z >>> from torch.library import wrap_triton 2025-07-17T07:11:26.4312738Z >>> 2025-07-17T07:11:26.4312942Z >>> @triton.jit 2025-07-17T07:11:26.4313196Z >>> def add_kernel( 2025-07-17T07:11:26.4313547Z >>> in_ptr0, 2025-07-17T07:11:26.4313793Z >>> in_ptr1, 2025-07-17T07:11:26.4314050Z >>> out_ptr, 2025-07-17T07:11:26.4314308Z >>> n_elements, 2025-07-17T07:11:26.4314638Z >>> BLOCK_SIZE: "tl.constexpr", 2025-07-17T07:11:26.4314951Z >>> ): 2025-07-17T07:11:26.4315198Z >>> pid = tl.program_id(axis=0) 2025-07-17T07:11:26.4315526Z >>> block_start = pid * BLOCK_SIZE 2025-07-17T07:11:26.4315904Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-07-17T07:11:26.4316280Z >>> mask = offsets < n_elements 2025-07-17T07:11:26.4316630Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-07-17T07:11:26.4316991Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-07-17T07:11:26.4317334Z >>> output = x + y 2025-07-17T07:11:26.4317663Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-07-17T07:11:26.4317998Z >>> 2025-07-17T07:11:26.4318222Z >>> def add(x, y): 2025-07-17T07:11:26.4318508Z >>> output = torch.empty_like(x) 2025-07-17T07:11:26.4318835Z >>> n_elements = output.numel() 2025-07-17T07:11:26.4319142Z >>> 2025-07-17T07:11:26.4319370Z >>> def grid_fn(meta): 2025-07-17T07:11:26.4319731Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-07-17T07:11:26.4320106Z >>> 2025-07-17T07:11:26.4320423Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-07-17T07:11:26.4320845Z >>> return output 2025-07-17T07:11:26.4321099Z >>> 2025-07-17T07:11:26.4321338Z >>> x = torch.randn(3, device="cuda") 2025-07-17T07:11:26.4321678Z >>> y = torch.randn(3, device="cuda") 2025-07-17T07:11:26.4321995Z >>> gm = make_fx(add)(x, y) 2025-07-17T07:11:26.4322295Z >>> print(gm.code) 2025-07-17T07:11:26.4322584Z >>> # def forward(self, x_1, y_1): 2025-07-17T07:11:26.4323030Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-07-17T07:11:26.4323637Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-07-17T07:11:26.4324143Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-07-17T07:11:26.4324507Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-07-17T07:11:26.4324881Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-07-17T07:11:26.4325285Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-07-17T07:11:26.4325620Z >>> # }) 2025-07-17T07:11:26.4325873Z >>> # return empty_like 2025-07-17T07:11:26.4326085Z 2025-07-17T07:11:26.4326166Z 2025-07-17T07:11:26.4326535Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.4326906Z 2025-07-17T07:11:26.4992534Z msg = Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=286. 2025-07-17T07:11:26.4993486Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.4993885Z 2025-07-17T07:11:26.4994112Z Test if two objects are equal, and print an error message if test fails. 2025-07-17T07:11:26.4994461Z 2025-07-17T07:11:26.4994604Z The test is performed with ``actual == desired``. 2025-07-17T07:11:26.4994860Z 2025-07-17T07:11:26.4994971Z Parameters 2025-07-17T07:11:26.4995284Z ---------- 2025-07-17T07:11:26.4995524Z test_string : str 2025-07-17T07:11:26.4995919Z The message supplied to AssertionError. 2025-07-17T07:11:26.4996241Z actual : object 2025-07-17T07:11:26.4996541Z The object to test for equality against `desired`. 2025-07-17T07:11:26.4996907Z desired : object 2025-07-17T07:11:26.4997140Z The expected result. 2025-07-17T07:11:26.4997323Z 2025-07-17T07:11:26.4997410Z Examples 2025-07-17T07:11:26.4997625Z -------- 2025-07-17T07:11:26.4997866Z >>> np.testing.print_assert_equal( 2025-07-17T07:11:26.4998355Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-07-17T07:11:26.4998831Z ... ) # doctest: +SKIP 2025-07-17T07:11:26.4999132Z >>> np.testing.print_assert_equal( 2025-07-17T07:11:26.4999664Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-07-17T07:11:26.4999991Z ... ) # doctest: +SKIP 2025-07-17T07:11:26.5000265Z Traceback (most recent call last): 2025-07-17T07:11:26.5000596Z ... 2025-07-17T07:11:26.5000840Z AssertionError: Test XYZ of func xyz failed 2025-07-17T07:11:26.5001148Z ACTUAL: 2025-07-17T07:11:26.5001350Z [0, 1] 2025-07-17T07:11:26.5001555Z DESIRED: 2025-07-17T07:11:26.5001755Z [0, 2] 2025-07-17T07:11:26.5001878Z 2025-07-17T07:11:26.5001883Z 2025-07-17T07:11:26.5002136Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.5002510Z 2025-07-17T07:11:26.5003095Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=331. 2025-07-17T07:11:26.5004190Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.5004574Z 2025-07-17T07:11:26.5004783Z Raises an AssertionError if two items are not equal up to desired 2025-07-17T07:11:26.5005203Z precision. 2025-07-17T07:11:26.5005331Z 2025-07-17T07:11:26.5005527Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T07:11:26.5005987Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T07:11:26.5006479Z instead of this function for more consistent floating point 2025-07-17T07:11:26.5006882Z comparisons. 2025-07-17T07:11:26.5007037Z 2025-07-17T07:11:26.5007269Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-07-17T07:11:26.5007631Z 2025-07-17T07:11:26.5007804Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-07-17T07:11:26.5008092Z 2025-07-17T07:11:26.5008322Z That is a looser test than originally documented, but agrees with what the 2025-07-17T07:11:26.5008918Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-07-17T07:11:26.5009516Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-07-17T07:11:26.5009979Z delegates to assert_array_almost_equal 2025-07-17T07:11:26.5010215Z 2025-07-17T07:11:26.5010303Z Parameters 2025-07-17T07:11:26.5010517Z ---------- 2025-07-17T07:11:26.5010728Z actual : array_like 2025-07-17T07:11:26.5010980Z The object to check. 2025-07-17T07:11:26.5011240Z desired : array_like 2025-07-17T07:11:26.5011484Z The expected object. 2025-07-17T07:11:26.5011750Z decimal : int, optional 2025-07-17T07:11:26.5012027Z Desired precision, default is 7. 2025-07-17T07:11:26.5012329Z err_msg : str, optional 2025-07-17T07:11:26.5012647Z The error message to be printed in case of failure. 2025-07-17T07:11:26.5013016Z verbose : bool, optional 2025-07-17T07:11:26.5013382Z If True, the conflicting values are appended to the error message. 2025-07-17T07:11:26.5013718Z 2025-07-17T07:11:26.5013799Z Raises 2025-07-17T07:11:26.5014001Z ------ 2025-07-17T07:11:26.5014214Z AssertionError 2025-07-17T07:11:26.5014539Z If actual and desired are not equal up to specified precision. 2025-07-17T07:11:26.5014862Z 2025-07-17T07:11:26.5014949Z See Also 2025-07-17T07:11:26.5015244Z -------- 2025-07-17T07:11:26.5015594Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T07:11:26.5016089Z relative and/or absolute precision. 2025-07-17T07:11:26.5016547Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T07:11:26.5016875Z 2025-07-17T07:11:26.5016959Z Examples 2025-07-17T07:11:26.5017166Z -------- 2025-07-17T07:11:26.5017453Z >>> from torch._numpy.testing import assert_almost_equal 2025-07-17T07:11:26.5017865Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-07-17T07:11:26.5018273Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-07-17T07:11:26.5018681Z Traceback (most recent call last): 2025-07-17T07:11:26.5019078Z ... 2025-07-17T07:11:26.5019281Z AssertionError: 2025-07-17T07:11:26.5019551Z Arrays are not almost equal to 10 decimals 2025-07-17T07:11:26.5019879Z ACTUAL: 2.3333333333333 2025-07-17T07:11:26.5020123Z DESIRED: 2.33333334 2025-07-17T07:11:26.5020406Z 2025-07-17T07:11:26.5020505Z >>> assert_almost_equal( 2025-07-17T07:11:26.5020878Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-07-17T07:11:26.5021268Z ... ) 2025-07-17T07:11:26.5021495Z Traceback (most recent call last): 2025-07-17T07:11:26.5021790Z ... 2025-07-17T07:11:26.5021987Z AssertionError: 2025-07-17T07:11:26.5022252Z Arrays are not almost equal to 9 decimals 2025-07-17T07:11:26.5022569Z 2025-07-17T07:11:26.5022802Z Mismatched elements: 1 / 2 (50%) 2025-07-17T07:11:26.5023116Z Max absolute difference: 6.666699636781459e-09 2025-07-17T07:11:26.5023487Z Max relative difference: 2.8571569790287484e-09 2025-07-17T07:11:26.5023874Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-07-17T07:11:26.5024255Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-07-17T07:11:26.5024517Z 2025-07-17T07:11:26.5024522Z 2025-07-17T07:11:26.5024773Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.5025159Z 2025-07-17T07:11:26.5025754Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=457. 2025-07-17T07:11:26.5026791Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.5027170Z 2025-07-17T07:11:26.5027394Z Raises an AssertionError if two items are not equal up to significant 2025-07-17T07:11:26.5027828Z digits. 2025-07-17T07:11:26.5027941Z 2025-07-17T07:11:26.5028133Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T07:11:26.5028602Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T07:11:26.5029085Z instead of this function for more consistent floating point 2025-07-17T07:11:26.5029488Z comparisons. 2025-07-17T07:11:26.5029642Z 2025-07-17T07:11:26.5029837Z Given two numbers, check that they are approximately equal. 2025-07-17T07:11:26.5030362Z Approximately equal is defined as the number of significant digits 2025-07-17T07:11:26.5030790Z that agree. 2025-07-17T07:11:26.5030913Z 2025-07-17T07:11:26.5031016Z Parameters 2025-07-17T07:11:26.5031219Z ---------- 2025-07-17T07:11:26.5031439Z actual : scalar 2025-07-17T07:11:26.5031682Z The object to check. 2025-07-17T07:11:26.5031948Z desired : scalar 2025-07-17T07:11:26.5032179Z The expected object. 2025-07-17T07:11:26.5032461Z significant : int, optional 2025-07-17T07:11:26.5032761Z Desired precision, default is 7. 2025-07-17T07:11:26.5033061Z err_msg : str, optional 2025-07-17T07:11:26.5033379Z The error message to be printed in case of failure. 2025-07-17T07:11:26.5033750Z verbose : bool, optional 2025-07-17T07:11:26.5034123Z If True, the conflicting values are appended to the error message. 2025-07-17T07:11:26.5034458Z 2025-07-17T07:11:26.5034541Z Raises 2025-07-17T07:11:26.5034748Z ------ 2025-07-17T07:11:26.5034950Z AssertionError 2025-07-17T07:11:26.5035293Z If actual and desired are not equal up to specified precision. 2025-07-17T07:11:26.5035603Z 2025-07-17T07:11:26.5035701Z See Also 2025-07-17T07:11:26.5035895Z -------- 2025-07-17T07:11:26.5036256Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T07:11:26.5036753Z relative and/or absolute precision. 2025-07-17T07:11:26.5037203Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T07:11:26.5037526Z 2025-07-17T07:11:26.5037610Z Examples 2025-07-17T07:11:26.5037815Z -------- 2025-07-17T07:11:26.5038050Z >>> np.testing.assert_approx_equal( 2025-07-17T07:11:26.5038357Z ... 0.12345677777777e-20, 0.1234567e-20 2025-07-17T07:11:26.5038667Z ... ) # doctest: +SKIP 2025-07-17T07:11:26.5039029Z >>> np.testing.assert_approx_equal( 2025-07-17T07:11:26.5039318Z ... 0.12345670e-20, 2025-07-17T07:11:26.5039580Z ... 0.12345671e-20, # doctest: +SKIP 2025-07-17T07:11:26.5039896Z ... significant=8, 2025-07-17T07:11:26.5040124Z ... ) 2025-07-17T07:11:26.5040412Z >>> np.testing.assert_approx_equal( 2025-07-17T07:11:26.5040716Z ... 0.12345670e-20, 2025-07-17T07:11:26.5040978Z ... 0.12345672e-20, # doctest: +SKIP 2025-07-17T07:11:26.5041281Z ... significant=8, 2025-07-17T07:11:26.5041524Z ... ) 2025-07-17T07:11:26.5041755Z Traceback (most recent call last): 2025-07-17T07:11:26.5042032Z ... 2025-07-17T07:11:26.5042246Z AssertionError: 2025-07-17T07:11:26.5042521Z Items are not equal to 8 significant digits: 2025-07-17T07:11:26.5042840Z ACTUAL: 1.234567e-21 2025-07-17T07:11:26.5043093Z DESIRED: 1.2345672e-21 2025-07-17T07:11:26.5043247Z 2025-07-17T07:11:26.5043418Z the evaluated condition that raises the exception is 2025-07-17T07:11:26.5043688Z 2025-07-17T07:11:26.5043887Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-07-17T07:11:26.5044280Z True 2025-07-17T07:11:26.5044388Z 2025-07-17T07:11:26.5044405Z 2025-07-17T07:11:26.5044656Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.5045032Z 2025-07-17T07:11:26.5045650Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=744. 2025-07-17T07:11:26.5046603Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.5046987Z 2025-07-17T07:11:26.5047213Z Raises an AssertionError if two array_like objects are not equal. 2025-07-17T07:11:26.5047537Z 2025-07-17T07:11:26.5047741Z Given two array_like objects, check that the shape is equal and all 2025-07-17T07:11:26.5048296Z elements of these objects are equal (but see the Notes for the special 2025-07-17T07:11:26.5048845Z handling of a scalar). An exception is raised at shape mismatch or 2025-07-17T07:11:26.5049395Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-07-17T07:11:26.5049944Z are compared like numbers, no assertion is raised if both objects have 2025-07-17T07:11:26.5050390Z NaNs in the same positions. 2025-07-17T07:11:26.5050575Z 2025-07-17T07:11:26.5050815Z The usual caution for verifying equality with floating point numbers is 2025-07-17T07:11:26.5051238Z advised. 2025-07-17T07:11:26.5051368Z 2025-07-17T07:11:26.5051458Z Parameters 2025-07-17T07:11:26.5051672Z ---------- 2025-07-17T07:11:26.5051884Z x : array_like 2025-07-17T07:11:26.5052119Z The actual object to check. 2025-07-17T07:11:26.5052401Z y : array_like 2025-07-17T07:11:26.5052643Z The desired, expected object. 2025-07-17T07:11:26.5052931Z err_msg : str, optional 2025-07-17T07:11:26.5053246Z The error message to be printed in case of failure. 2025-07-17T07:11:26.5053613Z verbose : bool, optional 2025-07-17T07:11:26.5053981Z If True, the conflicting values are appended to the error message. 2025-07-17T07:11:26.5054408Z strict : bool, optional 2025-07-17T07:11:26.5054775Z If True, raise an AssertionError when either the shape or the data 2025-07-17T07:11:26.5055335Z type of the array_like objects does not match. The special 2025-07-17T07:11:26.5055840Z handling for scalars mentioned in the Notes section is disabled. 2025-07-17T07:11:26.5056171Z 2025-07-17T07:11:26.5056252Z Raises 2025-07-17T07:11:26.5056453Z ------ 2025-07-17T07:11:26.5056648Z AssertionError 2025-07-17T07:11:26.5056919Z If actual and desired objects are not equal. 2025-07-17T07:11:26.5057160Z 2025-07-17T07:11:26.5057254Z See Also 2025-07-17T07:11:26.5057449Z -------- 2025-07-17T07:11:26.5057869Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T07:11:26.5058361Z relative and/or absolute precision. 2025-07-17T07:11:26.5058808Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T07:11:26.5059143Z 2025-07-17T07:11:26.5059312Z Notes 2025-07-17T07:11:26.5059517Z ----- 2025-07-17T07:11:26.5059820Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-07-17T07:11:26.5060338Z function checks that each element of the array_like object is equal to 2025-07-17T07:11:26.5060965Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-07-17T07:11:26.5061310Z 2025-07-17T07:11:26.5061403Z Examples 2025-07-17T07:11:26.5061594Z -------- 2025-07-17T07:11:26.5061846Z The first assert does not raise an exception: 2025-07-17T07:11:26.5062084Z 2025-07-17T07:11:26.5062207Z >>> np.testing.assert_array_equal( 2025-07-17T07:11:26.5062550Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-07-17T07:11:26.5062892Z ... ) 2025-07-17T07:11:26.5063017Z 2025-07-17T07:11:26.5063247Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-07-17T07:11:26.5063713Z functions for these cases instead: 2025-07-17T07:11:26.5063915Z 2025-07-17T07:11:26.5064023Z >>> np.testing.assert_allclose( 2025-07-17T07:11:26.5064430Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-07-17T07:11:26.5064838Z ... ) 2025-07-17T07:11:26.5064952Z 2025-07-17T07:11:26.5065162Z As mentioned in the Notes section, `assert_array_equal` has special 2025-07-17T07:11:26.5065719Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-07-17T07:11:26.5066078Z 2025-07-17T07:11:26.5066185Z >>> x = np.full((2, 5), fill_value=3) 2025-07-17T07:11:26.5066516Z >>> np.testing.assert_array_equal(x, 3) 2025-07-17T07:11:26.5066737Z 2025-07-17T07:11:26.5066951Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-07-17T07:11:26.5067378Z array: 2025-07-17T07:11:26.5067492Z 2025-07-17T07:11:26.5067656Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-07-17T07:11:26.5068025Z Traceback (most recent call last): 2025-07-17T07:11:26.5068325Z ... 2025-07-17T07:11:26.5068541Z AssertionError: 2025-07-17T07:11:26.5068780Z Arrays are not equal 2025-07-17T07:11:26.5069030Z 2025-07-17T07:11:26.5069260Z (shapes (2, 5), () mismatch) 2025-07-17T07:11:26.5069548Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-07-17T07:11:26.5069835Z [3, 3, 3, 3, 3]]) 2025-07-17T07:11:26.5070092Z y: torch.ndarray(3) 2025-07-17T07:11:26.5070246Z 2025-07-17T07:11:26.5070470Z The `strict` parameter also ensures that the array data types match: 2025-07-17T07:11:26.5070799Z 2025-07-17T07:11:26.5070894Z >>> x = np.array([2, 2, 2]) 2025-07-17T07:11:26.5071199Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-07-17T07:11:26.5071595Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-07-17T07:11:26.5072139Z Traceback (most recent call last): 2025-07-17T07:11:26.5072433Z ... 2025-07-17T07:11:26.5072644Z AssertionError: 2025-07-17T07:11:26.5072869Z Arrays are not equal 2025-07-17T07:11:26.5073112Z 2025-07-17T07:11:26.5073381Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-07-17T07:11:26.5073741Z x: torch.ndarray([2, 2, 2]) 2025-07-17T07:11:26.5074021Z y: torch.ndarray([2., 2., 2.]) 2025-07-17T07:11:26.5074218Z 2025-07-17T07:11:26.5074472Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.5074847Z 2025-07-17T07:11:26.5075459Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=851. 2025-07-17T07:11:26.5076421Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.5076819Z 2025-07-17T07:11:26.5077038Z Raises an AssertionError if two objects are not equal up to desired 2025-07-17T07:11:26.5077465Z precision. 2025-07-17T07:11:26.5077589Z 2025-07-17T07:11:26.5077786Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T07:11:26.5078244Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T07:11:26.5078736Z instead of this function for more consistent floating point 2025-07-17T07:11:26.5079256Z comparisons. 2025-07-17T07:11:26.5079412Z 2025-07-17T07:11:26.5079664Z The test verifies identical shapes and that the elements of ``actual`` and 2025-07-17T07:11:26.5080110Z ``desired`` satisfy. 2025-07-17T07:11:26.5080274Z 2025-07-17T07:11:26.5080473Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-07-17T07:11:26.5080719Z 2025-07-17T07:11:26.5080958Z That is a looser test than originally documented, but agrees with what the 2025-07-17T07:11:26.5081539Z actual implementation did up to rounding vagaries. An exception is raised 2025-07-17T07:11:26.5082144Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-07-17T07:11:26.5082728Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-07-17T07:11:26.5083197Z objects have NaNs in the same positions. 2025-07-17T07:11:26.5083422Z 2025-07-17T07:11:26.5083514Z Parameters 2025-07-17T07:11:26.5083735Z ---------- 2025-07-17T07:11:26.5083950Z x : array_like 2025-07-17T07:11:26.5084186Z The actual object to check. 2025-07-17T07:11:26.5084470Z y : array_like 2025-07-17T07:11:26.5084715Z The desired, expected object. 2025-07-17T07:11:26.5085000Z decimal : int, optional 2025-07-17T07:11:26.5085278Z Desired precision, default is 6. 2025-07-17T07:11:26.5085594Z err_msg : str, optional 2025-07-17T07:11:26.5085898Z The error message to be printed in case of failure. 2025-07-17T07:11:26.5086266Z verbose : bool, optional 2025-07-17T07:11:26.5086646Z If True, the conflicting values are appended to the error message. 2025-07-17T07:11:26.5086962Z 2025-07-17T07:11:26.5087058Z Raises 2025-07-17T07:11:26.5087247Z ------ 2025-07-17T07:11:26.5087456Z AssertionError 2025-07-17T07:11:26.5087793Z If actual and desired are not equal up to specified precision. 2025-07-17T07:11:26.5088101Z 2025-07-17T07:11:26.5088185Z See Also 2025-07-17T07:11:26.5088390Z -------- 2025-07-17T07:11:26.5088747Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T07:11:26.5089233Z relative and/or absolute precision. 2025-07-17T07:11:26.5089684Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T07:11:26.5090006Z 2025-07-17T07:11:26.5090101Z Examples 2025-07-17T07:11:26.5090295Z -------- 2025-07-17T07:11:26.5090546Z the first assert does not raise an exception 2025-07-17T07:11:26.5090797Z 2025-07-17T07:11:26.5091047Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-07-17T07:11:26.5091419Z 2025-07-17T07:11:26.5091548Z >>> np.testing.assert_array_almost_equal( 2025-07-17T07:11:26.5091917Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-07-17T07:11:26.5092268Z ... ) 2025-07-17T07:11:26.5092497Z Traceback (most recent call last): 2025-07-17T07:11:26.5092778Z ... 2025-07-17T07:11:26.5092988Z AssertionError: 2025-07-17T07:11:26.5093252Z Arrays are not almost equal to 5 decimals 2025-07-17T07:11:26.5093559Z 2025-07-17T07:11:26.5093793Z Mismatched elements: 1 / 3 (33.3%) 2025-07-17T07:11:26.5094140Z Max absolute difference: 5.999999999994898e-05 2025-07-17T07:11:26.5094513Z Max relative difference: 2.5713661239633743e-05 2025-07-17T07:11:26.5094909Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-07-17T07:11:26.5095428Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-07-17T07:11:26.5095700Z 2025-07-17T07:11:26.5095832Z >>> np.testing.assert_array_almost_equal( 2025-07-17T07:11:26.5096187Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-07-17T07:11:26.5096521Z ... ) 2025-07-17T07:11:26.5096748Z Traceback (most recent call last): 2025-07-17T07:11:26.5097029Z ... 2025-07-17T07:11:26.5097238Z AssertionError: 2025-07-17T07:11:26.5097501Z Arrays are not almost equal to 5 decimals 2025-07-17T07:11:26.5097811Z 2025-07-17T07:11:26.5098046Z x and y nan location mismatch: 2025-07-17T07:11:26.5098401Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-07-17T07:11:26.5098841Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-07-17T07:11:26.5099193Z 2025-07-17T07:11:26.5099197Z 2025-07-17T07:11:26.5099452Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.5099836Z 2025-07-17T07:11:26.5100522Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1848. 2025-07-17T07:11:26.5101497Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.5102104Z Context manager that resets warning registry for catching warnings 2025-07-17T07:11:26.5102432Z 2025-07-17T07:11:26.5102677Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-07-17T07:11:26.5103279Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-07-17T07:11:26.5103878Z it impossible to retrigger the warning in this module, whatever you put in 2025-07-17T07:11:26.5104494Z the warnings filters. This context manager accepts a sequence of `modules` 2025-07-17T07:11:26.5104997Z as a keyword argument to its constructor and: 2025-07-17T07:11:26.5105267Z 2025-07-17T07:11:26.5105498Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-07-17T07:11:26.5105940Z on entry; 2025-07-17T07:11:26.5106275Z * resets ``__warningregistry__`` to its previous state on exit. 2025-07-17T07:11:26.5106580Z 2025-07-17T07:11:26.5106802Z This makes it possible to trigger any warning afresh inside the context 2025-07-17T07:11:26.5107336Z manager without disturbing the state of warnings outside. 2025-07-17T07:11:26.5107629Z 2025-07-17T07:11:26.5107875Z For compatibility with Python 3.0, please consider all arguments to be 2025-07-17T07:11:26.5108315Z keyword-only. 2025-07-17T07:11:26.5108468Z 2025-07-17T07:11:26.5108556Z Parameters 2025-07-17T07:11:26.5108784Z ---------- 2025-07-17T07:11:26.5109017Z record : bool, optional 2025-07-17T07:11:26.5109383Z Specifies whether warnings should be captured by a custom 2025-07-17T07:11:26.5109925Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-07-17T07:11:26.5110499Z returned by the context manager. Otherwise None is returned by the 2025-07-17T07:11:26.5111046Z context manager. The objects appended to the list are arguments whose 2025-07-17T07:11:26.5111568Z attributes mirror the arguments to ``showwarning()``. 2025-07-17T07:11:26.5111964Z modules : sequence, optional 2025-07-17T07:11:26.5112378Z Sequence of modules for which to reset warnings registry on entry and 2025-07-17T07:11:26.5112916Z restore on exit. To work correctly, all 'ignore' filters should 2025-07-17T07:11:26.5113341Z filter by one of these modules. 2025-07-17T07:11:26.5113556Z 2025-07-17T07:11:26.5113656Z Examples 2025-07-17T07:11:26.5113861Z -------- 2025-07-17T07:11:26.5114092Z >>> import warnings 2025-07-17T07:11:26.5114449Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-07-17T07:11:26.5114853Z ... modules=[np.core.fromnumeric] 2025-07-17T07:11:26.5115166Z ... ): 2025-07-17T07:11:26.5115413Z ... warnings.simplefilter("always") 2025-07-17T07:11:26.5115859Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-07-17T07:11:26.5116382Z ... # do something that raises a warning but ignore those in 2025-07-17T07:11:26.5116777Z ... # np.core.fromnumeric 2025-07-17T07:11:26.5117057Z 2025-07-17T07:11:26.5117410Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.5117792Z 2025-07-17T07:11:26.6661097Z 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. 2025-07-17T07:11:26.6662065Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.6662673Z Applies a 1D convolution over a quantized input signal composed of 2025-07-17T07:11:26.6663266Z several quantized input planes. 2025-07-17T07:11:26.6663479Z 2025-07-17T07:11:26.6663698Z For details on input arguments, parameters, and implementation see 2025-07-17T07:11:26.6664223Z :class:`~torch.nn.Conv1d`. 2025-07-17T07:11:26.6664415Z 2025-07-17T07:11:26.6664529Z .. note:: 2025-07-17T07:11:26.6664857Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-07-17T07:11:26.6665182Z 2025-07-17T07:11:26.6665340Z .. note:: 2025-07-17T07:11:26.6665656Z Only `torch.quint8` is supported for the input data type. 2025-07-17T07:11:26.6665951Z 2025-07-17T07:11:26.6665955Z 2025-07-17T07:11:26.6666057Z Attributes: 2025-07-17T07:11:26.6666402Z weight (Tensor): packed tensor derived from the learnable weight 2025-07-17T07:11:26.6666836Z parameter. 2025-07-17T07:11:26.6667193Z scale (Tensor): scalar for the output scale 2025-07-17T07:11:26.6667610Z zero_point (Tensor): scalar for the output zero point 2025-07-17T07:11:26.6667896Z 2025-07-17T07:11:26.6668047Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-07-17T07:11:26.6668320Z 2025-07-17T07:11:26.6668414Z Examples:: 2025-07-17T07:11:26.6668564Z 2025-07-17T07:11:26.6668726Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-07-17T07:11:26.6669112Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-07-17T07:11:26.6669477Z >>> input = torch.randn(20, 16, 100) 2025-07-17T07:11:26.6669810Z >>> # quantize input to quint8 2025-07-17T07:11:26.6670112Z >>> # xdoctest: +SKIP 2025-07-17T07:11:26.6670512Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-07-17T07:11:26.6670978Z ... dtype=torch.quint8) 2025-07-17T07:11:26.6671327Z >>> output = m(q_input) 2025-07-17T07:11:26.6671511Z 2025-07-17T07:11:26.6671591Z 2025-07-17T07:11:26.6672113Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.6672485Z 2025-07-17T07:11:26.6845820Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=12. 2025-07-17T07:11:26.6846740Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.6847249Z A quantized long short-term memory (LSTM). 2025-07-17T07:11:26.6847492Z 2025-07-17T07:11:26.6847771Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-07-17T07:11:26.6848187Z 2025-07-17T07:11:26.6848276Z Attributes: 2025-07-17T07:11:26.6848540Z layers : instances of the `_LSTMLayer` 2025-07-17T07:11:26.6848771Z 2025-07-17T07:11:26.6848862Z .. note:: 2025-07-17T07:11:26.6849211Z To access the weights and biases, you need to access them per layer. 2025-07-17T07:11:26.6849727Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-07-17T07:11:26.6850016Z 2025-07-17T07:11:26.6850105Z Examples:: 2025-07-17T07:11:26.6850346Z >>> # xdoctest: +SKIP 2025-07-17T07:11:26.6850639Z >>> custom_module_config = { 2025-07-17T07:11:26.6850982Z ... 'float_to_observed_custom_module_class': { 2025-07-17T07:11:26.6851364Z ... nn.LSTM: nn.quantizable.LSTM, 2025-07-17T07:11:26.6851690Z ... }, 2025-07-17T07:11:26.6851979Z ... 'observed_to_quantized_custom_module_class': { 2025-07-17T07:11:26.6852378Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-07-17T07:11:26.6852721Z ... } 2025-07-17T07:11:26.6852950Z ... } 2025-07-17T07:11:26.6853290Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-07-17T07:11:26.6853848Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-07-17T07:11:26.6854270Z 2025-07-17T07:11:26.6854627Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.6855273Z 2025-07-17T07:11:26.7415731Z msg = Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-07-17T07:11:26.7416949Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.7417331Z 2025-07-17T07:11:26.7417608Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-07-17T07:11:26.7428550Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-07-17T07:11:26.7429238Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-07-17T07:11:26.7429716Z input by the user. 2025-07-17T07:11:26.7430105Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-07-17T07:11:26.7430604Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-07-17T07:11:26.7430902Z 2025-07-17T07:11:26.7430998Z Note:: 2025-07-17T07:11:26.7431449Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-07-17T07:11:26.7431894Z 2025-07-17T07:11:26.7431993Z Args: 2025-07-17T07:11:26.7432201Z model (nn.Module): 2025-07-17T07:11:26.7432602Z The model whose layers will be sparsified. The layers that needs to be 2025-07-17T07:11:26.7433230Z sparsified should be added separately using the register_layer() function 2025-07-17T07:11:26.7433716Z aggregate_fn (Optional, Callable): 2025-07-17T07:11:26.7434187Z default aggregate_fn that is used if not specified while registering the layer. 2025-07-17T07:11:26.7434741Z specifies how inputs should be aggregated over time. 2025-07-17T07:11:26.7435318Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-07-17T07:11:26.7435820Z Example 2025-07-17T07:11:26.7436127Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-07-17T07:11:26.7436623Z reduce_fn (Optional, Callable): 2025-07-17T07:11:26.7437262Z default reduce_fn that is used if not specified while registering the layer. 2025-07-17T07:11:26.7437889Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-07-17T07:11:26.7438394Z calling agg_fn() on all inputs. 2025-07-17T07:11:26.7438720Z Example 2025-07-17T07:11:26.7439066Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-07-17T07:11:26.7439502Z mask_fn (Optional, Callable): 2025-07-17T07:11:26.7440040Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-07-17T07:11:26.7440734Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-07-17T07:11:26.7441204Z register_layer(). 2025-07-17T07:11:26.7441753Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-07-17T07:11:26.7442326Z arguments. 2025-07-17T07:11:26.7442599Z features (Optional, list): 2025-07-17T07:11:26.7442954Z default selected features to sparsify. 2025-07-17T07:11:26.7443571Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-07-17T07:11:26.7444065Z For example, 2025-07-17T07:11:26.7444489Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-07-17T07:11:26.7444985Z feature_dim (Optional, int): 2025-07-17T07:11:26.7445493Z default dimension of input features. Again, features along this dim will be chosen 2025-07-17T07:11:26.7445991Z for sparsification. 2025-07-17T07:11:26.7446305Z sparse_config (Dict): 2025-07-17T07:11:26.7446728Z Default configuration for the mask_fn. This config will be passed 2025-07-17T07:11:26.7447332Z with the mask_fn() 2025-07-17T07:11:26.7447529Z 2025-07-17T07:11:26.7447617Z Example: 2025-07-17T07:11:26.7447844Z >>> # xdoctest: +SKIP 2025-07-17T07:11:26.7448120Z >>> model = SomeModel() 2025-07-17T07:11:26.7448597Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-07-17T07:11:26.7449072Z >>> # Initialize aggregate_fn 2025-07-17T07:11:26.7449370Z >>> def agg_fn(x, y): 2025-07-17T07:11:26.7449620Z >>> return x + y 2025-07-17T07:11:26.7449875Z >>> 2025-07-17T07:11:26.7450100Z >>> # Initialize reduce_fn 2025-07-17T07:11:26.7450372Z >>> def reduce_fn(x): 2025-07-17T07:11:26.7450652Z >>> return torch.mean(x, dim=0) 2025-07-17T07:11:26.7450957Z >>> 2025-07-17T07:11:26.7451162Z >>> # Initialize mask_fn 2025-07-17T07:11:26.7451445Z >>> def mask_fn(data): 2025-07-17T07:11:26.7451765Z >>> return torch.eye(data.shape).to(data.device) 2025-07-17T07:11:26.7452113Z >>> 2025-07-17T07:11:26.7452302Z >>> 2025-07-17T07:11:26.7452535Z >>> act_sparsifier.register_layer( 2025-07-17T07:11:26.7452859Z ... model.some_layer, 2025-07-17T07:11:26.7453137Z ... aggregate_fn=agg_fn, 2025-07-17T07:11:26.7453437Z ... reduce_fn=reduce_fn, 2025-07-17T07:11:26.7453728Z ... mask_fn=mask_fn, 2025-07-17T07:11:26.7453980Z ... ) 2025-07-17T07:11:26.7454187Z >>> 2025-07-17T07:11:26.7454410Z >>> # start training process 2025-07-17T07:11:26.7454684Z >>> for _ in [...]: 2025-07-17T07:11:26.7454937Z >>> # epoch starts 2025-07-17T07:11:26.7455348Z >>> # model.forward(), compute_loss() and model.backwards() 2025-07-17T07:11:26.7455724Z >>> # epoch ends 2025-07-17T07:11:26.7455986Z >>> act_sparsifier.step() 2025-07-17T07:11:26.7456294Z >>> # end training process 2025-07-17T07:11:26.7456579Z >>> sparsifier.squash_mask() 2025-07-17T07:11:26.7456785Z 2025-07-17T07:11:26.7457039Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.7457429Z 2025-07-17T07:11:26.7458301Z msg = Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-07-17T07:11:26.7459537Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.7459916Z 2025-07-17T07:11:26.7460128Z Abstract method that needs to be implemented by the child class. 2025-07-17T07:11:26.7460704Z The expected return type should is a dictionary of name to schedule_param value 2025-07-17T07:11:26.7461352Z The returned values will be updated in sparsifier when the scheduler step() function 2025-07-17T07:11:26.7461834Z is called. 2025-07-17T07:11:26.7461955Z 2025-07-17T07:11:26.7462051Z Example: 2025-07-17T07:11:26.7462274Z >>> def get_schedule_param(self): 2025-07-17T07:11:26.7462583Z ... new_param = {} 2025-07-17T07:11:26.7462918Z ... for name in self.sparsifier.data_groups.keys(): 2025-07-17T07:11:26.7463286Z ... new_param[name] = ( 2025-07-17T07:11:26.7463700Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-07-17T07:11:26.7464117Z ... ) 2025-07-17T07:11:26.7464345Z ... return new_param 2025-07-17T07:11:26.7464533Z 2025-07-17T07:11:26.7464872Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-07-17T07:11:26.7465426Z would be halved 2025-07-17T07:11:26.7465563Z 2025-07-17T07:11:26.7465827Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.7466196Z 2025-07-17T07:11:26.7656782Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=229. 2025-07-17T07:11:26.7657884Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.7658589Z Squashes the sparse masks into the appropriate tensors. 2025-07-17T07:11:26.7658873Z 2025-07-17T07:11:26.7659098Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-07-17T07:11:26.7659695Z the module will have a `sparse_params` dict attached to it. 2025-07-17T07:11:26.7660009Z 2025-07-17T07:11:26.7660095Z Args: 2025-07-17T07:11:26.7660423Z params_to_keep: List of keys to save in the module or a dict 2025-07-17T07:11:26.7660891Z representing the modules and keys that will have 2025-07-17T07:11:26.7661287Z sparsity parameters saved 2025-07-17T07:11:26.7661740Z params_to_keep_per_layer: Dict to specify the params that should be 2025-07-17T07:11:26.7662231Z saved for specific layers. The keys in the dict 2025-07-17T07:11:26.7662653Z should be the module fqn, while the values should 2025-07-17T07:11:26.7663099Z be a list of strings with the names of the variables 2025-07-17T07:11:26.7663506Z to save in the `sparse_params` 2025-07-17T07:11:26.7663744Z 2025-07-17T07:11:26.7663847Z Examples: 2025-07-17T07:11:26.7664117Z >>> # xdoctest: +SKIP("locals are undefined") 2025-07-17T07:11:26.7664483Z >>> # Don't save any sparse params 2025-07-17T07:11:26.7664845Z >>> sparsifier.squash_mask() 2025-07-17T07:11:26.7665208Z >>> hasattr(model.submodule1, "sparse_params") 2025-07-17T07:11:26.7665540Z False 2025-07-17T07:11:26.7665688Z 2025-07-17T07:11:26.7665804Z >>> # Keep sparse params per layer 2025-07-17T07:11:26.7666146Z >>> sparsifier.squash_mask( 2025-07-17T07:11:26.7666466Z ... params_to_keep_per_layer={ 2025-07-17T07:11:26.7666828Z ... "submodule1.linear1": ("foo", "bar"), 2025-07-17T07:11:26.7667206Z ... "submodule2.linear42": ("baz",), 2025-07-17T07:11:26.7667528Z ... } 2025-07-17T07:11:26.7667763Z ... ) 2025-07-17T07:11:26.7668062Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T07:11:26.7668426Z {'foo': 42, 'bar': 24} 2025-07-17T07:11:26.7668783Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T07:11:26.7669154Z {'baz': 0.1} 2025-07-17T07:11:26.7669310Z 2025-07-17T07:11:26.7669442Z >>> # Keep sparse params for all layers 2025-07-17T07:11:26.7669844Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-07-17T07:11:26.7670293Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T07:11:26.7670663Z {'foo': 42, 'bar': 24} 2025-07-17T07:11:26.7671004Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T07:11:26.7671374Z {'foo': 42, 'bar': 24} 2025-07-17T07:11:26.7671574Z 2025-07-17T07:11:26.7671940Z >>> # Keep some sparse params for all layers, and specific ones for 2025-07-17T07:11:26.7672369Z >>> # some other layers 2025-07-17T07:11:26.7672671Z >>> sparsifier.squash_mask( 2025-07-17T07:11:26.7673014Z ... params_to_keep=("foo", "bar"), 2025-07-17T07:11:26.7673440Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-07-17T07:11:26.7673831Z ... ) 2025-07-17T07:11:26.7674127Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T07:11:26.7674497Z {'foo': 42, 'bar': 24} 2025-07-17T07:11:26.7674838Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T07:11:26.7675221Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-07-17T07:11:26.7675525Z 2025-07-17T07:11:26.7675897Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.7676267Z 2025-07-17T07:11:26.8422330Z 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. 2025-07-17T07:11:26.8423575Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.8423977Z 2025-07-17T07:11:26.8424314Z Config object that specifies the supported data types passed as arguments to 2025-07-17T07:11:26.8424934Z quantize ops in the reference model spec, for input and output activations, 2025-07-17T07:11:26.8425381Z weights, and biases. 2025-07-17T07:11:26.8425551Z 2025-07-17T07:11:26.8425709Z For example, consider the following reference model: 2025-07-17T07:11:26.8425979Z 2025-07-17T07:11:26.8426172Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-07-17T07:11:26.8426436Z 2025-07-17T07:11:26.8426661Z The pattern in the square brackets refers to the reference pattern of 2025-07-17T07:11:26.8427219Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-07-17T07:11:26.8427803Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-07-17T07:11:26.8428387Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-07-17T07:11:26.8428960Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-07-17T07:11:26.8429408Z the second quantize op (quant2). 2025-07-17T07:11:26.8429618Z 2025-07-17T07:11:26.8429835Z Note that the dtype here does not refer to the interface dtypes of the 2025-07-17T07:11:26.8430379Z op. For example, the "input dtype" here is not the dtype of the input 2025-07-17T07:11:26.8430908Z tensor passed to the quantized linear op. Though it can still be the 2025-07-17T07:11:26.8431443Z same as the interface dtype, this is not always the case, e.g. the 2025-07-17T07:11:26.8431977Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-07-17T07:11:26.8432529Z specified in the DTypeConfig would still be quint8. The semantics of 2025-07-17T07:11:26.8433059Z dtypes here are the same as the semantics of the dtypes specified in 2025-07-17T07:11:26.8433484Z the observers. 2025-07-17T07:11:26.8433620Z 2025-07-17T07:11:26.8433838Z These dtypes are matched against the ones specified in the user's 2025-07-17T07:11:26.8434373Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-07-17T07:11:26.8434936Z specified in the DTypeConfig (if any), then we will quantize the given 2025-07-17T07:11:26.8435509Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-07-17T07:11:26.8435970Z the pattern will not be quantized. 2025-07-17T07:11:26.8436171Z 2025-07-17T07:11:26.8436276Z Example usage:: 2025-07-17T07:11:26.8436426Z 2025-07-17T07:11:26.8436532Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:26.8436842Z >>> dtype_config1 = DTypeConfig( 2025-07-17T07:11:26.8437152Z ... input_dtype=torch.quint8, 2025-07-17T07:11:26.8437472Z ... output_dtype=torch.quint8, 2025-07-17T07:11:26.8437802Z ... weight_dtype=torch.qint8, 2025-07-17T07:11:26.8438117Z ... bias_dtype=torch.float) 2025-07-17T07:11:26.8438321Z 2025-07-17T07:11:26.8438426Z >>> dtype_config2 = DTypeConfig( 2025-07-17T07:11:26.8438766Z ... input_dtype=DTypeWithConstraints( 2025-07-17T07:11:26.8439106Z ... dtype=torch.quint8, 2025-07-17T07:11:26.8439404Z ... quant_min_lower_bound=0, 2025-07-17T07:11:26.8439726Z ... quant_max_upper_bound=255, 2025-07-17T07:11:26.8440027Z ... ), 2025-07-17T07:11:26.8440274Z ... output_dtype=DTypeWithConstraints( 2025-07-17T07:11:26.8440617Z ... dtype=torch.quint8, 2025-07-17T07:11:26.8440920Z ... quant_min_lower_bound=0, 2025-07-17T07:11:26.8441232Z ... quant_max_upper_bound=255, 2025-07-17T07:11:26.8441533Z ... ), 2025-07-17T07:11:26.8441794Z ... weight_dtype=DTypeWithConstraints( 2025-07-17T07:11:26.8442137Z ... dtype=torch.qint8, 2025-07-17T07:11:26.8442436Z ... quant_min_lower_bound=-128, 2025-07-17T07:11:26.8442846Z ... quant_max_upper_bound=127, 2025-07-17T07:11:26.8443156Z ... ), 2025-07-17T07:11:26.8443381Z ... bias_dtype=torch.float) 2025-07-17T07:11:26.8443598Z 2025-07-17T07:11:26.8443705Z >>> dtype_config1.input_dtype 2025-07-17T07:11:26.8443997Z torch.quint8 2025-07-17T07:11:26.8444218Z 2025-07-17T07:11:26.8444325Z >>> dtype_config2.input_dtype 2025-07-17T07:11:26.8444613Z torch.quint8 2025-07-17T07:11:26.8444750Z 2025-07-17T07:11:26.8444895Z >>> dtype_config2.input_dtype_with_constraints 2025-07-17T07:11:26.8445691Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-07-17T07:11:26.8446342Z 2025-07-17T07:11:26.8446595Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.8446981Z 2025-07-17T07:11:26.9343535Z msg = Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-07-17T07:11:26.9344601Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.9344985Z 2025-07-17T07:11:26.9345313Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-07-17T07:11:26.9346034Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-07-17T07:11:26.9346766Z though the requirements on the type of model more-so depends on the specific report the user 2025-07-17T07:11:26.9347501Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-07-17T07:11:26.9348203Z a set of Detector classes, each of which generate reports on quantization configuration 2025-07-17T07:11:26.9348695Z issues a use might have. 2025-07-17T07:11:26.9348879Z 2025-07-17T07:11:26.9349007Z Currently supports generating reports on: 2025-07-17T07:11:26.9349474Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-07-17T07:11:26.9350100Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-07-17T07:11:26.9350773Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-07-17T07:11:26.9351410Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-07-17T07:11:26.9351734Z 2025-07-17T07:11:26.9352145Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-07-17T07:11:26.9353029Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-07-17T07:11:26.9353908Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-07-17T07:11:26.9354624Z has the capability to remove all the observers it inserted as well. 2025-07-17T07:11:26.9354946Z 2025-07-17T07:11:26.9355244Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-07-17T07:11:26.9355651Z 2025-07-17T07:11:26.9356040Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-07-17T07:11:26.9356891Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-07-17T07:11:26.9357324Z 2025-07-17T07:11:26.9357613Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-07-17T07:11:26.9358260Z This set is generated by calling the get_detector_name() of each detector 2025-07-17T07:11:26.9358604Z 2025-07-17T07:11:26.9358953Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-07-17T07:11:26.9359736Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-07-17T07:11:26.9360279Z can be removed at the end if desired 2025-07-17T07:11:26.9360513Z 2025-07-17T07:11:26.9361028Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-07-17T07:11:26.9361712Z This is to ensure we only insert observers once with the ModelReport instance 2025-07-17T07:11:26.9362068Z 2025-07-17T07:11:26.9362424Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-07-17T07:11:26.9363095Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-07-17T07:11:26.9363835Z instance. This also allows the functionality where we can generate the report multiple times 2025-07-17T07:11:26.9364422Z as long as we haven't removed the observers yet. 2025-07-17T07:11:26.9364677Z 2025-07-17T07:11:26.9364759Z Note: 2025-07-17T07:11:26.9365181Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-07-17T07:11:26.9365925Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-07-17T07:11:26.9366671Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-07-17T07:11:26.9367201Z the QuantizationTracer class. 2025-07-17T07:11:26.9367422Z 2025-07-17T07:11:26.9367530Z General Flow for Fx workflow: 2025-07-17T07:11:26.9368127Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-07-17T07:11:26.9368750Z 2.) Prepare your model with prepare_fx 2025-07-17T07:11:26.9369223Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-07-17T07:11:26.9369701Z 4.) Calibrate your model with data 2025-07-17T07:11:26.9370265Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-07-17T07:11:26.9370821Z Optional 2025-07-17T07:11:26.9371215Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-07-17T07:11:26.9372057Z 7.) To help in parsing report information and debugging, view report info as a: 2025-07-17T07:11:26.9372515Z - Table 2025-07-17T07:11:26.9372766Z - Histogram 2025-07-17T07:11:26.9372991Z - Line plot 2025-07-17T07:11:26.9373465Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-07-17T07:11:26.9373901Z 2025-07-17T07:11:26.9374031Z Example (with QuantizationTracer): 2025-07-17T07:11:26.9374333Z >>> # xdoctest: +SKIP 2025-07-17T07:11:26.9374612Z >>> # get the necessary qconfig 2025-07-17T07:11:26.9374935Z >>> config = PrepareCustomConfig() 2025-07-17T07:11:26.9375356Z >>> skipped_module_names, skipped_module_classes = ( 2025-07-17T07:11:26.9375792Z ... get_skipped_module_name_and_classes(config, False) 2025-07-17T07:11:26.9376160Z ... ) 2025-07-17T07:11:26.9376279Z 2025-07-17T07:11:26.9376423Z >>> # initialize our model and get GraphModule 2025-07-17T07:11:26.9376756Z >>> model = SomeModel() 2025-07-17T07:11:26.9377193Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-07-17T07:11:26.9377740Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-07-17T07:11:26.9378023Z 2025-07-17T07:11:26.9378172Z >>> # get our set of detectors and ModelReport instance 2025-07-17T07:11:26.9378549Z >>> detector_set = set( 2025-07-17T07:11:26.9378810Z ... [ 2025-07-17T07:11:26.9379084Z ... DynamicStaticDetector(tolerance=0.5), 2025-07-17T07:11:26.9379525Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-07-17T07:11:26.9379925Z ... ] 2025-07-17T07:11:26.9380142Z ... ) 2025-07-17T07:11:26.9380466Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-07-17T07:11:26.9380807Z 2025-07-17T07:11:26.9380963Z >>> # now we insert the observers and calibrate the model 2025-07-17T07:11:26.9381498Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-07-17T07:11:26.9382009Z >>> for i in range(num_callibration_batches): 2025-07-17T07:11:26.9382504Z >>> example_input = get_callibration_input() 2025-07-17T07:11:26.9382899Z >>> tracer_model_with_observers(example_input) 2025-07-17T07:11:26.9383147Z 2025-07-17T07:11:26.9383491Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-07-17T07:11:26.9384023Z >>> reports = tracer_reporter.generate_model_report( 2025-07-17T07:11:26.9384409Z ... remove_inserted_observers=True 2025-07-17T07:11:26.9384725Z ... ) 2025-07-17T07:11:26.9384843Z 2025-07-17T07:11:26.9385069Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-07-17T07:11:26.9385584Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-07-17T07:11:26.9385870Z 2025-07-17T07:11:26.9386115Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-07-17T07:11:26.9386668Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-07-17T07:11:26.9386966Z 2025-07-17T07:11:26.9387245Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-07-17T07:11:26.9387864Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-07-17T07:11:26.9388202Z 2025-07-17T07:11:26.9388206Z 2025-07-17T07:11:26.9388462Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.9388828Z 2025-07-17T07:11:26.9412371Z 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. 2025-07-17T07:11:26.9413660Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.9414127Z 2025-07-17T07:11:26.9414502Z Takes in optional filter values and generates two tables with desired information. 2025-07-17T07:11:26.9414903Z 2025-07-17T07:11:26.9415133Z The generated tables are presented in both a list-of-lists format 2025-07-17T07:11:26.9415554Z 2025-07-17T07:11:26.9415761Z The reason for the two tables are that they handle different things: 2025-07-17T07:11:26.9416255Z 1.) the first table handles all tensor level information 2025-07-17T07:11:26.9416751Z 2.) the second table handles and displays all channel based information 2025-07-17T07:11:26.9417082Z 2025-07-17T07:11:26.9417397Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-07-17T07:11:26.9418270Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-07-17T07:11:26.9419293Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-07-17T07:11:26.9419773Z 2025-07-17T07:11:26.9419882Z Tensor table columns: 2025-07-17T07:11:26.9420229Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-07-17T07:11:26.9420700Z ---- --------- --------- --------- --------- --------- 2025-07-17T07:11:26.9420978Z 2025-07-17T07:11:26.9421099Z Per-Channel table columns: 2025-07-17T07:11:26.9421541Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-07-17T07:11:26.9422173Z ---- --------- ------- --------- --------- --------- --------- 2025-07-17T07:11:26.9422467Z 2025-07-17T07:11:26.9422550Z Args: 2025-07-17T07:11:26.9422938Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T07:11:26.9423430Z contain this filter substring 2025-07-17T07:11:26.9423811Z Default = "", results in all the features being printed 2025-07-17T07:11:26.9424356Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T07:11:26.9424978Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T07:11:26.9425357Z 2025-07-17T07:11:26.9425470Z Returns a dictionary with two keys: 2025-07-17T07:11:26.9425973Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-07-17T07:11:26.9426393Z "tensor_level_info", "channel_level_info" 2025-07-17T07:11:26.9426729Z Each key maps to a tuple with: 2025-07-17T07:11:26.9427077Z A list of the headers of each table 2025-07-17T07:11:26.9427553Z A list of lists containing the table information row by row 2025-07-17T07:11:26.9428023Z The 0th index row will contain the headers of the columns 2025-07-17T07:11:26.9428438Z The rest of the rows will contain data 2025-07-17T07:11:26.9428675Z 2025-07-17T07:11:26.9428777Z Example Use: 2025-07-17T07:11:26.9429038Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:26.9429419Z >>> mod_report_visualizer.generate_filtered_tables( 2025-07-17T07:11:26.9429880Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T07:11:26.9430471Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-07-17T07:11:26.9430868Z 2025-07-17T07:11:26.9431120Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.9431503Z 2025-07-17T07:11:26.9432420Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=399. 2025-07-17T07:11:26.9433717Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.9434096Z 2025-07-17T07:11:26.9434380Z Takes in optional filter values and prints out formatted tables of the information. 2025-07-17T07:11:26.9434769Z 2025-07-17T07:11:26.9435123Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-07-17T07:11:26.9435744Z 1.) the first table handles all tensor level information 2025-07-17T07:11:26.9436235Z 2.) the second table handles and displays all channel based information 2025-07-17T07:11:26.9436570Z 2025-07-17T07:11:26.9436898Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-07-17T07:11:26.9437658Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-07-17T07:11:26.9438473Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-07-17T07:11:26.9438959Z 2025-07-17T07:11:26.9439056Z Tensor table columns: 2025-07-17T07:11:26.9439411Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-07-17T07:11:26.9439853Z ---- --------- --------- --------- --------- --------- 2025-07-17T07:11:26.9440128Z 2025-07-17T07:11:26.9440233Z Per-Channel table columns: 2025-07-17T07:11:26.9440411Z 2025-07-17T07:11:26.9440639Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-07-17T07:11:26.9441136Z ---- --------- ------- --------- --------- --------- --------- 2025-07-17T07:11:26.9441419Z 2025-07-17T07:11:26.9441500Z Args: 2025-07-17T07:11:26.9441885Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T07:11:26.9442388Z contain this filter substring 2025-07-17T07:11:26.9442760Z Default = "", results in all the features being printed 2025-07-17T07:11:26.9443307Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T07:11:26.9443939Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T07:11:26.9444306Z 2025-07-17T07:11:26.9444405Z Example Use: 2025-07-17T07:11:26.9444658Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:26.9445066Z >>> mod_report_visualizer.generate_table_visualization( 2025-07-17T07:11:26.9445546Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T07:11:26.9445934Z ... ) 2025-07-17T07:11:26.9446250Z >>> # prints out neatly formatted table with per_channel_min info 2025-07-17T07:11:26.9446768Z >>> # for all modules in block 1 of the model 2025-07-17T07:11:26.9447004Z 2025-07-17T07:11:26.9447269Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.9447639Z 2025-07-17T07:11:26.9448703Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=564. 2025-07-17T07:11:26.9449989Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.9450383Z 2025-07-17T07:11:26.9450619Z Takes in a feature and optional module_filter and plots of the desired data. 2025-07-17T07:11:26.9450987Z 2025-07-17T07:11:26.9451261Z For per channel features, it averages the value across the channels and plots a point 2025-07-17T07:11:26.9451922Z per module. The reason for this is that for models with hundreds of channels, it can 2025-07-17T07:11:26.9452578Z be hard to differentiate one channel line from another, and so the point of generating 2025-07-17T07:11:26.9453267Z a single average point per module is to give a sense of general trends that encourage 2025-07-17T07:11:26.9453762Z further deep dives. 2025-07-17T07:11:26.9453914Z 2025-07-17T07:11:26.9454016Z Note: 2025-07-17T07:11:26.9454394Z Only features in the report that have tensor value data are plottable by this class 2025-07-17T07:11:26.9455055Z When the tensor information is plotted, it will plot: 2025-07-17T07:11:26.9455540Z idx as the x val, feature value as the y_val 2025-07-17T07:11:26.9455950Z When the channel information is plotted, it will plot: 2025-07-17T07:11:26.9456509Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-07-17T07:11:26.9457130Z The reason for this is that we want to be able to compare values across the 2025-07-17T07:11:26.9457725Z channels for same layer, and it will be hard if values are staggered by idx 2025-07-17T07:11:26.9458253Z This means each module is represented by only 1 x value 2025-07-17T07:11:26.9458631Z Args: 2025-07-17T07:11:26.9458982Z feature_filter (str): Filters the features presented to only those that 2025-07-17T07:11:26.9459437Z contain this filter substring 2025-07-17T07:11:26.9459916Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T07:11:26.9460551Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T07:11:26.9460920Z 2025-07-17T07:11:26.9461011Z Example Use: 2025-07-17T07:11:26.9461277Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:26.9461684Z >>> mod_report_visualizer.generate_plot_visualization( 2025-07-17T07:11:26.9462160Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T07:11:26.9462545Z ... ) 2025-07-17T07:11:26.9462852Z >>> # outputs line plot of per_channel_min information for all 2025-07-17T07:11:26.9463338Z >>> # modules in block1 of model each channel gets it's own line, 2025-07-17T07:11:26.9463804Z >>> # and it's plotted across the in-order modules on the x-axis 2025-07-17T07:11:26.9464105Z 2025-07-17T07:11:26.9464359Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.9464740Z 2025-07-17T07:11:26.9465680Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=643. 2025-07-17T07:11:26.9466998Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:26.9467378Z 2025-07-17T07:11:26.9467665Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-07-17T07:11:26.9468064Z 2025-07-17T07:11:26.9468146Z Note: 2025-07-17T07:11:26.9468532Z Only features in the report that have tensor value data can be viewed as a histogram 2025-07-17T07:11:26.9469270Z If you want to plot a histogram from all the channel values of a specific feature for 2025-07-17T07:11:26.9469947Z a specific model, make sure to specify both the model and the feature properly 2025-07-17T07:11:26.9470573Z in the filters and you should be able to see a distribution of the channel data 2025-07-17T07:11:26.9470953Z 2025-07-17T07:11:26.9471035Z Args: 2025-07-17T07:11:26.9471425Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T07:11:26.9472092Z contain this filter substring 2025-07-17T07:11:26.9472477Z Default = "", results in all the features being printed 2025-07-17T07:11:26.9473024Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T07:11:26.9473645Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T07:11:26.9474251Z num_bins (int, optional): The number of bins to create the histogram with 2025-07-17T07:11:26.9474789Z Default = 10, the values will be split into 10 equal sized bins 2025-07-17T07:11:26.9475094Z 2025-07-17T07:11:26.9475195Z Example Use: 2025-07-17T07:11:26.9475422Z >>> # xdoctest: +SKIP 2025-07-17T07:11:26.9475898Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-07-17T07:11:26.9476518Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T07:11:26.9476905Z ... ) 2025-07-17T07:11:26.9477308Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-07-17T07:11:26.9477970Z information is gathered across all channels for all modules in block 1 for the 2025-07-17T07:11:26.9478578Z per_channel_min and is displayed in a histogram of equally sized bins 2025-07-17T07:11:26.9478916Z 2025-07-17T07:11:26.9479169Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:26.9479558Z 2025-07-17T07:11:27.0629129Z msg = Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py line=734. 2025-07-17T07:11:27.0630064Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.0630844Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-07-17T07:11:27.0631541Z Label will only appear if CPU activity tracing is enabled. 2025-07-17T07:11:27.0631837Z 2025-07-17T07:11:27.0631984Z It is useful when tracing the code profile. 2025-07-17T07:11:27.0632225Z 2025-07-17T07:11:27.0632309Z Args: 2025-07-17T07:11:27.0632582Z name (str): Label assigned to the block of code. 2025-07-17T07:11:27.0633028Z node_id (int): ID of node, for distributed profiling. Unset in 2025-07-17T07:11:27.0633437Z non-distributed cases. 2025-07-17T07:11:27.0633623Z 2025-07-17T07:11:27.0633720Z Example: 2025-07-17T07:11:27.0634036Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-07-17T07:11:27.0634471Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-07-17T07:11:27.0634889Z >>> with torch.autograd.profiler.profile() as prof: 2025-07-17T07:11:27.0635261Z ... y = x**2 2025-07-17T07:11:27.0635587Z ... with torch.autograd.profiler.record_function( 2025-07-17T07:11:27.0635960Z ... "label-z" 2025-07-17T07:11:27.0636232Z ... ): # label the block 2025-07-17T07:11:27.0636528Z ... z = y**3 2025-07-17T07:11:27.0636802Z ... y.backward() 2025-07-17T07:11:27.0637069Z >>> # xdoctest: +IGNORE_WANT 2025-07-17T07:11:27.0637409Z >>> # NOTE: some columns were removed for brevity 2025-07-17T07:11:27.0637861Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-07-17T07:11:27.0638374Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T07:11:27.0639038Z Name Self CPU total % CPU time avg Number of Calls 2025-07-17T07:11:27.0639626Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T07:11:27.0640081Z pow 60.77% 47.470us 3 2025-07-17T07:11:27.0640458Z mul 21.73% 25.465us 2 2025-07-17T07:11:27.0640890Z PowBackward0 12.03% 121.891us 1 2025-07-17T07:11:27.0641435Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-07-17T07:11:27.0641908Z label-z 2.13% 12.421us 1 2025-07-17T07:11:27.0642385Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-07-17T07:11:27.0642904Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T07:11:27.0643338Z Self CPU time total: 234.344us 2025-07-17T07:11:27.0643647Z CUDA time total: 0.000us 2025-07-17T07:11:27.0643851Z 2025-07-17T07:11:27.0643931Z 2025-07-17T07:11:27.0644311Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.0644680Z 2025-07-17T07:11:27.1905036Z 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=685. 2025-07-17T07:11:27.1905999Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:27.1906380Z 2025-07-17T07:11:27.1906650Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-07-17T07:11:27.1907319Z The submesh created consists of the dimensions and the communicators indicated by 2025-07-17T07:11:27.1907807Z ``mesh_dim_names`` 2025-07-17T07:11:27.1907948Z 2025-07-17T07:11:27.1908040Z Args: 2025-07-17T07:11:27.1908395Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-07-17T07:11:27.1908945Z mesh dimension of the DeviceMesh to create the submesh for. 2025-07-17T07:11:27.1909325Z Returns: 2025-07-17T07:11:27.1909563Z A :class:`DeviceMesh` object 2025-07-17T07:11:27.1909754Z 2025-07-17T07:11:27.1910051Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-07-17T07:11:27.1910563Z In the first example: 2025-07-17T07:11:27.1910970Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-07-17T07:11:27.1911600Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-07-17T07:11:27.1912204Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-07-17T07:11:27.1912776Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-07-17T07:11:27.1913359Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-07-17T07:11:27.1913928Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-07-17T07:11:27.1914269Z 2025-07-17T07:11:27.1914382Z In the second example: 2025-07-17T07:11:27.1914806Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-07-17T07:11:27.1915464Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-07-17T07:11:27.1916133Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-07-17T07:11:27.1916782Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-07-17T07:11:27.1917165Z 2025-07-17T07:11:27.1917264Z Example:: 2025-07-17T07:11:27.1917397Z 2025-07-17T07:11:27.1917502Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T07:11:27.1918043Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-07-17T07:11:27.1918422Z >>> 2025-07-17T07:11:27.1918742Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-07-17T07:11:27.1919216Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-07-17T07:11:27.1919810Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-07-17T07:11:27.1920284Z >>> tp_mesh = mesh_2d["tp"] 2025-07-17T07:11:27.1920582Z >>> dp_mesh = mesh_2d["dp"] 2025-07-17T07:11:27.1920856Z >>> 2025-07-17T07:11:27.1921063Z >>> # Initialize a 3D mesh. 2025-07-17T07:11:27.1921540Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-07-17T07:11:27.1922250Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-07-17T07:11:27.1922791Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-07-17T07:11:27.1923112Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-07-17T07:11:27.1923342Z 2025-07-17T07:11:27.1924026Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2025-07-17T07:11:27.1924829Z 2025-07-17T07:11:27.1925086Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-07-17T07:11:27.1925579Z ^ 2025-07-17T07:11:27.2230534Z 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=2706. 2025-07-17T07:11:27.2231537Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2231934Z 2025-07-17T07:11:27.2232189Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-07-17T07:11:27.2232557Z 2025-07-17T07:11:27.2232819Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-07-17T07:11:27.2233384Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-07-17T07:11:27.2233705Z 2025-07-17T07:11:27.2233789Z Args: 2025-07-17T07:11:27.2234159Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-07-17T07:11:27.2234737Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-07-17T07:11:27.2235287Z matters and it needs to match with corresponding isend/irecv on the 2025-07-17T07:11:27.2235708Z remote end. 2025-07-17T07:11:27.2235850Z 2025-07-17T07:11:27.2235947Z Returns: 2025-07-17T07:11:27.2236303Z A list of distributed request objects returned by calling the corresponding 2025-07-17T07:11:27.2236765Z op in the op_list. 2025-07-17T07:11:27.2236935Z 2025-07-17T07:11:27.2237022Z Examples: 2025-07-17T07:11:27.2237284Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T07:11:27.2237725Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-07-17T07:11:27.2238356Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-07-17T07:11:27.2238835Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-07-17T07:11:27.2239260Z >>> recv_op = dist.P2POp( 2025-07-17T07:11:27.2239651Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-07-17T07:11:27.2240047Z ... ) 2025-07-17T07:11:27.2240304Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-07-17T07:11:27.2240637Z >>> for req in reqs: 2025-07-17T07:11:27.2240895Z >>> req.wait() 2025-07-17T07:11:27.2241140Z >>> recv_tensor 2025-07-17T07:11:27.2241368Z tensor([2, 3]) # Rank 0 2025-07-17T07:11:27.2241646Z tensor([0, 1]) # Rank 1 2025-07-17T07:11:27.2241824Z 2025-07-17T07:11:27.2242095Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-07-17T07:11:27.2242671Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-07-17T07:11:27.2243320Z lead to unexpected hang issues. 2025-07-17T07:11:27.2243531Z 2025-07-17T07:11:27.2243759Z In addition, if this API is the first collective call in the ``group`` 2025-07-17T07:11:27.2244400Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-07-17T07:11:27.2244957Z this API call; otherwise, the behavior is undefined. If this API call is 2025-07-17T07:11:27.2245532Z not the first collective call in the ``group``, batched P2P operations 2025-07-17T07:11:27.2246079Z involving only a subset of ranks of the ``group`` are allowed. 2025-07-17T07:11:27.2246386Z 2025-07-17T07:11:27.2246636Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2247017Z 2025-07-17T07:11:27.2247608Z 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=2838. 2025-07-17T07:11:27.2248563Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2248958Z 2025-07-17T07:11:27.2249225Z Reduces the tensor data across all machines in a way that all get the final result. 2025-07-17T07:11:27.2249614Z 2025-07-17T07:11:27.2249855Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-07-17T07:11:27.2250202Z 2025-07-17T07:11:27.2250311Z Complex tensors are supported. 2025-07-17T07:11:27.2250514Z 2025-07-17T07:11:27.2250595Z Args: 2025-07-17T07:11:27.2250919Z tensor (Tensor): Input and output of the collective. The function 2025-07-17T07:11:27.2251351Z operates in-place. 2025-07-17T07:11:27.2251636Z op (optional): One of the values from 2025-07-17T07:11:27.2251981Z ``torch.distributed.ReduceOp`` 2025-07-17T07:11:27.2252403Z enum. Specifies an operation used for element-wise reductions. 2025-07-17T07:11:27.2252966Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:27.2253449Z the default process group will be used. 2025-07-17T07:11:27.2253880Z async_op (bool, optional): Whether this op should be an async op 2025-07-17T07:11:27.2254213Z 2025-07-17T07:11:27.2254332Z Returns: 2025-07-17T07:11:27.2254668Z Async work handle, if async_op is set to True. 2025-07-17T07:11:27.2255369Z None, if not async_op or if not part of the group 2025-07-17T07:11:27.2255662Z 2025-07-17T07:11:27.2255750Z Examples: 2025-07-17T07:11:27.2255988Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T07:11:27.2256339Z >>> # All tensors below are of torch.int64 type. 2025-07-17T07:11:27.2256696Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:27.2257050Z >>> device = torch.device(f"cuda:{rank}") 2025-07-17T07:11:27.2257515Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-07-17T07:11:27.2257950Z >>> tensor 2025-07-17T07:11:27.2258205Z tensor([1, 2], device='cuda:0') # Rank 0 2025-07-17T07:11:27.2258545Z tensor([3, 4], device='cuda:1') # Rank 1 2025-07-17T07:11:27.2258905Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-07-17T07:11:27.2259225Z >>> tensor 2025-07-17T07:11:27.2259475Z tensor([4, 6], device='cuda:0') # Rank 0 2025-07-17T07:11:27.2259813Z tensor([4, 6], device='cuda:1') # Rank 1 2025-07-17T07:11:27.2260035Z 2025-07-17T07:11:27.2260176Z >>> # All tensors below are of torch.cfloat type. 2025-07-17T07:11:27.2260545Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:27.2260873Z >>> tensor = torch.tensor( 2025-07-17T07:11:27.2261195Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-07-17T07:11:27.2261556Z ... ) + 2 * rank * (1 + 1j) 2025-07-17T07:11:27.2261825Z >>> tensor 2025-07-17T07:11:27.2262083Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-07-17T07:11:27.2262468Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-07-17T07:11:27.2262849Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-07-17T07:11:27.2263178Z >>> tensor 2025-07-17T07:11:27.2263531Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-07-17T07:11:27.2263916Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-07-17T07:11:27.2264157Z 2025-07-17T07:11:27.2264161Z 2025-07-17T07:11:27.2264488Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2264860Z 2025-07-17T07:11:27.2265492Z 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=3198. 2025-07-17T07:11:27.2266471Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2266848Z 2025-07-17T07:11:27.2267070Z Gathers picklable objects from the whole group in a single process. 2025-07-17T07:11:27.2267420Z 2025-07-17T07:11:27.2267655Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-07-17T07:11:27.2268163Z object must be picklable in order to be gathered. 2025-07-17T07:11:27.2268426Z 2025-07-17T07:11:27.2268520Z Args: 2025-07-17T07:11:27.2268757Z obj (Any): Input object. Must be picklable. 2025-07-17T07:11:27.2269205Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-07-17T07:11:27.2269727Z should be correctly sized as the size of the group for this 2025-07-17T07:11:27.2270333Z collective and will contain the output. Must be ``None`` on non-dst 2025-07-17T07:11:27.2270776Z ranks. (default is ``None``) 2025-07-17T07:11:27.2271305Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-07-17T07:11:27.2272091Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-07-17T07:11:27.2272636Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:27.2273184Z the default process group will be used. Default is ``None``. 2025-07-17T07:11:27.2273851Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-07-17T07:11:27.2274325Z 2025-07-17T07:11:27.2274412Z Returns: 2025-07-17T07:11:27.2274720Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-07-17T07:11:27.2275128Z output of the collective. 2025-07-17T07:11:27.2275319Z 2025-07-17T07:11:27.2275553Z .. note:: Note that this API differs slightly from the gather collective 2025-07-17T07:11:27.2276092Z since it does not provide an async_op handle and thus will be a blocking 2025-07-17T07:11:27.2276514Z call. 2025-07-17T07:11:27.2276631Z 2025-07-17T07:11:27.2276874Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-07-17T07:11:27.2277427Z of objects must be moved to the GPU device before communication takes 2025-07-17T07:11:27.2277906Z place. In this case, the device used is given by 2025-07-17T07:11:27.2278388Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-07-17T07:11:27.2278934Z ensure that this is set so that each rank has an individual GPU, via 2025-07-17T07:11:27.2279361Z ``torch.cuda.set_device()``. 2025-07-17T07:11:27.2279569Z 2025-07-17T07:11:27.2279657Z .. warning:: 2025-07-17T07:11:27.2280030Z Object collectives have a number of serious performance and scalability 2025-07-17T07:11:27.2280556Z limitations. See :ref:`object_collectives` for details. 2025-07-17T07:11:27.2280860Z 2025-07-17T07:11:27.2280948Z .. warning:: 2025-07-17T07:11:27.2281280Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-07-17T07:11:27.2281819Z known to be insecure. It is possible to construct malicious pickle data 2025-07-17T07:11:27.2282364Z which will execute arbitrary code during unpickling. Only call this 2025-07-17T07:11:27.2282810Z function with data you trust. 2025-07-17T07:11:27.2283009Z 2025-07-17T07:11:27.2283107Z .. warning:: 2025-07-17T07:11:27.2283439Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-07-17T07:11:27.2284130Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-07-17T07:11:27.2284652Z pickled. Please consider using :func:`gather` instead. 2025-07-17T07:11:27.2284932Z 2025-07-17T07:11:27.2285035Z Example:: 2025-07-17T07:11:27.2285354Z >>> # xdoctest: +SKIP("need process group init") 2025-07-17T07:11:27.2285791Z >>> # Note: Process group initialization omitted on each rank. 2025-07-17T07:11:27.2286209Z >>> import torch.distributed as dist 2025-07-17T07:11:27.2286531Z >>> # Assumes world_size of 3. 2025-07-17T07:11:27.2286911Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-07-17T07:11:27.2287331Z >>> output = [None for _ in gather_objects] 2025-07-17T07:11:27.2287654Z >>> dist.gather_object( 2025-07-17T07:11:27.2287957Z ... gather_objects[dist.get_rank()], 2025-07-17T07:11:27.2288322Z ... output if dist.get_rank() == 0 else None, 2025-07-17T07:11:27.2288644Z ... dst=0 2025-07-17T07:11:27.2288871Z ... ) 2025-07-17T07:11:27.2289087Z >>> # On rank 0 2025-07-17T07:11:27.2289319Z >>> output 2025-07-17T07:11:27.2289530Z ['foo', 12, {1: 2}] 2025-07-17T07:11:27.2289692Z 2025-07-17T07:11:27.2289942Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2290318Z 2025-07-17T07:11:27.2295330Z 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=3794. 2025-07-17T07:11:27.2296293Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2296674Z 2025-07-17T07:11:27.2296813Z Gathers tensors from the whole group in a list. 2025-07-17T07:11:27.2297075Z 2025-07-17T07:11:27.2297219Z Complex and uneven sized tensors are supported. 2025-07-17T07:11:27.2297468Z 2025-07-17T07:11:27.2297560Z Args: 2025-07-17T07:11:27.2297844Z tensor_list (list[Tensor]): Output list. It should contain 2025-07-17T07:11:27.2298362Z correctly-sized tensors to be used for output of the collective. 2025-07-17T07:11:27.2298829Z Uneven sized tensors are supported. 2025-07-17T07:11:27.2299258Z tensor (Tensor): Tensor to be broadcast from current process. 2025-07-17T07:11:27.2299796Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:27.2300285Z the default process group will be used. 2025-07-17T07:11:27.2300723Z async_op (bool, optional): Whether this op should be an async op 2025-07-17T07:11:27.2301035Z 2025-07-17T07:11:27.2301121Z Returns: 2025-07-17T07:11:27.2301388Z Async work handle, if async_op is set to True. 2025-07-17T07:11:27.2301793Z None, if not async_op or if not part of the group 2025-07-17T07:11:27.2302048Z 2025-07-17T07:11:27.2302148Z Examples: 2025-07-17T07:11:27.2302402Z >>> # xdoctest: +SKIP("need process group init") 2025-07-17T07:11:27.2302807Z >>> # All tensors below are of torch.int64 dtype. 2025-07-17T07:11:27.2303183Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:27.2303523Z >>> device = torch.device(f"cuda:{rank}") 2025-07-17T07:11:27.2303854Z >>> tensor_list = [ 2025-07-17T07:11:27.2304226Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-07-17T07:11:27.2304645Z ... ] 2025-07-17T07:11:27.2304848Z >>> tensor_list 2025-07-17T07:11:27.2305197Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-07-17T07:11:27.2305718Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-07-17T07:11:27.2306259Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-07-17T07:11:27.2306694Z >>> tensor 2025-07-17T07:11:27.2306942Z tensor([1, 2], device='cuda:0') # Rank 0 2025-07-17T07:11:27.2307270Z tensor([3, 4], device='cuda:1') # Rank 1 2025-07-17T07:11:27.2307610Z >>> dist.all_gather(tensor_list, tensor) 2025-07-17T07:11:27.2307923Z >>> tensor_list 2025-07-17T07:11:27.2308266Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-07-17T07:11:27.2308865Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-07-17T07:11:27.2309189Z 2025-07-17T07:11:27.2309331Z >>> # All tensors below are of torch.cfloat dtype. 2025-07-17T07:11:27.2309778Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:27.2310087Z >>> tensor_list = [ 2025-07-17T07:11:27.2310465Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-07-17T07:11:27.2310892Z ... ] 2025-07-17T07:11:27.2311094Z >>> tensor_list 2025-07-17T07:11:27.2311498Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-07-17T07:11:27.2312113Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-07-17T07:11:27.2312583Z >>> tensor = torch.tensor( 2025-07-17T07:11:27.2312909Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-07-17T07:11:27.2313274Z ... ) + 2 * rank * (1 + 1j) 2025-07-17T07:11:27.2313542Z >>> tensor 2025-07-17T07:11:27.2313805Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-07-17T07:11:27.2314191Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-07-17T07:11:27.2314565Z >>> dist.all_gather(tensor_list, tensor) 2025-07-17T07:11:27.2314868Z >>> tensor_list 2025-07-17T07:11:27.2315257Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-07-17T07:11:27.2315867Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-07-17T07:11:27.2316226Z 2025-07-17T07:11:27.2316230Z 2025-07-17T07:11:27.2316498Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2316868Z 2025-07-17T07:11:27.2352223Z 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=4500. 2025-07-17T07:11:27.2353202Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2353598Z 2025-07-17T07:11:27.2353872Z Split input tensor and then scatter the split list to all processes in a group. 2025-07-17T07:11:27.2354257Z 2025-07-17T07:11:27.2354519Z Later the received tensors are concatenated from all the processes in the group 2025-07-17T07:11:27.2355023Z and returned as a single output tensor. 2025-07-17T07:11:27.2355244Z 2025-07-17T07:11:27.2355370Z Complex tensors are supported. 2025-07-17T07:11:27.2355561Z 2025-07-17T07:11:27.2355641Z Args: 2025-07-17T07:11:27.2355926Z output (Tensor): Gathered concatenated output tensor. 2025-07-17T07:11:27.2356334Z input (Tensor): Input tensor to scatter. 2025-07-17T07:11:27.2356776Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-07-17T07:11:27.2357331Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-07-17T07:11:27.2357761Z equally by ``world_size``. 2025-07-17T07:11:27.2358180Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-07-17T07:11:27.2358704Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-07-17T07:11:27.2359128Z equally by ``world_size``. 2025-07-17T07:11:27.2359563Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:27.2360032Z the default process group will be used. 2025-07-17T07:11:27.2360469Z async_op (bool, optional): Whether this op should be an async op. 2025-07-17T07:11:27.2360783Z 2025-07-17T07:11:27.2360880Z Returns: 2025-07-17T07:11:27.2361129Z Async work handle, if async_op is set to True. 2025-07-17T07:11:27.2361529Z None, if not async_op or if not part of the group. 2025-07-17T07:11:27.2361797Z 2025-07-17T07:11:27.2361896Z .. warning:: 2025-07-17T07:11:27.2362200Z `all_to_all_single` is experimental and subject to change. 2025-07-17T07:11:27.2362483Z 2025-07-17T07:11:27.2362663Z Examples: 2025-07-17T07:11:27.2362991Z >>> # xdoctest: +SKIP("Undefined rank") 2025-07-17T07:11:27.2363375Z >>> input = torch.arange(4) + rank * 4 2025-07-17T07:11:27.2363850Z >>> input 2025-07-17T07:11:27.2364190Z tensor([0, 1, 2, 3]) # Rank 0 2025-07-17T07:11:27.2364694Z tensor([4, 5, 6, 7]) # Rank 1 2025-07-17T07:11:27.2364985Z tensor([8, 9, 10, 11]) # Rank 2 2025-07-17T07:11:27.2365284Z tensor([12, 13, 14, 15]) # Rank 3 2025-07-17T07:11:27.2365627Z >>> output = torch.empty([4], dtype=torch.int64) 2025-07-17T07:11:27.2366008Z >>> dist.all_to_all_single(output, input) 2025-07-17T07:11:27.2366319Z >>> output 2025-07-17T07:11:27.2366560Z tensor([0, 4, 8, 12]) # Rank 0 2025-07-17T07:11:27.2366860Z tensor([1, 5, 9, 13]) # Rank 1 2025-07-17T07:11:27.2367145Z tensor([2, 6, 10, 14]) # Rank 2 2025-07-17T07:11:27.2367440Z tensor([3, 7, 11, 15]) # Rank 3 2025-07-17T07:11:27.2367635Z 2025-07-17T07:11:27.2367810Z >>> # Essentially, it is similar to following operation: 2025-07-17T07:11:27.2368222Z >>> scatter_list = list(input.chunk(world_size)) 2025-07-17T07:11:27.2368615Z >>> gather_list = list(output.chunk(world_size)) 2025-07-17T07:11:27.2368969Z >>> for i in range(world_size): 2025-07-17T07:11:27.2369404Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-07-17T07:11:27.2369756Z 2025-07-17T07:11:27.2369873Z >>> # Another example with uneven split 2025-07-17T07:11:27.2370195Z >>> input 2025-07-17T07:11:27.2370483Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-07-17T07:11:27.2371006Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-07-17T07:11:27.2371450Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-07-17T07:11:27.2372052Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-07-17T07:11:27.2372437Z >>> input_splits 2025-07-17T07:11:27.2372714Z [2, 2, 1, 1] # Rank 0 2025-07-17T07:11:27.2373090Z [3, 2, 2, 2] # Rank 1 2025-07-17T07:11:27.2373462Z [2, 1, 1, 1] # Rank 2 2025-07-17T07:11:27.2373819Z [2, 2, 2, 1] # Rank 3 2025-07-17T07:11:27.2374165Z >>> output_splits 2025-07-17T07:11:27.2374451Z [2, 3, 2, 2] # Rank 0 2025-07-17T07:11:27.2374823Z [2, 2, 1, 2] # Rank 1 2025-07-17T07:11:27.2375246Z [1, 2, 1, 2] # Rank 2 2025-07-17T07:11:27.2375619Z [1, 2, 1, 1] # Rank 3 2025-07-17T07:11:27.2375959Z >>> output = ... 2025-07-17T07:11:27.2376313Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-07-17T07:11:27.2376743Z >>> output 2025-07-17T07:11:27.2377043Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-07-17T07:11:27.2377470Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-07-17T07:11:27.2377913Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-07-17T07:11:27.2378346Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-07-17T07:11:27.2378621Z 2025-07-17T07:11:27.2378626Z 2025-07-17T07:11:27.2378803Z >>> # Another example with tensors of torch.cfloat type. 2025-07-17T07:11:27.2379174Z >>> input = torch.tensor( 2025-07-17T07:11:27.2379501Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-07-17T07:11:27.2379853Z ... ) + 4 * rank * (1 + 1j) 2025-07-17T07:11:27.2380109Z >>> input 2025-07-17T07:11:27.2380414Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-07-17T07:11:27.2381007Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-07-17T07:11:27.2381479Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-07-17T07:11:27.2382021Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-07-17T07:11:27.2382475Z >>> output = torch.empty([4], dtype=torch.int64) 2025-07-17T07:11:27.2382855Z >>> dist.all_to_all_single(output, input) 2025-07-17T07:11:27.2383164Z >>> output 2025-07-17T07:11:27.2383475Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-07-17T07:11:27.2383955Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-07-17T07:11:27.2384433Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-07-17T07:11:27.2384892Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-07-17T07:11:27.2385201Z 2025-07-17T07:11:27.2385452Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2385828Z 2025-07-17T07:11:27.2386438Z 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=4642. 2025-07-17T07:11:27.2387384Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2387761Z 2025-07-17T07:11:27.2388142Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-07-17T07:11:27.2388632Z 2025-07-17T07:11:27.2388743Z Complex tensors are supported. 2025-07-17T07:11:27.2388951Z 2025-07-17T07:11:27.2389032Z Args: 2025-07-17T07:11:27.2389372Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-07-17T07:11:27.2389792Z per rank. 2025-07-17T07:11:27.2390167Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-07-17T07:11:27.2390754Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:27.2391242Z the default process group will be used. 2025-07-17T07:11:27.2391669Z async_op (bool, optional): Whether this op should be an async op. 2025-07-17T07:11:27.2391996Z 2025-07-17T07:11:27.2392082Z Returns: 2025-07-17T07:11:27.2392350Z Async work handle, if async_op is set to True. 2025-07-17T07:11:27.2392740Z None, if not async_op or if not part of the group. 2025-07-17T07:11:27.2393006Z 2025-07-17T07:11:27.2393101Z .. warning:: 2025-07-17T07:11:27.2393383Z `all_to_all` is experimental and subject to change. 2025-07-17T07:11:27.2393639Z 2025-07-17T07:11:27.2393736Z Examples: 2025-07-17T07:11:27.2393967Z >>> # xdoctest: +SKIP("Undefined rank") 2025-07-17T07:11:27.2394309Z >>> input = torch.arange(4) + rank * 4 2025-07-17T07:11:27.2394638Z >>> input = list(input.chunk(4)) 2025-07-17T07:11:27.2394920Z >>> input 2025-07-17T07:11:27.2395224Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-07-17T07:11:27.2395679Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-07-17T07:11:27.2396118Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-07-17T07:11:27.2396569Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-07-17T07:11:27.2397042Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-07-17T07:11:27.2397459Z >>> dist.all_to_all(output, input) 2025-07-17T07:11:27.2397750Z >>> output 2025-07-17T07:11:27.2398049Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-07-17T07:11:27.2398498Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-07-17T07:11:27.2398931Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-07-17T07:11:27.2399376Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-07-17T07:11:27.2399652Z 2025-07-17T07:11:27.2399827Z >>> # Essentially, it is similar to following operation: 2025-07-17T07:11:27.2400276Z >>> scatter_list = input 2025-07-17T07:11:27.2400544Z >>> gather_list = output 2025-07-17T07:11:27.2400833Z >>> for i in range(world_size): 2025-07-17T07:11:27.2401264Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-07-17T07:11:27.2401658Z 2025-07-17T07:11:27.2401742Z >>> input 2025-07-17T07:11:27.2402032Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-07-17T07:11:27.2402468Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-07-17T07:11:27.2402894Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-07-17T07:11:27.2403334Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-07-17T07:11:27.2403710Z >>> input_splits 2025-07-17T07:11:27.2403995Z [2, 2, 1, 1] # Rank 0 2025-07-17T07:11:27.2404357Z [3, 2, 2, 2] # Rank 1 2025-07-17T07:11:27.2404728Z [2, 1, 1, 1] # Rank 2 2025-07-17T07:11:27.2405099Z [2, 2, 2, 1] # Rank 3 2025-07-17T07:11:27.2405429Z >>> output_splits 2025-07-17T07:11:27.2405711Z [2, 3, 2, 2] # Rank 0 2025-07-17T07:11:27.2406077Z [2, 2, 1, 2] # Rank 1 2025-07-17T07:11:27.2406440Z [1, 2, 1, 2] # Rank 2 2025-07-17T07:11:27.2406796Z [1, 2, 1, 1] # Rank 3 2025-07-17T07:11:27.2407171Z >>> input = list(input.split(input_splits)) 2025-07-17T07:11:27.2407517Z >>> input 2025-07-17T07:11:27.2407960Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-07-17T07:11:27.2408544Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-07-17T07:11:27.2409093Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-07-17T07:11:27.2409634Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-07-17T07:11:27.2410057Z >>> output = ... 2025-07-17T07:11:27.2410332Z >>> dist.all_to_all(output, input) 2025-07-17T07:11:27.2410640Z >>> output 2025-07-17T07:11:27.2410969Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-07-17T07:11:27.2411506Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-07-17T07:11:27.2412043Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-07-17T07:11:27.2412583Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-07-17T07:11:27.2412910Z 2025-07-17T07:11:27.2413072Z >>> # Another example with tensors of torch.cfloat type. 2025-07-17T07:11:27.2413464Z >>> input = torch.tensor( 2025-07-17T07:11:27.2413797Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-07-17T07:11:27.2414141Z ... ) + 4 * rank * (1 + 1j) 2025-07-17T07:11:27.2425763Z >>> input = list(input.chunk(4)) 2025-07-17T07:11:27.2426093Z >>> input 2025-07-17T07:11:27.2426462Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-07-17T07:11:27.2427026Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-07-17T07:11:27.2427580Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-07-17T07:11:27.2428150Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-07-17T07:11:27.2428689Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-07-17T07:11:27.2429100Z >>> dist.all_to_all(output, input) 2025-07-17T07:11:27.2429412Z >>> output 2025-07-17T07:11:27.2429885Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-07-17T07:11:27.2430442Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-07-17T07:11:27.2431042Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-07-17T07:11:27.2431596Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-07-17T07:11:27.2431931Z 2025-07-17T07:11:27.2431936Z 2025-07-17T07:11:27.2432206Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2432579Z 2025-07-17T07:11:27.2433124Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2025-07-17T07:11:27.2433996Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2434375Z 2025-07-17T07:11:27.2434510Z Module ``torch.distributed.launch``. 2025-07-17T07:11:27.2434732Z 2025-07-17T07:11:27.2434982Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-07-17T07:11:27.2435504Z training processes on each of the training nodes. 2025-07-17T07:11:27.2435780Z 2025-07-17T07:11:27.2435882Z .. warning:: 2025-07-17T07:11:27.2436011Z 2025-07-17T07:11:27.2436283Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-07-17T07:11:27.2436662Z 2025-07-17T07:11:27.2436904Z The utility can be used for single-node distributed training, in which one or 2025-07-17T07:11:27.2437500Z more processes per node will be spawned. The utility can be used for either 2025-07-17T07:11:27.2438079Z CPU training or GPU training. If the utility is used for GPU training, 2025-07-17T07:11:27.2438666Z each distributed process will be operating on a single GPU. This can achieve 2025-07-17T07:11:27.2439255Z well-improved single-node training performance. It can also be used in 2025-07-17T07:11:27.2439876Z multi-node distributed training, by spawning up multiple processes on each node 2025-07-17T07:11:27.2440506Z for well-improved multi-node distributed training performance as well. 2025-07-17T07:11:27.2441082Z This will especially be beneficial for systems with multiple Infiniband 2025-07-17T07:11:27.2441696Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-07-17T07:11:27.2442193Z aggregated communication bandwidth. 2025-07-17T07:11:27.2442403Z 2025-07-17T07:11:27.2442650Z In both cases of single-node distributed training or multi-node distributed 2025-07-17T07:11:27.2443230Z training, this utility will launch the given number of processes per node 2025-07-17T07:11:27.2443820Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-07-17T07:11:27.2444391Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-07-17T07:11:27.2444921Z and each process will be operating on a single GPU from *GPU 0 to 2025-07-17T07:11:27.2445359Z GPU (nproc_per_node - 1)*. 2025-07-17T07:11:27.2445541Z 2025-07-17T07:11:27.2445656Z **How to use this module:** 2025-07-17T07:11:27.2445834Z 2025-07-17T07:11:27.2445998Z 1. Single-Node multi-process distributed training 2025-07-17T07:11:27.2446261Z 2025-07-17T07:11:27.2446348Z :: 2025-07-17T07:11:27.2446475Z 2025-07-17T07:11:27.2446717Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T07:11:27.2447272Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-07-17T07:11:27.2447697Z arguments of your training script) 2025-07-17T07:11:27.2447945Z 2025-07-17T07:11:27.2448157Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-07-17T07:11:27.2448494Z 2025-07-17T07:11:27.2448499Z 2025-07-17T07:11:27.2448640Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-07-17T07:11:27.2448893Z 2025-07-17T07:11:27.2448988Z :: 2025-07-17T07:11:27.2449094Z 2025-07-17T07:11:27.2449333Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T07:11:27.2449921Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-07-17T07:11:27.2450414Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-07-17T07:11:27.2450959Z and all other arguments of your training script) 2025-07-17T07:11:27.2451229Z 2025-07-17T07:11:27.2451316Z Node 2: 2025-07-17T07:11:27.2451446Z 2025-07-17T07:11:27.2451531Z :: 2025-07-17T07:11:27.2451637Z 2025-07-17T07:11:27.2451883Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T07:11:27.2452387Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-07-17T07:11:27.2452874Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-07-17T07:11:27.2453362Z and all other arguments of your training script) 2025-07-17T07:11:27.2453631Z 2025-07-17T07:11:27.2453808Z 3. To look up what optional arguments this module offers: 2025-07-17T07:11:27.2454084Z 2025-07-17T07:11:27.2454168Z :: 2025-07-17T07:11:27.2454293Z 2025-07-17T07:11:27.2454432Z python -m torch.distributed.launch --help 2025-07-17T07:11:27.2454689Z 2025-07-17T07:11:27.2454693Z 2025-07-17T07:11:27.2454791Z **Important Notices:** 2025-07-17T07:11:27.2454952Z 2025-07-17T07:11:27.2455154Z 1. This utility and multi-process distributed (single-node or 2025-07-17T07:11:27.2455793Z multi-node) GPU training currently only achieves the best performance using 2025-07-17T07:11:27.2456421Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-07-17T07:11:27.2456896Z use for GPU training. 2025-07-17T07:11:27.2457054Z 2025-07-17T07:11:27.2457269Z 2. In your training program, you must parse the command-line argument: 2025-07-17T07:11:27.2457847Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-07-17T07:11:27.2458433Z If your training program uses GPUs, you should ensure that your code only 2025-07-17T07:11:27.2458986Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-07-17T07:11:27.2459294Z 2025-07-17T07:11:27.2459405Z Parsing the local_rank argument 2025-07-17T07:11:27.2459609Z 2025-07-17T07:11:27.2459692Z :: 2025-07-17T07:11:27.2459801Z 2025-07-17T07:11:27.2459912Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.2460180Z >>> import argparse 2025-07-17T07:11:27.2460454Z >>> parser = argparse.ArgumentParser() 2025-07-17T07:11:27.2460884Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-07-17T07:11:27.2461308Z >>> args = parser.parse_args() 2025-07-17T07:11:27.2461502Z 2025-07-17T07:11:27.2461620Z Set your device to local rank using either 2025-07-17T07:11:27.2461867Z 2025-07-17T07:11:27.2461949Z :: 2025-07-17T07:11:27.2462054Z 2025-07-17T07:11:27.2462267Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-07-17T07:11:27.2462581Z 2025-07-17T07:11:27.2462674Z or 2025-07-17T07:11:27.2462780Z 2025-07-17T07:11:27.2462859Z :: 2025-07-17T07:11:27.2462981Z 2025-07-17T07:11:27.2463112Z >>> with torch.cuda.device(args.local_rank): 2025-07-17T07:11:27.2463453Z >>> # your code to run 2025-07-17T07:11:27.2463709Z >>> ... 2025-07-17T07:11:27.2463847Z 2025-07-17T07:11:27.2463948Z .. versionchanged:: 2.0.0 2025-07-17T07:11:27.2464120Z 2025-07-17T07:11:27.2464380Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-07-17T07:11:27.2464989Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-07-17T07:11:27.2465481Z previously used underscored ``--local_rank``. 2025-07-17T07:11:27.2465741Z 2025-07-17T07:11:27.2465979Z For backward compatibility, it may be necessary for users to handle both 2025-07-17T07:11:27.2466614Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-07-17T07:11:27.2467208Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-07-17T07:11:27.2467802Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-07-17T07:11:27.2468490Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-07-17T07:11:27.2468999Z including ``"--local-rank"`` should be sufficient. 2025-07-17T07:11:27.2469259Z 2025-07-17T07:11:27.2469550Z 3. In your training program, you are supposed to call the following function 2025-07-17T07:11:27.2470152Z at the beginning to start the distributed backend. It is strongly recommended 2025-07-17T07:11:27.2470749Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-07-17T07:11:27.2471410Z but ``env://`` is the one that is officially supported by this module. 2025-07-17T07:11:27.2471717Z 2025-07-17T07:11:27.2471943Z :: 2025-07-17T07:11:27.2472069Z 2025-07-17T07:11:27.2472281Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-07-17T07:11:27.2472748Z >>> init_method='env://') 2025-07-17T07:11:27.2472988Z 2025-07-17T07:11:27.2473234Z 4. In your training program, you can either use regular distributed functions 2025-07-17T07:11:27.2473842Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-07-17T07:11:27.2474420Z training program uses GPUs for training and you would like to use 2025-07-17T07:11:27.2474941Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-07-17T07:11:27.2475336Z here is how to configure it. 2025-07-17T07:11:27.2475530Z 2025-07-17T07:11:27.2475612Z :: 2025-07-17T07:11:27.2475718Z 2025-07-17T07:11:27.2475922Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-07-17T07:11:27.2476357Z >>> device_ids=[args.local_rank], 2025-07-17T07:11:27.2476762Z >>> output_device=args.local_rank) 2025-07-17T07:11:27.2477036Z 2025-07-17T07:11:27.2477278Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-07-17T07:11:27.2477878Z that your code will be operating on. This is generally the local rank of the 2025-07-17T07:11:27.2478459Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-07-17T07:11:27.2479044Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-07-17T07:11:27.2479468Z utility 2025-07-17T07:11:27.2479583Z 2025-07-17T07:11:27.2479840Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-07-17T07:11:27.2480412Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-07-17T07:11:27.2480973Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-07-17T07:11:27.2481515Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-07-17T07:11:27.2481991Z will not pass ``--local-rank`` when you specify this flag. 2025-07-17T07:11:27.2482286Z 2025-07-17T07:11:27.2482376Z .. warning:: 2025-07-17T07:11:27.2482502Z 2025-07-17T07:11:27.2482715Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-07-17T07:11:27.2483235Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-07-17T07:11:27.2483650Z write to a networked filesystem. See 2025-07-17T07:11:27.2484103Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-07-17T07:11:27.2484604Z how things can go wrong if you don't do this correctly. 2025-07-17T07:11:27.2484876Z 2025-07-17T07:11:27.2484879Z 2025-07-17T07:11:27.2484883Z 2025-07-17T07:11:27.2484887Z 2025-07-17T07:11:27.2485147Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2485517Z 2025-07-17T07:11:27.2836166Z 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. 2025-07-17T07:11:27.2837227Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2837618Z 2025-07-17T07:11:27.2838001Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-07-17T07:11:27.2838514Z Needs to be called on all ranks in an SPMD fashion. 2025-07-17T07:11:27.2838771Z 2025-07-17T07:11:27.2838856Z Args: 2025-07-17T07:11:27.2839346Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-07-17T07:11:27.2839934Z of shards that represent the local shards on this rank. 2025-07-17T07:11:27.2840458Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-07-17T07:11:27.2840917Z shape of the overall sharded tensor. 2025-07-17T07:11:27.2841153Z 2025-07-17T07:11:27.2841243Z Keyword args: 2025-07-17T07:11:27.2841651Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:27.2842160Z the default process group will be used. 2025-07-17T07:11:27.2842580Z init_rrefs (bool, optional): Whether or not to initialize 2025-07-17T07:11:27.2843088Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-07-17T07:11:27.2843617Z Need to initialize the RPC Framework if specified as ``True``. 2025-07-17T07:11:27.2844020Z Default: ``False``. 2025-07-17T07:11:27.2844207Z 2025-07-17T07:11:27.2844294Z Returns: 2025-07-17T07:11:27.2844572Z A :class:`ShardedTensor` object handle on this rank 2025-07-17T07:11:27.2844834Z 2025-07-17T07:11:27.2844837Z 2025-07-17T07:11:27.2844926Z Examples: 2025-07-17T07:11:27.2845313Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-07-17T07:11:27.2845881Z each shard have a (5, 5) local tensor, we can do it like below: 2025-07-17T07:11:27.2846174Z 2025-07-17T07:11:27.2846274Z on rank 0: 2025-07-17T07:11:27.2846523Z >>> # xdoctest: +SKIP("not distributed") 2025-07-17T07:11:27.2846883Z >>> local_shard_metadata = ShardMetadata( 2025-07-17T07:11:27.2847225Z >>> shard_offsets=[0, 0], 2025-07-17T07:11:27.2847515Z >>> shard_lengths=[5, 5], 2025-07-17T07:11:27.2847831Z >>> placement="rank:0/cuda:0" 2025-07-17T07:11:27.2848143Z >>> ) 2025-07-17T07:11:27.2848451Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-07-17T07:11:27.2848973Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-07-17T07:11:27.2849293Z 2025-07-17T07:11:27.2849378Z on rank 1: 2025-07-17T07:11:27.2849633Z >>> # xdoctest: +SKIP("not distributed") 2025-07-17T07:11:27.2849970Z >>> local_shard_metadata = ShardMetadata( 2025-07-17T07:11:27.2850306Z >>> shard_offsets=[5, 0], 2025-07-17T07:11:27.2850602Z >>> shard_lengths=[5, 5], 2025-07-17T07:11:27.2850890Z >>> placement="rank:1/cuda:1" 2025-07-17T07:11:27.2851183Z >>> ) 2025-07-17T07:11:27.2851496Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-07-17T07:11:27.2851993Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-07-17T07:11:27.2852314Z 2025-07-17T07:11:27.2852570Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2852950Z 2025-07-17T07:11:27.2935245Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=835. 2025-07-17T07:11:27.2936372Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2936754Z 2025-07-17T07:11:27.2937027Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-07-17T07:11:27.2937509Z size and sharding spec on each rank. 2025-07-17T07:11:27.2937734Z 2025-07-17T07:11:27.2937815Z Args: 2025-07-17T07:11:27.2938163Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-07-17T07:11:27.2938774Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-07-17T07:11:27.2939314Z The specification describing how to shard the Tensor. 2025-07-17T07:11:27.2939900Z global_size (Sequence[int]): Size of the sharded tensor. 2025-07-17T07:11:27.2940441Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-07-17T07:11:27.2940903Z Default: None 2025-07-17T07:11:27.2941309Z init_rrefs (bool, optional): Whether or not to initialize 2025-07-17T07:11:27.2941821Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-07-17T07:11:27.2942442Z Need to initialize the RPC Framework if specified as ``True``. 2025-07-17T07:11:27.2942861Z Default: ``False``. 2025-07-17T07:11:27.2943048Z 2025-07-17T07:11:27.2943132Z Returns: 2025-07-17T07:11:27.2943503Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-07-17T07:11:27.2943975Z tensor stored in the current rank. 2025-07-17T07:11:27.2944208Z 2025-07-17T07:11:27.2944294Z Examples: 2025-07-17T07:11:27.2944544Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.2944852Z >>> # All tensors below are of torch.int64 type. 2025-07-17T07:11:27.2945214Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:27.2945616Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-07-17T07:11:27.2946108Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-07-17T07:11:27.2946508Z >>> local_tensor 2025-07-17T07:11:27.2946752Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-07-17T07:11:27.2947047Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-07-17T07:11:27.2947338Z >>> sharding_dim = 0 2025-07-17T07:11:27.2947616Z >>> sharding_spec = ChunkShardingSpec( 2025-07-17T07:11:27.2947947Z dim=sharding_dim, 2025-07-17T07:11:27.2948227Z placements=[ 2025-07-17T07:11:27.2948479Z "rank:0/cuda:0", 2025-07-17T07:11:27.2948761Z "rank:1/cuda:1", 2025-07-17T07:11:27.2949031Z ], 2025-07-17T07:11:27.2949238Z ) 2025-07-17T07:11:27.2949491Z >>> st = ShardedTensor._init_from_local_tensor( 2025-07-17T07:11:27.2949865Z ... local_tensor, sharding_spec, [2, 4] 2025-07-17T07:11:27.2950178Z ... ) 2025-07-17T07:11:27.2950383Z >>> st 2025-07-17T07:11:27.2950601Z ShardedTensor( 2025-07-17T07:11:27.2950847Z ShardedTensorMetadata( 2025-07-17T07:11:27.2951137Z shards_metadata=[ 2025-07-17T07:11:27.2951596Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-07-17T07:11:27.2952255Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-07-17T07:11:27.2952718Z ], 2025-07-17T07:11:27.2952961Z size=torch.Size([2, 4]) 2025-07-17T07:11:27.2953253Z ) 2025-07-17T07:11:27.2953454Z >>> st.local_tensor() 2025-07-17T07:11:27.2953721Z tensor([1, 2, 3, 4]) # Rank 0 2025-07-17T07:11:27.2954009Z tensor([3, 4, 5, 6]) # Rank 1 2025-07-17T07:11:27.2954194Z 2025-07-17T07:11:27.2954462Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-07-17T07:11:27.2955103Z rank validations, and we only validate the local shard on the current rank. 2025-07-17T07:11:27.2955686Z We fully rely on the user to ensure local tensor is sharded based on the 2025-07-17T07:11:27.2956119Z sharding spec. 2025-07-17T07:11:27.2956281Z 2025-07-17T07:11:27.2956535Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2956912Z 2025-07-17T07:11:27.2957600Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1076. 2025-07-17T07:11:27.2958667Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.2959045Z 2025-07-17T07:11:27.2959307Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-07-17T07:11:27.2959764Z single local shard. 2025-07-17T07:11:27.2959922Z 2025-07-17T07:11:27.2960146Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-07-17T07:11:27.2960876Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-07-17T07:11:27.2961348Z we swap local shards directly. 2025-07-17T07:11:27.2961862Z For more generic cases, we merge different shards across different ranks and split 2025-07-17T07:11:27.2962506Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-07-17T07:11:27.2962879Z 2025-07-17T07:11:27.2962974Z Args: 2025-07-17T07:11:27.2963372Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-07-17T07:11:27.2963953Z specification describing how the tensor is sharded. 2025-07-17T07:11:27.2964230Z 2025-07-17T07:11:27.2964332Z Returns: 2025-07-17T07:11:27.2964649Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-07-17T07:11:27.2964981Z 2025-07-17T07:11:27.2965070Z Examples: 2025-07-17T07:11:27.2965291Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.2965582Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:27.2965982Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-07-17T07:11:27.2966397Z >>> tensor = torch.stack([tensor, tensor]) 2025-07-17T07:11:27.2966715Z >>> tensor 2025-07-17T07:11:27.2966958Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-07-17T07:11:27.2967306Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-07-17T07:11:27.2967652Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-07-17T07:11:27.2967999Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-07-17T07:11:27.2968321Z >>> sharding_dim = 0 2025-07-17T07:11:27.2968603Z >>> spec = ChunkShardingSpec( 2025-07-17T07:11:27.2968913Z dim=sharding_dim, 2025-07-17T07:11:27.2969184Z placements=[ 2025-07-17T07:11:27.2969449Z "rank:0/cuda:0", 2025-07-17T07:11:27.2969738Z "rank:1/cuda:1", 2025-07-17T07:11:27.2970004Z "rank:2/cuda:2", 2025-07-17T07:11:27.2970283Z "rank:3/cuda:3", 2025-07-17T07:11:27.2970551Z ], 2025-07-17T07:11:27.2970754Z ) 2025-07-17T07:11:27.2970981Z >>> current_offsets = [0] * 2 2025-07-17T07:11:27.2971289Z >>> current_offsets[0] = rank * 2 2025-07-17T07:11:27.2971605Z >>> shard_metadata = ShardMetadata( 2025-07-17T07:11:27.2972154Z shard_offsets=copy.deepcopy(current_offsets), 2025-07-17T07:11:27.2972537Z shard_sizes=tensor.size(), 2025-07-17T07:11:27.2972884Z placement=spec.placements[rank], 2025-07-17T07:11:27.2973192Z ) 2025-07-17T07:11:27.2973412Z >>> local_shards = [ 2025-07-17T07:11:27.2973679Z Shard( 2025-07-17T07:11:27.2973997Z tensor=tensor, 2025-07-17T07:11:27.2974298Z metadata=shard_metadata, 2025-07-17T07:11:27.2974601Z ) 2025-07-17T07:11:27.2974804Z ] 2025-07-17T07:11:27.2975230Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-07-17T07:11:27.2975688Z >>> sharding_dim = 1 2025-07-17T07:11:27.2975973Z >>> resharding_spec = ChunkShardingSpec( 2025-07-17T07:11:27.2976312Z dim=sharding_dim, 2025-07-17T07:11:27.2976593Z placements=[ 2025-07-17T07:11:27.2976846Z "rank:0/cuda:0", 2025-07-17T07:11:27.2977135Z "rank:1/cuda:1", 2025-07-17T07:11:27.2977414Z "rank:2/cuda:2", 2025-07-17T07:11:27.2977686Z "rank:3/cuda:3", 2025-07-17T07:11:27.2977943Z ], 2025-07-17T07:11:27.2978159Z ) 2025-07-17T07:11:27.2978385Z >>> st.reshard(resharding_spec) 2025-07-17T07:11:27.2978697Z >>> tensor = st.local_shards()[0].tensor 2025-07-17T07:11:27.2979005Z >>> tensor 2025-07-17T07:11:27.2979281Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-07-17T07:11:27.2979662Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-07-17T07:11:27.2980051Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-07-17T07:11:27.2980570Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-07-17T07:11:27.2980824Z 2025-07-17T07:11:27.2981091Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.2981461Z 2025-07-17T07:11:27.3069345Z 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. 2025-07-17T07:11:27.3070373Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.3070768Z 2025-07-17T07:11:27.3070991Z Representation of a sharding plan, describes how to shard a module 2025-07-17T07:11:27.3071599Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-07-17T07:11:27.3072391Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-07-17T07:11:27.3073051Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-07-17T07:11:27.3073439Z 2025-07-17T07:11:27.3073521Z Args: 2025-07-17T07:11:27.3073917Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-07-17T07:11:27.3074555Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-07-17T07:11:27.3075125Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-07-17T07:11:27.3075781Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-07-17T07:11:27.3076288Z a parameter to a `ShardingSpec`. 2025-07-17T07:11:27.3076772Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-07-17T07:11:27.3077262Z to a `Sharder` object. 2025-07-17T07:11:27.3077808Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-07-17T07:11:27.3078515Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-07-17T07:11:27.3079143Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-07-17T07:11:27.3079607Z Default: `None` 2025-07-17T07:11:27.3080029Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-07-17T07:11:27.3080645Z a module's sharded output to be returned as a Tensor from its local shards to 2025-07-17T07:11:27.3081266Z ensure further processing in a data parallel fashion. ("" in list means the 2025-07-17T07:11:27.3081735Z root module). 2025-07-17T07:11:27.3081971Z Default: None 2025-07-17T07:11:27.3082217Z Example: 2025-07-17T07:11:27.3082629Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-07-17T07:11:27.3083311Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-07-17T07:11:27.3083729Z 2025-07-17T07:11:27.3083901Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-07-17T07:11:27.3084303Z >>> class MyModule(nn.Module): 2025-07-17T07:11:27.3084619Z >>> def __init__(self) -> None: 2025-07-17T07:11:27.3084922Z >>> super().__init__() 2025-07-17T07:11:27.3085221Z >>> self.fc1 = nn.Linear() 2025-07-17T07:11:27.3085532Z >>> self.gelu = nn.GELU() 2025-07-17T07:11:27.3085824Z >>> self.fc2 = nn.Linear() 2025-07-17T07:11:27.3086132Z >>> self.relu = nn.Linear() 2025-07-17T07:11:27.3086423Z >>> 2025-07-17T07:11:27.3086632Z >>> def forward(self, input): 2025-07-17T07:11:27.3087012Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-07-17T07:11:27.3087311Z 2025-07-17T07:11:27.3087316Z 2025-07-17T07:11:27.3087444Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-07-17T07:11:27.3087799Z >>> sharding_plan = ShardingPlan( 2025-07-17T07:11:27.3088090Z >>> plan={ 2025-07-17T07:11:27.3088334Z >>> "fc1.weight": spec1, 2025-07-17T07:11:27.3088637Z >>> "fc2.weight": spec2 2025-07-17T07:11:27.3089055Z >>> }, 2025-07-17T07:11:27.3089284Z >>> output_plan={ 2025-07-17T07:11:27.3089552Z >>> "fc2": output_spec 2025-07-17T07:11:27.3089814Z >>> }, 2025-07-17T07:11:27.3090118Z >>> return_local_tensor=["fc2"] 2025-07-17T07:11:27.3090417Z >>> ) 2025-07-17T07:11:27.3090532Z 2025-07-17T07:11:27.3090784Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.3091167Z 2025-07-17T07:11:27.3747933Z 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. 2025-07-17T07:11:27.3749072Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.3749469Z 2025-07-17T07:11:27.3749579Z Run post-localSGD algorithm. 2025-07-17T07:11:27.3749766Z 2025-07-17T07:11:27.3750016Z This DDP communication hook is used for running post-localSGD algorithm, 2025-07-17T07:11:27.3750532Z by combining with a model averaging component (e.g., 2025-07-17T07:11:27.3751146Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-07-17T07:11:27.3751723Z that runs after the optimizer step. 2025-07-17T07:11:27.3751940Z 2025-07-17T07:11:27.3752024Z Args: 2025-07-17T07:11:27.3752370Z state (PostLocalSGDState): State information to run post-localSGD. 2025-07-17T07:11:27.3752993Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-07-17T07:11:27.3753820Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-07-17T07:11:27.3754604Z Note that since DDP comm hook only supports single process single device mode, 2025-07-17T07:11:27.3755136Z only exactly one tensor is stored in this bucket. 2025-07-17T07:11:27.3755395Z 2025-07-17T07:11:27.3755484Z Returns: 2025-07-17T07:11:27.3755841Z Future handler of the communication, which updates the gradients in place. 2025-07-17T07:11:27.3756206Z 2025-07-17T07:11:27.3756300Z Example:: 2025-07-17T07:11:27.3756510Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.3756938Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-07-17T07:11:27.3757422Z start_localSGD_iter=10) 2025-07-17T07:11:27.3757833Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T07:11:27.3758457Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-07-17T07:11:27.3759265Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-07-17T07:11:27.3759733Z 2025-07-17T07:11:27.3759984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.3760361Z 2025-07-17T07:11:27.3793057Z 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. 2025-07-17T07:11:27.3794170Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.3794548Z 2025-07-17T07:11:27.3794668Z Implement PowerSGD algorithm. 2025-07-17T07:11:27.3794860Z 2025-07-17T07:11:27.3795093Z This DDP communication hook implements PowerSGD gradient compression 2025-07-17T07:11:27.3795666Z algorithm described in the `paper `_. 2025-07-17T07:11:27.3796260Z Once gradient tensors are aggregated across all workers, this hook applies 2025-07-17T07:11:27.3796721Z compression as follows: 2025-07-17T07:11:27.3796887Z 2025-07-17T07:11:27.3797330Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-07-17T07:11:27.3797900Z 2025-07-17T07:11:27.3798323Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-07-17T07:11:27.3798998Z 2025-07-17T07:11:27.3799407Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-07-17T07:11:27.3799934Z 2025-07-17T07:11:27.3800120Z 2. Handles uncompressed tensors: 2025-07-17T07:11:27.3800318Z 2025-07-17T07:11:27.3800841Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-07-17T07:11:27.3801477Z 2025-07-17T07:11:27.3801821Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-07-17T07:11:27.3802292Z 2025-07-17T07:11:27.3802522Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-07-17T07:11:27.3802880Z 2025-07-17T07:11:27.3803115Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-07-17T07:11:27.3803798Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-07-17T07:11:27.3804219Z 2025-07-17T07:11:27.3804361Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-07-17T07:11:27.3804622Z 2025-07-17T07:11:27.3804724Z 3.3. Allreduces Ps as a batch; 2025-07-17T07:11:27.3804933Z 2025-07-17T07:11:27.3805043Z 3.4. Orthogonalizes each P in Ps; 2025-07-17T07:11:27.3805252Z 2025-07-17T07:11:27.3805450Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-07-17T07:11:27.3805758Z 2025-07-17T07:11:27.3805856Z 3.6. Allreduces Qs as a batch; 2025-07-17T07:11:27.3806060Z 2025-07-17T07:11:27.3806351Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-07-17T07:11:27.3806771Z 2025-07-17T07:11:27.3807178Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-07-17T07:11:27.3807976Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-07-17T07:11:27.3808801Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-07-17T07:11:27.3809363Z 2025-07-17T07:11:27.3809444Z Args: 2025-07-17T07:11:27.3809998Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-07-17T07:11:27.3810914Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-07-17T07:11:27.3811489Z and ``min_compression_rate``. 2025-07-17T07:11:27.3812113Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-07-17T07:11:27.3812890Z Note that since DDP comm hook only supports single process single device mode, 2025-07-17T07:11:27.3813395Z only exactly one tensor is stored in this bucket. 2025-07-17T07:11:27.3813662Z 2025-07-17T07:11:27.3813747Z Returns: 2025-07-17T07:11:27.3814101Z Future handler of the communication, which updates the gradients in place. 2025-07-17T07:11:27.3814456Z 2025-07-17T07:11:27.3814557Z Example:: 2025-07-17T07:11:27.3814764Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.3815270Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-07-17T07:11:27.3815832Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-07-17T07:11:27.3816263Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-07-17T07:11:27.3816545Z 2025-07-17T07:11:27.3816793Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.3817169Z 2025-07-17T07:11:27.3829912Z 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. 2025-07-17T07:11:27.3831046Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.3831519Z 2025-07-17T07:11:27.3831718Z Averages parameters periodically after the warm-up stage. 2025-07-17T07:11:27.3832010Z 2025-07-17T07:11:27.3832266Z This can be used for running `post-local SGD `_, 2025-07-17T07:11:27.3832882Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-07-17T07:11:27.3833426Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-07-17T07:11:27.3833772Z 2025-07-17T07:11:27.3833864Z Args: 2025-07-17T07:11:27.3834133Z period (int): The number of steps per model averaging. 2025-07-17T07:11:27.3834680Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-07-17T07:11:27.3835201Z Otherwise, only DDP needs to be used. 2025-07-17T07:11:27.3835633Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-07-17T07:11:27.3836074Z model averaging is skipped. 2025-07-17T07:11:27.3836514Z process_group: The process group to be used for all-reduce. 2025-07-17T07:11:27.3836950Z If ``None``, the default process group, which 2025-07-17T07:11:27.3837384Z is created by :func:`torch.distributed.init_process_group`, 2025-07-17T07:11:27.3837822Z will be used. (default: ``None``) 2025-07-17T07:11:27.3838048Z 2025-07-17T07:11:27.3838149Z Example:: 2025-07-17T07:11:27.3838264Z 2025-07-17T07:11:27.3838396Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:27.3838707Z >>> import torch 2025-07-17T07:11:27.3838966Z >>> import torch.distributed as dist 2025-07-17T07:11:27.3839501Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-07-17T07:11:27.3840194Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-07-17T07:11:27.3840684Z >>> import torch.nn as nn 2025-07-17T07:11:27.3840944Z >>> 2025-07-17T07:11:27.3841231Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-07-17T07:11:27.3841625Z >>> torch.cuda.set_device(rank) 2025-07-17T07:11:27.3841951Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-07-17T07:11:27.3842330Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T07:11:27.3842737Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T07:11:27.3843066Z >>> ) 2025-07-17T07:11:27.3843323Z >>> # Register a post-localSGD communication hook. 2025-07-17T07:11:27.3843860Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-07-17T07:11:27.3844585Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T07:11:27.3844955Z >>> 2025-07-17T07:11:27.3845330Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-07-17T07:11:27.3845870Z >>> # After 100 steps, run model averaging every 4 steps. 2025-07-17T07:11:27.3846452Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T07:11:27.3847140Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-07-17T07:11:27.3847600Z >>> for step in range(0, 200): 2025-07-17T07:11:27.3847903Z >>> optimizer.zero_grad() 2025-07-17T07:11:27.3848200Z >>> loss = loss_fn(output, labels) 2025-07-17T07:11:27.3848499Z >>> loss.backward() 2025-07-17T07:11:27.3848758Z >>> optimizer.step() 2025-07-17T07:11:27.3849130Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-07-17T07:11:27.3849634Z >>> # inter-node communication only occurs every 4 iterations after 2025-07-17T07:11:27.3850085Z >>> # the initial ``warmup_steps`` period. 2025-07-17T07:11:27.3850476Z >>> averager.average_parameters(model.parameters()) 2025-07-17T07:11:27.3850749Z 2025-07-17T07:11:27.3851009Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.3851452Z 2025-07-17T07:11:27.3852343Z 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. 2025-07-17T07:11:27.3853618Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.3854001Z 2025-07-17T07:11:27.3854341Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-07-17T07:11:27.3854807Z 2025-07-17T07:11:27.3855112Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-07-17T07:11:27.3855815Z by using different periods concurrently after the warm-up stage. 2025-07-17T07:11:27.3856530Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-07-17T07:11:27.3857399Z that supports `post-local SGD `_, which essentially only supports 2025-07-17T07:11:27.3858171Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-07-17T07:11:27.3858962Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-07-17T07:11:27.3859736Z Similarly, the process groups within this class do not have such an intra-machine process 2025-07-17T07:11:27.3860439Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-07-17T07:11:27.3860846Z 2025-07-17T07:11:27.3860928Z Args: 2025-07-17T07:11:27.3861309Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-07-17T07:11:27.3861887Z process group size, used for initializing process groups of 2025-07-17T07:11:27.3862445Z different sizes in a hierarchy to average parameters concurrently. 2025-07-17T07:11:27.3863013Z Particularly, at each iteration, there will be at most a single 2025-07-17T07:11:27.3863582Z process group that runs averaging -- the period of such group should 2025-07-17T07:11:27.3864153Z have the largest period which the current step can be divided by. 2025-07-17T07:11:27.3864668Z For example, if the dict has three keys: 2, 4, and 8, 2025-07-17T07:11:27.3865166Z then this means totally three process groups will be created to 2025-07-17T07:11:27.3865696Z average parameters every 2, 4, and 8 iterations, respectively. 2025-07-17T07:11:27.3866218Z At the 4th iteration, only the second process group will run 2025-07-17T07:11:27.3866714Z averaging, because the first process group should be a 2025-07-17T07:11:27.3867244Z subset of the second process group, and no need to execute the first 2025-07-17T07:11:27.3867707Z process group redundantly. 2025-07-17T07:11:27.3868151Z On the other hand, the third process group can only be triggered 2025-07-17T07:11:27.3868708Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-07-17T07:11:27.3869364Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-07-17T07:11:27.3870245Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-07-17T07:11:27.3871000Z If ``None``, the default process group, which is created 2025-07-17T07:11:27.3871512Z by :func:`torch.distributed.init_process_group`, will be used. 2025-07-17T07:11:27.3872114Z (default: ``None``) 2025-07-17T07:11:27.3872364Z 2025-07-17T07:11:27.3872462Z Example:: 2025-07-17T07:11:27.3872829Z >>> # xdoctest: +SKIP('undefined rank') 2025-07-17T07:11:27.3873182Z >>> from collections import OrderedDict 2025-07-17T07:11:27.3873495Z >>> import torch 2025-07-17T07:11:27.3873766Z >>> import torch.distributed as dist 2025-07-17T07:11:27.3874331Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-07-17T07:11:27.3874835Z >>> PostLocalSGDState, 2025-07-17T07:11:27.3875125Z >>> post_localSGD_hook, 2025-07-17T07:11:27.3875403Z >>> ) 2025-07-17T07:11:27.3875897Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-07-17T07:11:27.3876512Z >>> import torch.nn as nn 2025-07-17T07:11:27.3876789Z >>> 2025-07-17T07:11:27.3877074Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-07-17T07:11:27.3877481Z >>> torch.cuda.set_device(rank) 2025-07-17T07:11:27.3877826Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-07-17T07:11:27.3878231Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T07:11:27.3878632Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T07:11:27.3878969Z >>> ) 2025-07-17T07:11:27.3879238Z >>> # Register a post-localSGD communication hook. 2025-07-17T07:11:27.3879765Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-07-17T07:11:27.3880285Z >>> subgroup, _ = dist.new_subgroups() 2025-07-17T07:11:27.3880828Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-07-17T07:11:27.3881429Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T07:11:27.3881777Z >>> 2025-07-17T07:11:27.3882179Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-07-17T07:11:27.3882708Z >>> # the 16 processes every 16 iterations. 2025-07-17T07:11:27.3883117Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-07-17T07:11:27.3883655Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-07-17T07:11:27.3884336Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T07:11:27.3885048Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-07-17T07:11:27.3885583Z >>> # After 100 steps, run model averaging at two levels. 2025-07-17T07:11:27.3885961Z >>> for step in range(0, 200): 2025-07-17T07:11:27.3886270Z >>> optimizer.zero_grad() 2025-07-17T07:11:27.3886563Z >>> loss = loss_fn(output, labels) 2025-07-17T07:11:27.3886882Z >>> loss.backward() 2025-07-17T07:11:27.3887155Z >>> optimizer.step() 2025-07-17T07:11:27.3887474Z >>> # Average parameters after ``optimizer.step()``. 2025-07-17T07:11:27.3888032Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-07-17T07:11:27.3888605Z >>> averager.average_parameters(model.parameters()) 2025-07-17T07:11:27.3888880Z 2025-07-17T07:11:27.3888984Z .. warning :: 2025-07-17T07:11:27.3889359Z The last group size in the dict must be the size of the provided ``process_group``, 2025-07-17T07:11:27.3889974Z which indicates model averaging at the highest level of the hierarchy. 2025-07-17T07:11:27.3890642Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-07-17T07:11:27.3891064Z 2025-07-17T07:11:27.3891156Z .. warning :: 2025-07-17T07:11:27.3891534Z `HierarchicalModelAverager` is experimental and subject to change. 2025-07-17T07:11:27.3891897Z 2025-07-17T07:11:27.3892152Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.3892521Z 2025-07-17T07:11:27.4215040Z 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. 2025-07-17T07:11:27.4216298Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4216702Z 2025-07-17T07:11:27.4216997Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-07-17T07:11:27.4217736Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-07-17T07:11:27.4218107Z 2025-07-17T07:11:27.4218282Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-07-17T07:11:27.4218625Z 2025-07-17T07:11:27.4218731Z .. warning:: 2025-07-17T07:11:27.4219030Z Current implementation only supports loading Tensors. 2025-07-17T07:11:27.4219323Z 2025-07-17T07:11:27.4219438Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:27.4219754Z >>> sd = {"mode": model} 2025-07-17T07:11:27.4219996Z >>> dcp.load( 2025-07-17T07:11:27.4220213Z >>> sd, 2025-07-17T07:11:27.4220484Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-07-17T07:11:27.4220861Z >>> planner=DynamicMetaLoadPlanner(), 2025-07-17T07:11:27.4221202Z >>> checkpoint_id="path_to_model.pt" 2025-07-17T07:11:27.4221504Z >>> ) 2025-07-17T07:11:27.4221615Z 2025-07-17T07:11:27.4221875Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4222244Z 2025-07-17T07:11:27.4222955Z 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. 2025-07-17T07:11:27.4224014Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4224406Z 2025-07-17T07:11:27.4224772Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-07-17T07:11:27.4225588Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-07-17T07:11:27.4226139Z metadata file, like Torch Save files. 2025-07-17T07:11:27.4226363Z 2025-07-17T07:11:27.4226541Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-07-17T07:11:27.4226844Z 2025-07-17T07:11:27.4226931Z .. warning:: 2025-07-17T07:11:27.4227236Z Current implementation only supports loading Tensors. 2025-07-17T07:11:27.4227517Z 2025-07-17T07:11:27.4227628Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:27.4227944Z >>> sd = {"mode": model} 2025-07-17T07:11:27.4228198Z >>> dcp.load( 2025-07-17T07:11:27.4228401Z >>> sd, 2025-07-17T07:11:27.4228676Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-07-17T07:11:27.4229066Z >>> planner=DynamicMetaLoadPlanner(), 2025-07-17T07:11:27.4229394Z >>> checkpoint_id="path_to_model.pt" 2025-07-17T07:11:27.4229691Z >>> ) 2025-07-17T07:11:27.4229800Z 2025-07-17T07:11:27.4230059Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4230424Z 2025-07-17T07:11:27.4294317Z 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. 2025-07-17T07:11:27.4295429Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4295820Z 2025-07-17T07:11:27.4296043Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-07-17T07:11:27.4296380Z 2025-07-17T07:11:27.4296542Z This is the current recommended way to checkpoint FSDP. 2025-07-17T07:11:27.4296916Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.4297212Z >>> import torch.distributed.checkpoint as dist_cp 2025-07-17T07:11:27.4297560Z >>> # Save 2025-07-17T07:11:27.4297787Z >>> model: torch.nn.Model 2025-07-17T07:11:27.4298070Z >>> optim_params = model.parameters() 2025-07-17T07:11:27.4298414Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-07-17T07:11:27.4298749Z >>> # Save 2025-07-17T07:11:27.4299084Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-07-17T07:11:27.4299501Z >>> state_dict = { 2025-07-17T07:11:27.4299811Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-07-17T07:11:27.4300310Z >>> "model": model.state_dict() 2025-07-17T07:11:27.4300597Z >>> } 2025-07-17T07:11:27.4300705Z >>> dist_cp.save_state_dict( 2025-07-17T07:11:27.4300808Z >>> state_dict=optim_state, 2025-07-17T07:11:27.4301047Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-07-17T07:11:27.4301189Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-07-17T07:11:27.4301271Z >>> ) 2025-07-17T07:11:27.4301355Z >>> 2025-07-17T07:11:27.4301435Z >>> # Load 2025-07-17T07:11:27.4301659Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-07-17T07:11:27.4301790Z >>> model_state_dict = model_tp.state_dict() 2025-07-17T07:11:27.4301882Z >>> checkpoint = { 2025-07-17T07:11:27.4301984Z >>> "model": model_state_dict 2025-07-17T07:11:27.4302072Z >>> } 2025-07-17T07:11:27.4302173Z >>> dist_cp.load_state_dict( 2025-07-17T07:11:27.4302274Z >>> state_dict=checkpoint, 2025-07-17T07:11:27.4302475Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-07-17T07:11:27.4302608Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-07-17T07:11:27.4302685Z >>> ) 2025-07-17T07:11:27.4302845Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-07-17T07:11:27.4302925Z >>> 2025-07-17T07:11:27.4303108Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-07-17T07:11:27.4303205Z >>> model_state_dict, 2025-07-17T07:11:27.4303313Z >>> optimizer_key="optimizer", 2025-07-17T07:11:27.4303496Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-07-17T07:11:27.4303577Z >>> ) 2025-07-17T07:11:27.4303654Z >>> 2025-07-17T07:11:27.4303810Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-07-17T07:11:27.4303932Z >>> model, optim, optim_state["optimizer"] 2025-07-17T07:11:27.4304012Z >>> ) 2025-07-17T07:11:27.4304097Z >>> 2025-07-17T07:11:27.4304214Z >>> optim.load_state_dict(flattened_osd) 2025-07-17T07:11:27.4304219Z 2025-07-17T07:11:27.4304484Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4304488Z 2025-07-17T07:11:27.4319030Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=122. 2025-07-17T07:11:27.4319296Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4319315Z 2025-07-17T07:11:27.4319598Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-07-17T07:11:27.4319603Z 2025-07-17T07:11:27.4319898Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-07-17T07:11:27.4319903Z 2025-07-17T07:11:27.4320195Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-07-17T07:11:27.4320308Z will be visible to the whole process. 2025-07-17T07:11:27.4320312Z 2025-07-17T07:11:27.4320594Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-07-17T07:11:27.4320601Z 2025-07-17T07:11:27.4320720Z 1) set_up_planner - called on all ranks. 2025-07-17T07:11:27.4320838Z Signals the start of a checkpoint save. 2025-07-17T07:11:27.4320842Z 2025-07-17T07:11:27.4320976Z 2) create_local_plan - called on all ranks. 2025-07-17T07:11:27.4321277Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-07-17T07:11:27.4321281Z 2025-07-17T07:11:27.4321470Z 3) create_global_plan - called on the coordinator rank only. 2025-07-17T07:11:27.4321666Z Takes the SavePlan from all ranks and make any global decision. 2025-07-17T07:11:27.4321670Z 2025-07-17T07:11:27.4321793Z 4) finish_plan - called on all ranks. 2025-07-17T07:11:27.4322007Z This gives each rank a chance to adjust to global planning decisions. 2025-07-17T07:11:27.4322011Z 2025-07-17T07:11:27.4322160Z 5) resolve_data - called multiple times on each rank 2025-07-17T07:11:27.4322369Z Lookups a value on the `state_dict` for the storage layer to write. 2025-07-17T07:11:27.4322446Z 2025-07-17T07:11:27.4322751Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-07-17T07:11:27.4323013Z most changes can be expressed by changes in a single method. 2025-07-17T07:11:27.4323018Z 2025-07-17T07:11:27.4323137Z There are 3 usual patterns of extension: 2025-07-17T07:11:27.4323142Z 2025-07-17T07:11:27.4323392Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-07-17T07:11:27.4323628Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-07-17T07:11:27.4323633Z 2025-07-17T07:11:27.4323745Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:27.4323876Z >>> class RenamePlanner(DefaultSavePlanner): 2025-07-17T07:11:27.4323983Z >>> def set_up_planner( 2025-07-17T07:11:27.4324067Z >>> self, 2025-07-17T07:11:27.4324184Z >>> state_dict: STATE_DICT_TYPE, 2025-07-17T07:11:27.4324302Z >>> storage_meta: Optional[StorageMeta], 2025-07-17T07:11:27.4324407Z >>> is_coordinator: bool, 2025-07-17T07:11:27.4324507Z >>> ) -> None: 2025-07-17T07:11:27.4324610Z >>> # prefix all keys with `foo_`` 2025-07-17T07:11:27.4324909Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-07-17T07:11:27.4324914Z 2025-07-17T07:11:27.4325350Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-07-17T07:11:27.4325357Z 2025-07-17T07:11:27.4325538Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:27.4325714Z >>> class FP16Planner(DefaultSavePlanner): 2025-07-17T07:11:27.4325817Z >>> def create_local_plan(self): 2025-07-17T07:11:27.4325934Z >>> plan = super().create_local_plan() 2025-07-17T07:11:27.4326039Z >>> for p in plan: 2025-07-17T07:11:27.4326149Z >>> if p.tensor_data is not None: 2025-07-17T07:11:27.4326313Z >>> p.tensor_data.properties.dtype = torch.float16 2025-07-17T07:11:27.4326416Z >>> return plan 2025-07-17T07:11:27.4326492Z >>> 2025-07-17T07:11:27.4326603Z >>> def resolve_data(self, write_item): 2025-07-17T07:11:27.4326734Z >>> item = super().resolve_data(write_item) 2025-07-17T07:11:27.4327013Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-07-17T07:11:27.4327018Z 2025-07-17T07:11:27.4327367Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-07-17T07:11:27.4327371Z 2025-07-17T07:11:27.4327476Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:27.4327586Z >>> from itertools import zip_longest 2025-07-17T07:11:27.4327705Z >>> from dataclasses import replace 2025-07-17T07:11:27.4327872Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-07-17T07:11:27.4328158Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-07-17T07:11:27.4328284Z >>> # This sample doesn't handle ShardedTensors 2025-07-17T07:11:27.4328410Z >>> def create_global_plan(self, all_plans): 2025-07-17T07:11:27.4328569Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-07-17T07:11:27.4328664Z >>> items_per_rank = [ 2025-07-17T07:11:27.4328800Z >>> [item for item in items if item is not None] 2025-07-17T07:11:27.4328969Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-07-17T07:11:27.4329047Z >>> ] 2025-07-17T07:11:27.4329136Z >>> all_plans = [ 2025-07-17T07:11:27.4329252Z >>> replace(plan, items=items) 2025-07-17T07:11:27.4329435Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-07-17T07:11:27.4329525Z >>> ] 2025-07-17T07:11:27.4329662Z >>> return super().create_global_plan(all_plans) 2025-07-17T07:11:27.4329667Z 2025-07-17T07:11:27.4329928Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-07-17T07:11:27.4330276Z accomplished by having each rank contribute their data items in the local plan and 2025-07-17T07:11:27.4330388Z the global planner aggregate them: 2025-07-17T07:11:27.4330392Z 2025-07-17T07:11:27.4330499Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:27.4330716Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-07-17T07:11:27.4330845Z >>> def create_local_plan(self) -> SavePlan: 2025-07-17T07:11:27.4330974Z >>> plan = super().create_local_plan() 2025-07-17T07:11:27.4331135Z >>> return replace(plan, planner_data="per-rank-data") 2025-07-17T07:11:27.4331212Z >>> 2025-07-17T07:11:27.4331517Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-07-17T07:11:27.4331711Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-07-17T07:11:27.4331866Z >>> merged_data = [p.planner_data for p in global_plan] 2025-07-17T07:11:27.4332046Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-07-17T07:11:27.4332160Z >>> return global_plan, metadata 2025-07-17T07:11:27.4332164Z 2025-07-17T07:11:27.4332424Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4332429Z 2025-07-17T07:11:27.4333056Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=305. 2025-07-17T07:11:27.4333326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4333331Z 2025-07-17T07:11:27.4333613Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-07-17T07:11:27.4333617Z 2025-07-17T07:11:27.4333916Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-07-17T07:11:27.4333920Z 2025-07-17T07:11:27.4334196Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-07-17T07:11:27.4334310Z will be visible to the whole process. 2025-07-17T07:11:27.4334318Z 2025-07-17T07:11:27.4334605Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-07-17T07:11:27.4334609Z 2025-07-17T07:11:27.4334726Z 1) set_up_planner - called on all ranks. 2025-07-17T07:11:27.4334870Z Signals the start of loading a checkpoint. 2025-07-17T07:11:27.4334874Z 2025-07-17T07:11:27.4334991Z 2) create_local_plan - called on all ranks. 2025-07-17T07:11:27.4335341Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-07-17T07:11:27.4335346Z 2025-07-17T07:11:27.4335540Z 3) create_global_plan - called on the coordinator rank only. 2025-07-17T07:11:27.4335736Z Takes the LoadPlan from all ranks and make any global decision. 2025-07-17T07:11:27.4335740Z 2025-07-17T07:11:27.4335894Z 4) load_bytes - called multiple times on each rank 2025-07-17T07:11:27.4336060Z This is called once per non-tensor value in state_dict. 2025-07-17T07:11:27.4336064Z 2025-07-17T07:11:27.4336285Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-07-17T07:11:27.4336477Z They are called in pair for each Tensor value in state_dict. 2025-07-17T07:11:27.4336481Z 2025-07-17T07:11:27.4336780Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-07-17T07:11:27.4336958Z most changes can be expressed by changes in a single method. 2025-07-17T07:11:27.4336974Z 2025-07-17T07:11:27.4337102Z There are two usual patterns of extension: 2025-07-17T07:11:27.4337106Z 2025-07-17T07:11:27.4337356Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-07-17T07:11:27.4337620Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-07-17T07:11:27.4337847Z to keep a reference to the original state_dict as load happens in place so 2025-07-17T07:11:27.4337965Z we need to be able to perform it in place 2025-07-17T07:11:27.4337970Z 2025-07-17T07:11:27.4338090Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:27.4338279Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-07-17T07:11:27.4338382Z >>> def set_up_planner( 2025-07-17T07:11:27.4338466Z >>> self, 2025-07-17T07:11:27.4338574Z >>> state_dict: STATE_DICT_TYPE, 2025-07-17T07:11:27.4338734Z >>> metadata: Metadata, 2025-07-17T07:11:27.4338836Z >>> is_coordinator: bool, 2025-07-17T07:11:27.4338921Z >>> ) -> None: 2025-07-17T07:11:27.4339050Z >>> self.original_state_dict = state_dict 2025-07-17T07:11:27.4339224Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-07-17T07:11:27.4339298Z >>> 2025-07-17T07:11:27.4339424Z >>> if self.flatten_sharded_tensors: 2025-07-17T07:11:27.4339575Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-07-17T07:11:27.4339664Z >>> 2025-07-17T07:11:27.4339767Z >>> if self.flatten_state_dict: 2025-07-17T07:11:27.4339946Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-07-17T07:11:27.4340036Z >>> 2025-07-17T07:11:27.4340139Z >>> self.state_dict = state_dict 2025-07-17T07:11:27.4340239Z >>> self.metadata = metadata 2025-07-17T07:11:27.4340361Z >>> self.is_coordinator = is_coordinator 2025-07-17T07:11:27.4340437Z >>> 2025-07-17T07:11:27.4340559Z >>> def load_bytes(self, read_item, value): 2025-07-17T07:11:27.4340660Z >>> # Remove the "foo_" prefix 2025-07-17T07:11:27.4340979Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-07-17T07:11:27.4340984Z 2025-07-17T07:11:27.4340988Z 2025-07-17T07:11:27.4341253Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-07-17T07:11:27.4341257Z 2025-07-17T07:11:27.4341364Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:27.4341517Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-07-17T07:11:27.4341640Z >>> def resolve_tensor(self, read_item): 2025-07-17T07:11:27.4341767Z >>> tensor = super().resolve_tensor(read_item) 2025-07-17T07:11:27.4341915Z >>> return torch.empty_like(tensor, device="cpu") 2025-07-17T07:11:27.4341999Z >>> 2025-07-17T07:11:27.4342122Z >>> def commit_tensor(self, read_item, tensor): 2025-07-17T07:11:27.4342291Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-07-17T07:11:27.4342296Z 2025-07-17T07:11:27.4342549Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4342553Z 2025-07-17T07:11:27.4486647Z msg = Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-07-17T07:11:27.4486948Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4486953Z 2025-07-17T07:11:27.4487128Z Return the model state_dict and optimizers state_dict. 2025-07-17T07:11:27.4487133Z 2025-07-17T07:11:27.4487349Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-07-17T07:11:27.4487605Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-07-17T07:11:27.4487863Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-07-17T07:11:27.4488082Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-07-17T07:11:27.4488288Z with a different number of trainers and/or different parallelisms. 2025-07-17T07:11:27.4488550Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-07-17T07:11:27.4488637Z these APIs. 2025-07-17T07:11:27.4488772Z 3.) sanity checking the result state_dict. 2025-07-17T07:11:27.4488777Z 2025-07-17T07:11:27.4488981Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-07-17T07:11:27.4489215Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-07-17T07:11:27.4489459Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-07-17T07:11:27.4489783Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-07-17T07:11:27.4490002Z ``module.named_buffers()`` when the module is not distributed by any 2025-07-17T07:11:27.4490255Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-07-17T07:11:27.4490533Z a parameter, there will be a conversion from the parameter IDs to the 2025-07-17T07:11:27.4490661Z canonical FQNs when calling this API. 2025-07-17T07:11:27.4490665Z 2025-07-17T07:11:27.4490881Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-07-17T07:11:27.4491101Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-07-17T07:11:27.4491255Z optimizer parameter IDs to the canonical FQNs. 2025-07-17T07:11:27.4491259Z 2025-07-17T07:11:27.4491341Z Example: 2025-07-17T07:11:27.4491449Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.4491541Z >>> import torch 2025-07-17T07:11:27.4491779Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T07:11:27.4491994Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-07-17T07:11:27.4492213Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-07-17T07:11:27.4492217Z 2025-07-17T07:11:27.4492342Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-07-17T07:11:27.4492540Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-07-17T07:11:27.4492659Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-07-17T07:11:27.4492853Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-07-17T07:11:27.4492857Z 2025-07-17T07:11:27.4492861Z 2025-07-17T07:11:27.4493093Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-07-17T07:11:27.4493258Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-07-17T07:11:27.4493373Z ... fsdp_model, fsdp_optim 2025-07-17T07:11:27.4493454Z ... ) 2025-07-17T07:11:27.4493458Z 2025-07-17T07:11:27.4493679Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-07-17T07:11:27.4493780Z >>> # the asserts will fail. 2025-07-17T07:11:27.4493903Z >>> assert ddp_state_dict == fsdp_state_dict 2025-07-17T07:11:27.4494055Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-07-17T07:11:27.4494064Z 2025-07-17T07:11:27.4494067Z 2025-07-17T07:11:27.4494146Z Args: 2025-07-17T07:11:27.4494280Z model (nn.Module): the nn.Module to the model. 2025-07-17T07:11:27.4494471Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-07-17T07:11:27.4494625Z The optimizers that are used to optimize ``model``. 2025-07-17T07:11:27.4494916Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-07-17T07:11:27.4495023Z that belong to the submodules. 2025-07-17T07:11:27.4495236Z options (StateDictOptions): the options to control how 2025-07-17T07:11:27.4495455Z model state_dict and optimizer state_dict should be returned. See 2025-07-17T07:11:27.4495574Z `StateDictOptions` for the details. 2025-07-17T07:11:27.4495578Z 2025-07-17T07:11:27.4495662Z Returns: 2025-07-17T07:11:27.4495863Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-07-17T07:11:27.4495867Z 2025-07-17T07:11:27.4496093Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-07-17T07:11:27.4496098Z 2025-07-17T07:11:27.4496361Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4496366Z 2025-07-17T07:11:27.4516893Z 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. 2025-07-17T07:11:27.4517166Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4517170Z 2025-07-17T07:11:27.4517362Z Load a checkpoint into a distributed state dict in SPMD style. 2025-07-17T07:11:27.4517366Z 2025-07-17T07:11:27.4517583Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-07-17T07:11:27.4517943Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-07-17T07:11:27.4518160Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-07-17T07:11:27.4518310Z costs). 2025-07-17T07:11:27.4518328Z 2025-07-17T07:11:27.4518502Z Each rank will try to read the least amount of data necessary 2025-07-17T07:11:27.4518765Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-07-17T07:11:27.4519010Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-07-17T07:11:27.4519015Z 2025-07-17T07:11:27.4519267Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-07-17T07:11:27.4519534Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-07-17T07:11:27.4519697Z ``load_state_dict`` once the deserialization is complete. 2025-07-17T07:11:27.4519950Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-07-17T07:11:27.4520116Z it in the ``state_dict`` with the deserialized object. 2025-07-17T07:11:27.4520120Z 2025-07-17T07:11:27.4520209Z .. warning:: 2025-07-17T07:11:27.4520391Z All tensors in ``state_dict`` must be allocated on their 2025-07-17T07:11:27.4520552Z destination device *prior to* calling this function. 2025-07-17T07:11:27.4520556Z 2025-07-17T07:11:27.4520776Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-07-17T07:11:27.4520876Z on state_dict. 2025-07-17T07:11:27.4520880Z 2025-07-17T07:11:27.4520965Z .. warning:: 2025-07-17T07:11:27.4521172Z Users must call `load_state_dict` on the root module to ensure load 2025-07-17T07:11:27.4521352Z pos-processing and non-tensor data properly propagates. 2025-07-17T07:11:27.4521357Z 2025-07-17T07:11:27.4521439Z .. note: 2025-07-17T07:11:27.4521667Z If no process group is initialized, this function will assume the intent 2025-07-17T07:11:27.4521892Z is to load a checkpoint into the local process. This can be useful in the 2025-07-17T07:11:27.4522131Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-07-17T07:11:27.4522228Z or ShardedTensor) 2025-07-17T07:11:27.4522237Z 2025-07-17T07:11:27.4522319Z .. note: 2025-07-17T07:11:27.4522456Z Rank 0 is assumed to be the coordinator rank. 2025-07-17T07:11:27.4522461Z 2025-07-17T07:11:27.4522539Z Args: 2025-07-17T07:11:27.4522741Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-07-17T07:11:27.4522891Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T07:11:27.4523095Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T07:11:27.4523290Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T07:11:27.4523468Z It can also be a key if the storage is a key-value store. 2025-07-17T07:11:27.4523561Z (Default: ``None``) 2025-07-17T07:11:27.4523704Z storage_reader (Optional[StorageReader]): 2025-07-17T07:11:27.4523908Z Instance of StorageWriter used to perform reads. If this is not 2025-07-17T07:11:27.4524104Z specified, DCP will automatically infer the reader based on the 2025-07-17T07:11:27.4524316Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T07:11:27.4524423Z be raised. (Default: ``None``) 2025-07-17T07:11:27.4524534Z planner (Optional[LoadPlanner]): 2025-07-17T07:11:27.4524742Z Instance of LoadPlanner. If this is not specified, the default 2025-07-17T07:11:27.4524867Z planner will be used. (Default: ``None``) 2025-07-17T07:11:27.4524992Z process_group (Optional[ProcessGroup]): 2025-07-17T07:11:27.4525186Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T07:11:27.4525282Z (Default: ``None``) 2025-07-17T07:11:27.4525497Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-07-17T07:11:27.4525804Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-07-17T07:11:27.4525888Z Returns: 2025-07-17T07:11:27.4525979Z None. 2025-07-17T07:11:27.4525984Z 2025-07-17T07:11:27.4526068Z Examples 2025-07-17T07:11:27.4526207Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.4526316Z >>> my_model = MyModule() 2025-07-17T07:11:27.4526448Z >>> optimizer = Adagrad(my_model.parameters()) 2025-07-17T07:11:27.4526568Z >>> model_state_dict = my_model.state_dict() 2025-07-17T07:11:27.4526808Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-07-17T07:11:27.4526903Z ... "/checkpoint/1" 2025-07-17T07:11:27.4526992Z ... ) 2025-07-17T07:11:27.4526996Z 2025-07-17T07:11:27.4527149Z >>> torch.distributed.checkpoint.load_state_dict( 2025-07-17T07:11:27.4527257Z >>> state_dict=model_state_dict, 2025-07-17T07:11:27.4527391Z >>> storage_reader=fs_storage_reader, 2025-07-17T07:11:27.4527478Z >>> ) 2025-07-17T07:11:27.4527483Z 2025-07-17T07:11:27.4527678Z >>> # module.load_state_dict() function might have customized steps 2025-07-17T07:11:27.4527818Z >>> # to flush the state_dict, must call it to 2025-07-17T07:11:27.4527922Z >>> # ensure correct behavior. 2025-07-17T07:11:27.4528067Z >>> my_model.load_state_dict(model_state_dict) 2025-07-17T07:11:27.4528071Z 2025-07-17T07:11:27.4528157Z .. note:: 2025-07-17T07:11:27.4528366Z load_state_dict uses collectives to coordinate reads across ranks. 2025-07-17T07:11:27.4528591Z For NCCL-based process groups, internal tensor representations of 2025-07-17T07:11:27.4528821Z objects must be moved to the GPU device before communication takes place. 2025-07-17T07:11:27.4529039Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-07-17T07:11:27.4529276Z and it is the user's responsibility to ensure that this is set so that each 2025-07-17T07:11:27.4529460Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-07-17T07:11:27.4529468Z 2025-07-17T07:11:27.4529732Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4529736Z 2025-07-17T07:11:27.4545398Z 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=96. 2025-07-17T07:11:27.4545683Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4545688Z 2025-07-17T07:11:27.4545809Z Save a distributed model in SPMD style. 2025-07-17T07:11:27.4545813Z 2025-07-17T07:11:27.4546005Z This function is different from ``torch.save()`` as it handles 2025-07-17T07:11:27.4546279Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-07-17T07:11:27.4546283Z 2025-07-17T07:11:27.4546541Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-07-17T07:11:27.4546702Z save will call ``state_dict`` before serialization. 2025-07-17T07:11:27.4546729Z 2025-07-17T07:11:27.4546822Z .. warning:: 2025-07-17T07:11:27.4547076Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-07-17T07:11:27.4547179Z for saved state_dicts. 2025-07-17T07:11:27.4547183Z 2025-07-17T07:11:27.4547273Z .. warning:: 2025-07-17T07:11:27.4547497Z If using the `process_group` argument, make sure that only its ranks 2025-07-17T07:11:27.4547700Z call `save_state_dict` and that all data in state_dict belong to it. 2025-07-17T07:11:27.4547704Z 2025-07-17T07:11:27.4547790Z .. note:: 2025-07-17T07:11:27.4548060Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-07-17T07:11:27.4548320Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-07-17T07:11:27.4548447Z group needs to be passed in. 2025-07-17T07:11:27.4548451Z 2025-07-17T07:11:27.4548534Z .. note:: 2025-07-17T07:11:27.4548799Z If no process group is available, this function assumes the intention is to save the 2025-07-17T07:11:27.4549011Z state_dict in the local process. 2025-07-17T07:11:27.4549015Z 2025-07-17T07:11:27.4549099Z .. note: 2025-07-17T07:11:27.4549237Z Rank 0 is assumed to be the coordinator rank. 2025-07-17T07:11:27.4549306Z 2025-07-17T07:11:27.4549310Z 2025-07-17T07:11:27.4549393Z Args: 2025-07-17T07:11:27.4549545Z state_dict (Dict[str, Any]): The state_dict to save. 2025-07-17T07:11:27.4549703Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T07:11:27.4549911Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T07:11:27.4550111Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T07:11:27.4550294Z It can also be a key if the storage is a key-value store. 2025-07-17T07:11:27.4550389Z (Default: ``None``) 2025-07-17T07:11:27.4550532Z storage_writer (Optional[StorageWriter]): 2025-07-17T07:11:27.4550738Z Instance of StorageWriter used to perform writes. If this is not 2025-07-17T07:11:27.4550942Z specified, DCP will automatically infer the writer based on the 2025-07-17T07:11:27.4551153Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T07:11:27.4551266Z be raised. (Default: ``None``) 2025-07-17T07:11:27.4551378Z planner (Optional[SavePlanner]): 2025-07-17T07:11:27.4551613Z Instance of SavePlanner. If this is not specified, the default 2025-07-17T07:11:27.4551815Z planner will be used. (Default: ``None``) 2025-07-17T07:11:27.4552006Z process_group (Optional[ProcessGroup]): 2025-07-17T07:11:27.4552276Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T07:11:27.4552370Z (Default: ``None``) 2025-07-17T07:11:27.4552472Z no_dist (bool): 2025-07-17T07:11:27.4552642Z If ``True``, this function will assume the intent is to load 2025-07-17T07:11:27.4552812Z a checkpoint without using cross-rank synchronization. 2025-07-17T07:11:27.4552926Z (Default: ``False``) 2025-07-17T07:11:27.4552931Z 2025-07-17T07:11:27.4553016Z Returns: 2025-07-17T07:11:27.4553170Z Metadata: Metadata object for the saved checkpoint. 2025-07-17T07:11:27.4553175Z 2025-07-17T07:11:27.4553270Z Example: 2025-07-17T07:11:27.4553369Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.4553479Z >>> my_model = MyModule() 2025-07-17T07:11:27.4553483Z 2025-07-17T07:11:27.4553590Z >>> state_dict = {"model": my_model} 2025-07-17T07:11:27.4553595Z 2025-07-17T07:11:27.4553828Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-07-17T07:11:27.4553937Z ... "/checkpoint/1" 2025-07-17T07:11:27.4554018Z ... ) 2025-07-17T07:11:27.4554147Z >>> torch.distributed.checkpoint.save( 2025-07-17T07:11:27.4554264Z >>> state_dict=state_dict, 2025-07-17T07:11:27.4554379Z >>> storage_writer=fs_storage_writer, 2025-07-17T07:11:27.4554463Z >>> ) 2025-07-17T07:11:27.4554481Z 2025-07-17T07:11:27.4554570Z .. note:: 2025-07-17T07:11:27.4559535Z save_state_dict uses collectives to coordinate writes across ranks. 2025-07-17T07:11:27.4559766Z For NCCL-based process groups, internal tensor representations of 2025-07-17T07:11:27.4560010Z objects must be moved to the GPU device before communication takes place. 2025-07-17T07:11:27.4560231Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-07-17T07:11:27.4560446Z and it is the user's responsibility to ensure that this is set so that 2025-07-17T07:11:27.4560642Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-07-17T07:11:27.4560647Z 2025-07-17T07:11:27.4560901Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4560917Z 2025-07-17T07:11:27.4561581Z 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=221. 2025-07-17T07:11:27.4561843Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4562216Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-07-17T07:11:27.4562559Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-07-17T07:11:27.4562565Z 2025-07-17T07:11:27.4562675Z .. warning:: 2025-07-17T07:11:27.4562835Z This feature is experimental and subject to change. 2025-07-17T07:11:27.4562980Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-07-17T07:11:27.4562984Z 2025-07-17T07:11:27.4563081Z Args: 2025-07-17T07:11:27.4563238Z state_dict (Dict[str, Any]): The state_dict to save. 2025-07-17T07:11:27.4563383Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T07:11:27.4563609Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T07:11:27.4563813Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T07:11:27.4563998Z It can also be a key if the storage is a key-value store. 2025-07-17T07:11:27.4564101Z (Default: ``None``) 2025-07-17T07:11:27.4564238Z storage_writer (Optional[StorageWriter]): 2025-07-17T07:11:27.4564465Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-07-17T07:11:27.4564699Z this is not specified, DCP will automatically infer the writer based on the 2025-07-17T07:11:27.4564900Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T07:11:27.4565023Z be raised. (Default: ``None``) 2025-07-17T07:11:27.4565140Z planner (Optional[SavePlanner]): 2025-07-17T07:11:27.4565336Z Instance of SavePlanner. If this is not specified, the default 2025-07-17T07:11:27.4565478Z planner will be used. (Default: ``None``) 2025-07-17T07:11:27.4565607Z process_group (Optional[ProcessGroup]): 2025-07-17T07:11:27.4565800Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T07:11:27.4565902Z (Default: ``None``) 2025-07-17T07:11:27.4566059Z async_checkpointer_type (AsyncCheckpointerType): 2025-07-17T07:11:27.4566240Z whether to do checkpoint in separate thread or process 2025-07-17T07:11:27.4566387Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-07-17T07:11:27.4566495Z async_stager (AsyncStager): 2025-07-17T07:11:27.4566764Z provides staging implementation. If storage_writer implements AsyncStager 2025-07-17T07:11:27.4566973Z and async_stager is provided, async_stager will be used for staging 2025-07-17T07:11:27.4566977Z 2025-07-17T07:11:27.4567075Z Returns: 2025-07-17T07:11:27.4567284Z Future: A future holding the resultant Metadata object from `save`. 2025-07-17T07:11:27.4567288Z 2025-07-17T07:11:27.4567372Z Example: 2025-07-17T07:11:27.4567483Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.4567580Z >>> my_model = MyModule() 2025-07-17T07:11:27.4567586Z 2025-07-17T07:11:27.4567762Z >>> state_dict = {"model": my_model} 2025-07-17T07:11:27.4567779Z 2025-07-17T07:11:27.4568010Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-07-17T07:11:27.4568110Z ... "/checkpoint/1" 2025-07-17T07:11:27.4568207Z ... ) 2025-07-17T07:11:27.4568415Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-07-17T07:11:27.4568520Z >>> state_dict=state_dict, 2025-07-17T07:11:27.4568653Z >>> storage_writer=fs_storage_writer, 2025-07-17T07:11:27.4568735Z >>> ) 2025-07-17T07:11:27.4568817Z >>> 2025-07-17T07:11:27.4568929Z >>> # ... do some work ... 2025-07-17T07:11:27.4569011Z >>> 2025-07-17T07:11:27.4569120Z >>> checkpoint_future.result() 2025-07-17T07:11:27.4569139Z 2025-07-17T07:11:27.4569219Z 2025-07-17T07:11:27.4569472Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4569521Z 2025-07-17T07:11:27.4791889Z 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. 2025-07-17T07:11:27.4792271Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.4792278Z 2025-07-17T07:11:27.4792493Z Initialize rendezvous event object and record its operations. 2025-07-17T07:11:27.4792498Z 2025-07-17T07:11:27.4792579Z Args: 2025-07-17T07:11:27.4792704Z run_id (str): The run id of the rendezvous. 2025-07-17T07:11:27.4792866Z message (str): The message describing the event. 2025-07-17T07:11:27.4793121Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-07-17T07:11:27.4793304Z name (str): Event name. (E.g. Current action being performed). 2025-07-17T07:11:27.4793431Z hostname (str): Hostname of the node. 2025-07-17T07:11:27.4793569Z pid (Optional[int]): The process id of the node. 2025-07-17T07:11:27.4793832Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-07-17T07:11:27.4794103Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-07-17T07:11:27.4794264Z rank (Optional[int]): The rank of the node, if known. 2025-07-17T07:11:27.4794361Z Returns: 2025-07-17T07:11:27.4794443Z None 2025-07-17T07:11:27.4794526Z Example: 2025-07-17T07:11:27.4794665Z >>> # See DynamicRendezvousHandler class 2025-07-17T07:11:27.4794754Z >>> def _record( 2025-07-17T07:11:27.4794859Z ... self, 2025-07-17T07:11:27.4794952Z ... message: str, 2025-07-17T07:11:27.4795088Z ... node_state: NodeState = NodeState.RUNNING, 2025-07-17T07:11:27.4795207Z ... rank: Optional[int] = None, 2025-07-17T07:11:27.4795297Z ... ) -> None: 2025-07-17T07:11:27.4795415Z ... construct_and_record_rdzv_event( 2025-07-17T07:11:27.4795592Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-07-17T07:11:27.4795713Z ... run_id=self._settings.run_id, 2025-07-17T07:11:27.4795811Z ... message=message, 2025-07-17T07:11:27.4795927Z ... node_state=node_state, 2025-07-17T07:11:27.4796047Z ... hostname=self._this_node.addr, 2025-07-17T07:11:27.4796161Z ... pid=self._this_node.pid, 2025-07-17T07:11:27.4796281Z ... local_id=self._this_node.local_id, 2025-07-17T07:11:27.4796378Z ... rank=rank, 2025-07-17T07:11:27.4796501Z ... ) 2025-07-17T07:11:27.4796506Z 2025-07-17T07:11:27.4796784Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.4796789Z 2025-07-17T07:11:27.7925601Z 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. 2025-07-17T07:11:27.7926594Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.7926999Z 2025-07-17T07:11:27.7927373Z This configures FSDP-native mixed precision training. 2025-07-17T07:11:27.7927669Z 2025-07-17T07:11:27.7927761Z Attributes: 2025-07-17T07:11:27.7928154Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-07-17T07:11:27.7928725Z parameters during forward and backward and thus the dtype for 2025-07-17T07:11:27.7929262Z forward and backward computation. Outside forward and backward, the 2025-07-17T07:11:27.7929807Z *sharded* parameters are kept in full precision (e.g. for the 2025-07-17T07:11:27.7930334Z optimizer step), and for model checkpointing, the parameters are 2025-07-17T07:11:27.7930816Z always saved in full precision. (Default: ``None``) 2025-07-17T07:11:27.7931316Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-07-17T07:11:27.7931871Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-07-17T07:11:27.7932460Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-07-17T07:11:27.7932955Z the ``param_dtype`` value, still running gradient reduction in low 2025-07-17T07:11:27.7933496Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-07-17T07:11:27.7934285Z to force gradient reduction to run in full precision. (Default: 2025-07-17T07:11:27.7934707Z ``None``) 2025-07-17T07:11:27.7935069Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-07-17T07:11:27.7935676Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-07-17T07:11:27.7936203Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-07-17T07:11:27.7936723Z dtype thereafter. For model checkpointing, the buffers are saved 2025-07-17T07:11:27.7937246Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-07-17T07:11:27.7937643Z ``None``) 2025-07-17T07:11:27.7937977Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-07-17T07:11:27.7938524Z gradients to full precision after the backward pass in preparation 2025-07-17T07:11:27.7939071Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-07-17T07:11:27.7939590Z in the dtype used for gradient reduction, which can save memory if 2025-07-17T07:11:27.7940124Z using a custom optimizer that supports running in low precision. 2025-07-17T07:11:27.7940554Z (Default: ``False``) 2025-07-17T07:11:27.7940946Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-07-17T07:11:27.7941467Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-07-17T07:11:27.7942005Z that parameter and input dtypes match for forward computation, as 2025-07-17T07:11:27.7942545Z required by many ops. This may need to be set to ``True`` when only 2025-07-17T07:11:27.7943082Z applying mixed precision to some but not all FSDP modules, in which 2025-07-17T07:11:27.7943643Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-07-17T07:11:27.7944077Z (Default: ``False``) 2025-07-17T07:11:27.7944478Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-07-17T07:11:27.7945015Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-07-17T07:11:27.7945566Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-07-17T07:11:27.7946017Z this does not do anything. (Default: ``True``) 2025-07-17T07:11:27.7946491Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-07-17T07:11:27.7947010Z module classes to ignore for mixed precision when using an 2025-07-17T07:11:27.7947498Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-07-17T07:11:27.7948013Z applied to them separately with mixed precision disabled (meaning 2025-07-17T07:11:27.7948555Z that the final FSDP construction would deviate from the specified 2025-07-17T07:11:27.7949122Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-07-17T07:11:27.7949638Z not do anything. This API is experimental and subject to change. 2025-07-17T07:11:27.7950071Z (Default: ``(_BatchNorm,)``) 2025-07-17T07:11:27.7950276Z 2025-07-17T07:11:27.7950460Z .. note:: This API is experimental and subject to change. 2025-07-17T07:11:27.7950748Z 2025-07-17T07:11:27.7950970Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-07-17T07:11:27.7951318Z 2025-07-17T07:11:27.7951500Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-07-17T07:11:27.7951914Z precision, but buffers are not. 2025-07-17T07:11:27.7952122Z 2025-07-17T07:11:27.7952325Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-07-17T07:11:27.7952867Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-07-17T07:11:27.7953475Z Disabling FSDP's mixed precision for those norm modules only means that 2025-07-17T07:11:27.7954051Z the affine parameters are kept in ``float32``. However, this incurs 2025-07-17T07:11:27.7954612Z separate all-gathers and reduce-scatters for those norm modules, which 2025-07-17T07:11:27.7955256Z may be inefficient, so if the workload permits, the user should prefer 2025-07-17T07:11:27.7955779Z to still apply mixed precision to those modules. 2025-07-17T07:11:27.7956038Z 2025-07-17T07:11:27.7956245Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-07-17T07:11:27.7956784Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-07-17T07:11:27.7957342Z modules will have FSDP applied to them separately with mixed precision 2025-07-17T07:11:27.7957870Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-07-17T07:11:27.7958158Z 2025-07-17T07:11:27.7958366Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-07-17T07:11:27.7958910Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-07-17T07:11:27.7959424Z its ``cast_root_forward_inputs`` takes precedence over its 2025-07-17T07:11:27.7959895Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-07-17T07:11:27.7960404Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-07-17T07:11:27.7960969Z sufficient for the typical case where each FSDP instance has the same 2025-07-17T07:11:27.7961544Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-07-17T07:11:27.7962068Z ``param_dtype`` at the beginning of the model's forward pass. 2025-07-17T07:11:27.7962377Z 2025-07-17T07:11:27.7962584Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-07-17T07:11:27.7963152Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-07-17T07:11:27.7963718Z values to configure casting inputs or not before each instance's 2025-07-17T07:11:27.7964227Z forward. In such a case, since the casts happen before each FSDP 2025-07-17T07:11:27.7964770Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-07-17T07:11:27.7965347Z submodules run before its FSDP submodules to avoid the activation dtype 2025-07-17T07:11:27.7965905Z being changed due to a different ``MixedPrecision`` configuration. 2025-07-17T07:11:27.7966244Z 2025-07-17T07:11:27.7966338Z Example:: 2025-07-17T07:11:27.7966483Z 2025-07-17T07:11:27.7966616Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:27.7967042Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-07-17T07:11:27.7967415Z >>> model[1] = FSDP( 2025-07-17T07:11:27.7967688Z >>> model[1], 2025-07-17T07:11:27.7968164Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-07-17T07:11:27.7968671Z >>> ) 2025-07-17T07:11:27.7968901Z >>> model = FSDP( 2025-07-17T07:11:27.7969164Z >>> model, 2025-07-17T07:11:27.7969653Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-07-17T07:11:27.7970170Z >>> ) 2025-07-17T07:11:27.7970312Z 2025-07-17T07:11:27.7970529Z The above shows a working example. On the other hand, if ``model[1]`` 2025-07-17T07:11:27.7971067Z were replaced with ``model[0]``, meaning that the submodule using 2025-07-17T07:11:27.7971601Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-07-17T07:11:27.7972360Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-07-17T07:11:27.7972791Z ones. 2025-07-17T07:11:27.7972907Z 2025-07-17T07:11:27.7972912Z 2025-07-17T07:11:27.7973178Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.7973546Z 2025-07-17T07:11:27.7974137Z 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. 2025-07-17T07:11:27.7975261Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.7975647Z 2025-07-17T07:11:27.7975953Z ``FullStateDictConfig`` is a config class meant to be used with 2025-07-17T07:11:27.7976482Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-07-17T07:11:27.7976991Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-07-17T07:11:27.7977539Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-07-17T07:11:27.7978083Z is meant to be used via the :func:`state_dict_type` context manager as 2025-07-17T07:11:27.7978475Z follows: 2025-07-17T07:11:27.7978608Z 2025-07-17T07:11:27.7978737Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:27.7979218Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T07:11:27.7979696Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-07-17T07:11:27.7980147Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-07-17T07:11:27.7980698Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-07-17T07:11:27.7981175Z >>> state = fsdp.state_dict() 2025-07-17T07:11:27.7981590Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-07-17T07:11:27.7982165Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-07-17T07:11:27.7982765Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-07-17T07:11:27.7983218Z >>> if dist.get_rank() == 0: 2025-07-17T07:11:27.7983597Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-07-17T07:11:27.7984039Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-07-17T07:11:27.7984415Z >>> model.load_state_dict(state_dict) 2025-07-17T07:11:27.7984868Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-07-17T07:11:27.7985474Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-07-17T07:11:27.7985936Z >>> fsdp = FSDP( 2025-07-17T07:11:27.7986166Z ... model, 2025-07-17T07:11:27.7986443Z ... device_id=torch.cuda.current_device(), 2025-07-17T07:11:27.7986797Z ... auto_wrap_policy=..., 2025-07-17T07:11:27.7987091Z ... sync_module_states=True, 2025-07-17T07:11:27.7987383Z ... ) 2025-07-17T07:11:27.7987718Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-07-17T07:11:27.7988052Z 2025-07-17T07:11:27.7988152Z Attributes: 2025-07-17T07:11:27.7988468Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-07-17T07:11:27.7988992Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-07-17T07:11:27.7989473Z ranks save the full state dict. (Default: ``False``) 2025-07-17T07:11:27.7989741Z 2025-07-17T07:11:27.7989991Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.7990428Z 2025-07-17T07:11:27.8053101Z 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=637. 2025-07-17T07:11:27.8054306Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8054949Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-07-17T07:11:27.8055374Z 2025-07-17T07:11:27.8055650Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-07-17T07:11:27.8056242Z The target module does not have to be a FSDP module. If the target 2025-07-17T07:11:27.8056781Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-07-17T07:11:27.8057109Z 2025-07-17T07:11:27.8057369Z .. note:: This API should be called for only the top-level (root) 2025-07-17T07:11:27.8057915Z module. 2025-07-17T07:11:27.8058059Z 2025-07-17T07:11:27.8058269Z .. note:: This API enables users to transparently use the conventional 2025-07-17T07:11:27.8058861Z ``state_dict`` API to take model checkpoints in cases where the 2025-07-17T07:11:27.8059386Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-07-17T07:11:27.8059954Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-07-17T07:11:27.8060511Z instances, while dispatching into `sharded_state_dict` implementation 2025-07-17T07:11:27.8060965Z for FSDP: 2025-07-17T07:11:27.8061116Z 2025-07-17T07:11:27.8061220Z Example:: 2025-07-17T07:11:27.8061359Z 2025-07-17T07:11:27.8061503Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:27.8061850Z >>> model = DDP(FSDP(...)) 2025-07-17T07:11:27.8062175Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:27.8062493Z >>> model, 2025-07-17T07:11:27.8062783Z >>> StateDictType.SHARDED_STATE_DICT, 2025-07-17T07:11:27.8063245Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-07-17T07:11:27.8063827Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-07-17T07:11:27.8064258Z >>> ) 2025-07-17T07:11:27.8064530Z >>> param_state_dict = model.state_dict() 2025-07-17T07:11:27.8064950Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-07-17T07:11:27.8065240Z 2025-07-17T07:11:27.8065337Z Args: 2025-07-17T07:11:27.8065582Z module (torch.nn.Module): Root module. 2025-07-17T07:11:27.8066082Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-07-17T07:11:27.8066686Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-07-17T07:11:27.8067155Z target ``state_dict_type``. 2025-07-17T07:11:27.8067635Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-07-17T07:11:27.8068142Z for the optimizer state dict. 2025-07-17T07:11:27.8068368Z 2025-07-17T07:11:27.8068467Z Returns: 2025-07-17T07:11:27.8068831Z A StateDictSettings that include the previous state_dict type and 2025-07-17T07:11:27.8069290Z configuration for the module. 2025-07-17T07:11:27.8069599Z 2025-07-17T07:11:27.8069958Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8070339Z 2025-07-17T07:11:27.8071152Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=795. 2025-07-17T07:11:27.8072499Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8073140Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-07-17T07:11:27.8073597Z 2025-07-17T07:11:27.8073914Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-07-17T07:11:27.8074494Z :meth:`set_state_dict_type` for the detail. 2025-07-17T07:11:27.8074750Z 2025-07-17T07:11:27.8074843Z Example:: 2025-07-17T07:11:27.8074981Z 2025-07-17T07:11:27.8075127Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:27.8075471Z >>> model = DDP(FSDP(...)) 2025-07-17T07:11:27.8075800Z >>> with FSDP.state_dict_type( 2025-07-17T07:11:27.8076118Z >>> model, 2025-07-17T07:11:27.8076411Z >>> StateDictType.SHARDED_STATE_DICT, 2025-07-17T07:11:27.8076743Z >>> ): 2025-07-17T07:11:27.8077011Z >>> checkpoint = model.state_dict() 2025-07-17T07:11:27.8077246Z 2025-07-17T07:11:27.8077341Z Args: 2025-07-17T07:11:27.8077583Z module (torch.nn.Module): Root module. 2025-07-17T07:11:27.8078104Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-07-17T07:11:27.8078702Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-07-17T07:11:27.8079283Z configuration for the target ``state_dict_type``. 2025-07-17T07:11:27.8079808Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-07-17T07:11:27.8080377Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-07-17T07:11:27.8080774Z 2025-07-17T07:11:27.8081156Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8081545Z 2025-07-17T07:11:27.8109970Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1808. 2025-07-17T07:11:27.8111188Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8111582Z 2025-07-17T07:11:27.8111835Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-07-17T07:11:27.8112194Z 2025-07-17T07:11:27.8112391Z The given state-dict can be transformed to one of three types: 2025-07-17T07:11:27.8113008Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-07-17T07:11:27.8113562Z 2025-07-17T07:11:27.8113811Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-07-17T07:11:27.8114384Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-07-17T07:11:27.8114799Z avoid OOM. 2025-07-17T07:11:27.8114935Z 2025-07-17T07:11:27.8115168Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-07-17T07:11:27.8115733Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-07-17T07:11:27.8116133Z memory. 2025-07-17T07:11:27.8116259Z 2025-07-17T07:11:27.8116479Z For local state_dict, no transformation will be performed. But a state 2025-07-17T07:11:27.8117067Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-07-17T07:11:27.8117545Z nature (this is not supported yet). 2025-07-17T07:11:27.8117756Z 2025-07-17T07:11:27.8117849Z Example:: 2025-07-17T07:11:27.8117982Z 2025-07-17T07:11:27.8118106Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:27.8118582Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T07:11:27.8119087Z >>> from torch.distributed.fsdp import StateDictType 2025-07-17T07:11:27.8119563Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-07-17T07:11:27.8120060Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-07-17T07:11:27.8120479Z >>> # Save a checkpoint 2025-07-17T07:11:27.8120755Z >>> model, optim = ... 2025-07-17T07:11:27.8121021Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:27.8121311Z >>> model, 2025-07-17T07:11:27.8121651Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T07:11:27.8122002Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T07:11:27.8122393Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T07:11:27.8122745Z >>> ) 2025-07-17T07:11:27.8122966Z >>> state_dict = model.state_dict() 2025-07-17T07:11:27.8123357Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-07-17T07:11:27.8123788Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-07-17T07:11:27.8124133Z >>> # Load a checkpoint 2025-07-17T07:11:27.8124413Z >>> model, optim = ... 2025-07-17T07:11:27.8124748Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-07-17T07:11:27.8125124Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:27.8125399Z >>> model, 2025-07-17T07:11:27.8125657Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T07:11:27.8126014Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T07:11:27.8126450Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T07:11:27.8126792Z >>> ) 2025-07-17T07:11:27.8127030Z >>> model.load_state_dict(state_dict) 2025-07-17T07:11:27.8127396Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-07-17T07:11:27.8127834Z >>> model, optim, optim_state_dict 2025-07-17T07:11:27.8128147Z >>> ) 2025-07-17T07:11:27.8128382Z >>> optim.load_state_dict(optim_state_dict) 2025-07-17T07:11:27.8128629Z 2025-07-17T07:11:27.8128711Z Args: 2025-07-17T07:11:27.8129031Z model (torch.nn.Module): Root module (which may or may not be a 2025-07-17T07:11:27.8129556Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-07-17T07:11:27.8130002Z were passed into the optimizer ``optim``. 2025-07-17T07:11:27.8130434Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-07-17T07:11:27.8130830Z parameters. 2025-07-17T07:11:27.8131181Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-07-17T07:11:27.8131729Z transform. If the value is None, optim.state_dict() will be used. ( 2025-07-17T07:11:27.8132160Z Default: ``None``) 2025-07-17T07:11:27.8132563Z group (dist.ProcessGroup): Model's process group across which parameters 2025-07-17T07:11:27.8133117Z are sharded or ``None`` if using the default process group. ( 2025-07-17T07:11:27.8133522Z Default: ``None``) 2025-07-17T07:11:27.8133689Z 2025-07-17T07:11:27.8133788Z Returns: 2025-07-17T07:11:27.8134111Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-07-17T07:11:27.8134591Z ``model``. The sharding of the optimizer state is based on 2025-07-17T07:11:27.8134980Z ``state_dict_type``. 2025-07-17T07:11:27.8135144Z 2025-07-17T07:11:27.8135471Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8135855Z 2025-07-17T07:11:27.8136705Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1906. 2025-07-17T07:11:27.8137940Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8138326Z 2025-07-17T07:11:27.8138700Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-07-17T07:11:27.8139177Z 2025-07-17T07:11:27.8139356Z Given a ``optim_state_dict`` that is transformed through 2025-07-17T07:11:27.8139838Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-07-17T07:11:27.8140382Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-07-17T07:11:27.8140904Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-07-17T07:11:27.8141207Z 2025-07-17T07:11:27.8141332Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:27.8141808Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T07:11:27.8142323Z >>> from torch.distributed.fsdp import StateDictType 2025-07-17T07:11:27.8142825Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-07-17T07:11:27.8143310Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-07-17T07:11:27.8143733Z >>> # Save a checkpoint 2025-07-17T07:11:27.8144010Z >>> model, optim = ... 2025-07-17T07:11:27.8144283Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:27.8144590Z >>> model, 2025-07-17T07:11:27.8144850Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T07:11:27.8145194Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T07:11:27.8145591Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T07:11:27.8145943Z >>> ) 2025-07-17T07:11:27.8146166Z >>> state_dict = model.state_dict() 2025-07-17T07:11:27.8146503Z >>> original_osd = optim.state_dict() 2025-07-17T07:11:27.8146858Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-07-17T07:11:27.8147219Z >>> model, 2025-07-17T07:11:27.8147434Z >>> optim, 2025-07-17T07:11:27.8147687Z >>> optim_state_dict=original_osd 2025-07-17T07:11:27.8147991Z >>> ) 2025-07-17T07:11:27.8148260Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-07-17T07:11:27.8148670Z >>> # Load a checkpoint 2025-07-17T07:11:27.8148950Z >>> model, optim = ... 2025-07-17T07:11:27.8149267Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-07-17T07:11:27.8149648Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:27.8149936Z >>> model, 2025-07-17T07:11:27.8150186Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T07:11:27.8150549Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T07:11:27.8150944Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T07:11:27.8151282Z >>> ) 2025-07-17T07:11:27.8151525Z >>> model.load_state_dict(state_dict) 2025-07-17T07:11:27.8151907Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-07-17T07:11:27.8152293Z >>> model, optim, optim_state_dict 2025-07-17T07:11:27.8152596Z >>> ) 2025-07-17T07:11:27.8152845Z >>> optim.load_state_dict(optim_state_dict) 2025-07-17T07:11:27.8153082Z 2025-07-17T07:11:27.8153176Z Args: 2025-07-17T07:11:27.8153488Z model (torch.nn.Module): Root module (which may or may not be a 2025-07-17T07:11:27.8154004Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-07-17T07:11:27.8154455Z were passed into the optimizer ``optim``. 2025-07-17T07:11:27.8154870Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-07-17T07:11:27.8155264Z parameters. 2025-07-17T07:11:27.8155624Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-07-17T07:11:27.8156165Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-07-17T07:11:27.8156668Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-07-17T07:11:27.8157160Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-07-17T07:11:27.8157657Z load_directly (bool): If this is set to True, this API will also 2025-07-17T07:11:27.8158159Z call optim.load_state_dict(result) before returning the result. 2025-07-17T07:11:27.8158708Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-07-17T07:11:27.8159152Z (Default: ``False``) 2025-07-17T07:11:27.8159575Z group (dist.ProcessGroup): Model's process group across which parameters 2025-07-17T07:11:27.8160113Z are sharded or ``None`` if using the default process group. ( 2025-07-17T07:11:27.8160515Z Default: ``None``) 2025-07-17T07:11:27.8160681Z 2025-07-17T07:11:27.8160945Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8161313Z 2025-07-17T07:11:27.8581128Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=129. 2025-07-17T07:11:27.8582166Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8582723Z 2025-07-17T07:11:27.8582949Z RemoteModule instance can only be created after RPC initialization. 2025-07-17T07:11:27.8583290Z 2025-07-17T07:11:27.8583504Z It creates a user-specified module on a specified remote node. 2025-07-17T07:11:27.8584050Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-07-17T07:11:27.8584499Z executed on the remote node. 2025-07-17T07:11:27.8584923Z It takes care of autograd recording to ensure the backward pass propagates 2025-07-17T07:11:27.8585435Z gradients back to the corresponding remote module. 2025-07-17T07:11:27.8586048Z It can be shared across processors using `RPC framework `__, 2025-07-17T07:11:27.8586720Z without incurring any overheads of copying the actual module, 2025-07-17T07:11:27.8587228Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-07-17T07:11:27.8587711Z pointing to the remote module. 2025-07-17T07:11:27.8587901Z 2025-07-17T07:11:27.8588098Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-07-17T07:11:27.8588622Z the ``forward`` method of the module returned by the ``module_cls``. 2025-07-17T07:11:27.8588940Z 2025-07-17T07:11:27.8589342Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-07-17T07:11:27.8589911Z 2025-07-17T07:11:27.8590224Z Particularly, to create a hybrid model, typically the local modules should be 2025-07-17T07:11:27.8591017Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-07-17T07:11:27.8591612Z Hybrid Example: 2025-07-17T07:11:27.8591861Z >>> class HybridModel(nn.Module): 2025-07-17T07:11:27.8592206Z >>> def __init__(self) -> None: 2025-07-17T07:11:27.8592542Z >>> nn.Module.__init__(self) 2025-07-17T07:11:27.8592899Z >>> self.remote_embedding = RemoteModule(...) 2025-07-17T07:11:27.8593291Z >>> self.local_linear = nn.Linear(...) 2025-07-17T07:11:27.8593526Z 2025-07-17T07:11:27.8593735Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-07-17T07:11:27.8594296Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-07-17T07:11:27.8594882Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-07-17T07:11:27.8595338Z ``def forward(input: Tensor) -> Tensor:`` and 2025-07-17T07:11:27.8595743Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-07-17T07:11:27.8596012Z 2025-07-17T07:11:27.8596110Z .. note:: 2025-07-17T07:11:27.8596376Z If the remote module is placed on a cuda device, 2025-07-17T07:11:27.8596865Z any input CPU tensors will be automatically moved to the same cuda device, 2025-07-17T07:11:27.8597609Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-07-17T07:11:27.8598275Z 2025-07-17T07:11:27.8598408Z Args: 2025-07-17T07:11:27.8598865Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T07:11:27.8599584Z The device can be a local device or a remote device specified by one of the following remote 2025-07-17T07:11:27.8600076Z formats: 2025-07-17T07:11:27.8600221Z 2025-07-17T07:11:27.8600362Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-07-17T07:11:27.8600770Z 2. "/" (ex: "trainer0/cuda:0"). 2025-07-17T07:11:27.8601031Z 2025-07-17T07:11:27.8601290Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T07:11:27.8601765Z module_cls (nn.Module): For example, 2025-07-17T07:11:27.8602098Z >>> class MyModule(nn.Module): 2025-07-17T07:11:27.8602415Z >>> def forward(input): 2025-07-17T07:11:27.8602707Z >>> return input + 1 2025-07-17T07:11:27.8602993Z >>> 2025-07-17T07:11:27.8603220Z >>> module_cls = MyModule 2025-07-17T07:11:27.8603662Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-07-17T07:11:27.8604179Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-07-17T07:11:27.8604781Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-07-17T07:11:27.8605430Z to be created. The type object should be decorated by @torch.jit.interface. 2025-07-17T07:11:27.8606002Z If not provided, the generated RemoteModule is not torchscript-able. 2025-07-17T07:11:27.8606583Z Warning, this is an experimental API and susceptible to frequent changes. 2025-07-17T07:11:27.8606941Z 2025-07-17T07:11:27.8607037Z Returns: 2025-07-17T07:11:27.8607391Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T07:11:27.8607988Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-07-17T07:11:27.8608657Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T07:11:27.8609201Z on the user-provided module on the remote side. 2025-07-17T07:11:27.8609453Z 2025-07-17T07:11:27.8609545Z Example:: 2025-07-17T07:11:27.8609938Z Run the following code in two different processes: 2025-07-17T07:11:27.8610228Z 2025-07-17T07:11:27.8610359Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:27.8610667Z >>> # On worker 0: 2025-07-17T07:11:27.8610921Z >>> import torch 2025-07-17T07:11:27.8611209Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:27.8611544Z >>> from torch import nn, Tensor 2025-07-17T07:11:27.8611979Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T07:11:27.8612412Z >>> 2025-07-17T07:11:27.8612676Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T07:11:27.8613043Z >>> remote_linear_module = RemoteModule( 2025-07-17T07:11:27.8613404Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T07:11:27.8613739Z >>> ) 2025-07-17T07:11:27.8613958Z >>> input = torch.randn(128, 20) 2025-07-17T07:11:27.8614325Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-07-17T07:11:27.8614702Z >>> ret = ret_fut.wait() 2025-07-17T07:11:27.8614974Z >>> rpc.shutdown() 2025-07-17T07:11:27.8615144Z 2025-07-17T07:11:27.8615301Z >>> # On worker 1: 2025-07-17T07:11:27.8615554Z >>> import torch 2025-07-17T07:11:27.8615822Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:27.8616146Z >>> 2025-07-17T07:11:27.8616405Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T07:11:27.8616755Z >>> rpc.shutdown() 2025-07-17T07:11:27.8616909Z 2025-07-17T07:11:27.8617160Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8617543Z 2025-07-17T07:11:27.8618243Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=506. 2025-07-17T07:11:27.8619321Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8619695Z 2025-07-17T07:11:27.8620034Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-07-17T07:11:27.8620480Z 2025-07-17T07:11:27.8620804Z This alternate initialization method can be particularly useful if we want to create multiple 2025-07-17T07:11:27.8621570Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-07-17T07:11:27.8622003Z 2025-07-17T07:11:27.8622291Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-07-17T07:11:27.8622871Z which is not supported. The recommended way is as follows: 2025-07-17T07:11:27.8623157Z 2025-07-17T07:11:27.8623271Z 1. the sender creates a RemoteModule; 2025-07-17T07:11:27.8623644Z 2. the sender sends its ``module_rref`` over RPC; 2025-07-17T07:11:27.8624241Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-07-17T07:11:27.8624746Z 2025-07-17T07:11:27.8624835Z Example:: 2025-07-17T07:11:27.8625115Z Run the following code in two different processes: 2025-07-17T07:11:27.8625392Z 2025-07-17T07:11:27.8625509Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:27.8625827Z >>> # On worker 0: 2025-07-17T07:11:27.8626063Z >>> import torch 2025-07-17T07:11:27.8626346Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:27.8626689Z >>> from torch import nn, Tensor 2025-07-17T07:11:27.8627106Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T07:11:27.8627533Z >>> 2025-07-17T07:11:27.8627787Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T07:11:27.8628139Z >>> remote_module = RemoteModule( 2025-07-17T07:11:27.8628481Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T07:11:27.8628802Z >>> ) 2025-07-17T07:11:27.8629032Z >>> 2025-07-17T07:11:27.8629260Z >>> remote_module1 = rpc.rpc_sync( 2025-07-17T07:11:27.8629568Z >>> "worker1/cpu", 2025-07-17T07:11:27.8629866Z >>> RemoteModule.init_from_module_rref, 2025-07-17T07:11:27.8630302Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-07-17T07:11:27.8630659Z >>> ) 2025-07-17T07:11:27.8630877Z >>> rpc.shutdown() 2025-07-17T07:11:27.8631031Z 2025-07-17T07:11:27.8631121Z >>> # On worker 1: 2025-07-17T07:11:27.8631366Z >>> import torch 2025-07-17T07:11:27.8631643Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:27.8631950Z >>> 2025-07-17T07:11:27.8632210Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T07:11:27.8632563Z >>> rpc.shutdown() 2025-07-17T07:11:27.8632717Z 2025-07-17T07:11:27.8632798Z Args: 2025-07-17T07:11:27.8633214Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T07:11:27.8633929Z The device can be a local device or a remote device specified by one of the following remote 2025-07-17T07:11:27.8634436Z formats: 2025-07-17T07:11:27.8634570Z 2025-07-17T07:11:27.8634708Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-07-17T07:11:27.8635119Z 2. "/" (ex: "trainer0/cuda:0"). 2025-07-17T07:11:27.8635381Z 2025-07-17T07:11:27.8635638Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T07:11:27.8636263Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-07-17T07:11:27.8636740Z the created remote module. 2025-07-17T07:11:27.8637227Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-07-17T07:11:27.8637870Z to be created. The type object should be decorated by @torch.jit.interface. 2025-07-17T07:11:27.8638441Z If not provided, the generated RemoteModule is not torchscript-able. 2025-07-17T07:11:27.8639017Z Warning, this is an experimental API and susceptible to frequent changes. 2025-07-17T07:11:27.8639376Z 2025-07-17T07:11:27.8639473Z Returns: 2025-07-17T07:11:27.8639842Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T07:11:27.8640430Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-07-17T07:11:27.8641060Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T07:11:27.8641598Z on the user-provided module on the remote side. 2025-07-17T07:11:27.8641850Z 2025-07-17T07:11:27.8642103Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8642487Z 2025-07-17T07:11:27.8643140Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=598. 2025-07-17T07:11:27.8644126Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8644518Z 2025-07-17T07:11:27.8644786Z A RemoteModule instance can only be created after RPC initialization. 2025-07-17T07:11:27.8645131Z 2025-07-17T07:11:27.8645338Z It creates a user-specified module on a specified remote node. 2025-07-17T07:11:27.8645888Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-07-17T07:11:27.8646339Z executed on the remote node. 2025-07-17T07:11:27.8646781Z It takes care of autograd recording to ensure the backward pass propagates 2025-07-17T07:11:27.8647301Z gradients back to the corresponding remote module. 2025-07-17T07:11:27.8647568Z 2025-07-17T07:11:27.8647786Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-07-17T07:11:27.8648347Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-07-17T07:11:27.8648936Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-07-17T07:11:27.8649510Z and ``forward`` are the same as the ``forward`` method of the module 2025-07-17T07:11:27.8649960Z returned by the ``module_cls``. 2025-07-17T07:11:27.8650175Z 2025-07-17T07:11:27.8650376Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-07-17T07:11:27.8651023Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-07-17T07:11:27.8651621Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-07-17T07:11:27.8651979Z 2025-07-17T07:11:27.8652103Z | ``def forward(input: Tensor) -> Tensor:`` 2025-07-17T07:11:27.8652506Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-07-17T07:11:27.8652793Z 2025-07-17T07:11:27.8652891Z Args: 2025-07-17T07:11:27.8653300Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T07:11:27.8654067Z The format should be "/", where the device field can be parsed as torch.device type. 2025-07-17T07:11:27.8654685Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-07-17T07:11:27.8655252Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T07:11:27.8655913Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-07-17T07:11:27.8656299Z 2025-07-17T07:11:27.8656411Z >>> class MyModule(nn.Module): 2025-07-17T07:11:27.8656741Z >>> def forward(input): 2025-07-17T07:11:27.8657037Z >>> return input + 1 2025-07-17T07:11:27.8657331Z >>> 2025-07-17T07:11:27.8657565Z >>> module_cls = MyModule 2025-07-17T07:11:27.8657757Z 2025-07-17T07:11:27.8657955Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-07-17T07:11:27.8658479Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-07-17T07:11:27.8658800Z 2025-07-17T07:11:27.8658887Z Returns: 2025-07-17T07:11:27.8659256Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T07:11:27.8659843Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-07-17T07:11:27.8660474Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T07:11:27.8661013Z on the user-provided module on the remote side. 2025-07-17T07:11:27.8661266Z 2025-07-17T07:11:27.8661356Z Example:: 2025-07-17T07:11:27.8661634Z Run the following code in two different processes: 2025-07-17T07:11:27.8661907Z 2025-07-17T07:11:27.8662019Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:27.8662341Z >>> # On worker 0: 2025-07-17T07:11:27.8662577Z >>> import torch 2025-07-17T07:11:27.8662860Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:27.8663201Z >>> from torch import nn, Tensor 2025-07-17T07:11:27.8663617Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T07:11:27.8664037Z >>> 2025-07-17T07:11:27.8664292Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T07:11:27.8664656Z >>> remote_linear_module = RemoteModule( 2025-07-17T07:11:27.8665060Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T07:11:27.8665385Z >>> ) 2025-07-17T07:11:27.8665600Z >>> input = torch.randn(128, 20) 2025-07-17T07:11:27.8665970Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-07-17T07:11:27.8666349Z >>> ret = ret_fut.wait() 2025-07-17T07:11:27.8666628Z >>> rpc.shutdown() 2025-07-17T07:11:27.8666783Z 2025-07-17T07:11:27.8666872Z >>> # On worker 1: 2025-07-17T07:11:27.8667121Z >>> import torch 2025-07-17T07:11:27.8667398Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:27.8667480Z >>> 2025-07-17T07:11:27.8667617Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T07:11:27.8667721Z >>> rpc.shutdown() 2025-07-17T07:11:27.8667725Z 2025-07-17T07:11:27.8667916Z Furthermore, a more practical example that is combined with 2025-07-17T07:11:27.8668409Z `DistributedDataParallel `__ (DDP) 2025-07-17T07:11:27.8668786Z can be found in this `tutorial `__. 2025-07-17T07:11:27.8668792Z 2025-07-17T07:11:27.8669100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8669106Z 2025-07-17T07:11:27.8741640Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=129. 2025-07-17T07:11:27.8741904Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8741908Z 2025-07-17T07:11:27.8742162Z DistributedOptimizer takes remote references to parameters scattered 2025-07-17T07:11:27.8742400Z across workers and applies the given optimizer locally for each parameter. 2025-07-17T07:11:27.8742404Z 2025-07-17T07:11:27.8742639Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-07-17T07:11:27.8742811Z to retrieve the gradients for specific parameters. 2025-07-17T07:11:27.8742815Z 2025-07-17T07:11:27.8742907Z Concurrent calls to 2025-07-17T07:11:27.8743124Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-07-17T07:11:27.8743267Z either from the same or different clients, will 2025-07-17T07:11:27.8743493Z be serialized on each worker -- as each worker's optimizer can only work 2025-07-17T07:11:27.8743727Z on one set of gradients at a time. However, there is no guarantee that 2025-07-17T07:11:27.8743970Z the full forward-backward-optimizer sequence will execute for one client 2025-07-17T07:11:27.8744195Z at a time. This means that the gradients being applied may not correspond 2025-07-17T07:11:27.8744419Z to the latest forward pass executed on a given worker. Also, there is no 2025-07-17T07:11:27.8744534Z guaranteed ordering across workers. 2025-07-17T07:11:27.8744539Z 2025-07-17T07:11:27.8744814Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-07-17T07:11:27.8745046Z by default, so that optimizer updates are not blocked by the Python Global 2025-07-17T07:11:27.8745300Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-07-17T07:11:27.8745542Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-07-17T07:11:27.8745789Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-07-17T07:11:27.8745911Z for your own custom optimizers. 2025-07-17T07:11:27.8745916Z 2025-07-17T07:11:27.8745997Z Args: 2025-07-17T07:11:27.8746193Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-07-17T07:11:27.8746313Z instantiate on each worker. 2025-07-17T07:11:27.8746521Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-07-17T07:11:27.8746623Z to optimize. 2025-07-17T07:11:27.8746838Z args: arguments to pass to the optimizer constructor on each worker. 2025-07-17T07:11:27.8747059Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-07-17T07:11:27.8747152Z 2025-07-17T07:11:27.8747266Z Example:: 2025-07-17T07:11:27.8747378Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:27.8747545Z >>> import torch.distributed.autograd as dist_autograd 2025-07-17T07:11:27.8747682Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:27.8747785Z >>> from torch import optim 2025-07-17T07:11:27.8747974Z >>> from torch.distributed.optim import DistributedOptimizer 2025-07-17T07:11:27.8748071Z >>> 2025-07-17T07:11:27.8748204Z >>> with dist_autograd.context() as context_id: 2025-07-17T07:11:27.8748313Z >>> # Forward pass. 2025-07-17T07:11:27.8748512Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-07-17T07:11:27.8748706Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-07-17T07:11:27.8748844Z >>> loss = rref1.to_here() + rref2.to_here() 2025-07-17T07:11:27.8748969Z >>> 2025-07-17T07:11:27.8749066Z >>> # Backward pass. 2025-07-17T07:11:27.8749226Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-07-17T07:11:27.8749308Z >>> 2025-07-17T07:11:27.8749397Z >>> # Optimizer. 2025-07-17T07:11:27.8749604Z >>> dist_optim = DistributedOptimizer( 2025-07-17T07:11:27.8749695Z >>> optim.SGD, 2025-07-17T07:11:27.8749800Z >>> [rref1, rref2], 2025-07-17T07:11:27.8749885Z >>> lr=0.05, 2025-07-17T07:11:27.8749968Z >>> ) 2025-07-17T07:11:27.8750093Z >>> dist_optim.step(context_id) 2025-07-17T07:11:27.8750098Z 2025-07-17T07:11:27.8750253Z __ https://github.com/pytorch/tutorials/pull/1465 2025-07-17T07:11:27.8750258Z 2025-07-17T07:11:27.8750510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8750514Z 2025-07-17T07:11:27.8754046Z 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. 2025-07-17T07:11:27.8754314Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8754318Z 2025-07-17T07:11:27.8754724Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-07-17T07:11:27.8754874Z This optimizer runs local optimizer at every step. 2025-07-17T07:11:27.8755220Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-07-17T07:11:27.8755224Z 2025-07-17T07:11:27.8755303Z Args: 2025-07-17T07:11:27.8755404Z optim: The local optimizer. 2025-07-17T07:11:27.8755634Z averager: A model averager instance to run post-localSGD algorithm. 2025-07-17T07:11:27.8755638Z 2025-07-17T07:11:27.8755731Z Example:: 2025-07-17T07:11:27.8755735Z 2025-07-17T07:11:27.8755858Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:27.8755957Z >>> import torch 2025-07-17T07:11:27.8756074Z >>> import torch.distributed as dist 2025-07-17T07:11:27.8756358Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-07-17T07:11:27.8756461Z >>> import torch.nn as nn 2025-07-17T07:11:27.8756657Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-07-17T07:11:27.8756940Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-07-17T07:11:27.8757043Z >>> PostLocalSGDState, 2025-07-17T07:11:27.8757141Z >>> post_localSGD_hook, 2025-07-17T07:11:27.8757236Z >>> ) 2025-07-17T07:11:27.8757317Z >>> 2025-07-17T07:11:27.8757470Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T07:11:27.8757622Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T07:11:27.8757705Z >>> ) 2025-07-17T07:11:27.8757801Z >>> 2025-07-17T07:11:27.8757947Z >>> # Register a post-localSGD communication hook. 2025-07-17T07:11:27.8758247Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-07-17T07:11:27.8758484Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T07:11:27.8758566Z >>> 2025-07-17T07:11:27.8758782Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-07-17T07:11:27.8759048Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-07-17T07:11:27.8759212Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T07:11:27.8759437Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-07-17T07:11:27.8759550Z >>> opt = PostLocalSGDOptimizer( 2025-07-17T07:11:27.8759651Z >>> optim=local_optim, 2025-07-17T07:11:27.8759909Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-07-17T07:11:27.8759989Z >>> ) 2025-07-17T07:11:27.8760068Z >>> 2025-07-17T07:11:27.8760301Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-07-17T07:11:27.8760634Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-07-17T07:11:27.8761025Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-07-17T07:11:27.8761182Z >>> for step in range(0, 200): 2025-07-17T07:11:27.8761277Z >>> opt.zero_grad() 2025-07-17T07:11:27.8761397Z >>> loss = loss_fn(output, labels) 2025-07-17T07:11:27.8761494Z >>> loss.backward() 2025-07-17T07:11:27.8761583Z >>> opt.step() 2025-07-17T07:11:27.8761587Z 2025-07-17T07:11:27.8761857Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8761861Z 2025-07-17T07:11:27.8852323Z 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. 2025-07-17T07:11:27.8852603Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.8852614Z 2025-07-17T07:11:27.8853021Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-07-17T07:11:27.8853026Z 2025-07-17T07:11:27.8853293Z The sharing is done as described by `ZeRO `_. 2025-07-17T07:11:27.8853297Z 2025-07-17T07:11:27.8853444Z The local optimizer instance in each rank is only 2025-07-17T07:11:27.8853688Z responsible for updating approximately ``1 / world_size`` parameters and 2025-07-17T07:11:27.8853904Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-07-17T07:11:27.8854149Z parameters are updated locally, each rank will broadcast its parameters to 2025-07-17T07:11:27.8854332Z all other peers to keep all model replicas in the same state. 2025-07-17T07:11:27.8854540Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-07-17T07:11:27.8854798Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-07-17T07:11:27.8854911Z memory consumption. 2025-07-17T07:11:27.8854938Z 2025-07-17T07:11:27.8855236Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-07-17T07:11:27.8855483Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-07-17T07:11:27.8855728Z not divided among ranks. The partition is arbitrary and might not match the 2025-07-17T07:11:27.8855857Z the parameter registration or usage order. 2025-07-17T07:11:27.8855861Z 2025-07-17T07:11:27.8855964Z Arguments: 2025-07-17T07:11:27.8856154Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-07-17T07:11:27.8856354Z or :class:`dict` s giving all parameters, which will be sharded 2025-07-17T07:11:27.8856447Z across ranks. 2025-07-17T07:11:27.8856452Z 2025-07-17T07:11:27.8856541Z Keyword Args: 2025-07-17T07:11:27.8856778Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-07-17T07:11:27.8856868Z optimizer. 2025-07-17T07:11:27.8857081Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-07-17T07:11:27.8857376Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-07-17T07:11:27.8857526Z :meth:`torch.distributed.init_process_group`). 2025-07-17T07:11:27.8857771Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-07-17T07:11:27.8857983Z packed into buckets to speed up communication, and ``param.data`` 2025-07-17T07:11:27.8858185Z fields point to bucket views at different offsets; if ``False``, 2025-07-17T07:11:27.8858405Z each individual parameter is communicated separately, and each 2025-07-17T07:11:27.8858554Z ``params.data`` stays intact (default: ``False``). 2025-07-17T07:11:27.8858746Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-07-17T07:11:27.8858962Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-07-17T07:11:27.8859176Z synchronization; this requires (1) either a functional optimizer 2025-07-17T07:11:27.8859409Z for the ``optimizer_class`` argument or one with a functional 2025-07-17T07:11:27.8859584Z equivalent and (2) registering a DDP communication hook 2025-07-17T07:11:27.8859877Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-07-17T07:11:27.8860057Z parameters are packed into buckets matching those in 2025-07-17T07:11:27.8860210Z :class:`DistributedDataParallel`, meaning that the 2025-07-17T07:11:27.8860357Z ``parameters_as_bucket_view`` argument is ignored. 2025-07-17T07:11:27.8860562Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-07-17T07:11:27.8860653Z (per normal). 2025-07-17T07:11:27.8860752Z (default: ``False``) 2025-07-17T07:11:27.8860976Z **defaults: any trailing arguments, which are forwarded to the local 2025-07-17T07:11:27.8861066Z optimizer. 2025-07-17T07:11:27.8861071Z 2025-07-17T07:11:27.8861183Z Example:: 2025-07-17T07:11:27.8861188Z 2025-07-17T07:11:27.8861288Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.8861385Z >>> import torch.nn as nn 2025-07-17T07:11:27.8861598Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-07-17T07:11:27.8861802Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-07-17T07:11:27.8862028Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-07-17T07:11:27.8862153Z >>> ddp = DDP(model, device_ids=[rank]) 2025-07-17T07:11:27.8862268Z >>> opt = ZeroRedundancyOptimizer( 2025-07-17T07:11:27.8862379Z >>> ddp.parameters(), 2025-07-17T07:11:27.8862499Z >>> optimizer_class=torch.optim.Adam, 2025-07-17T07:11:27.8862583Z >>> lr=0.01 2025-07-17T07:11:27.8862677Z >>> ) 2025-07-17T07:11:27.8862780Z >>> ddp(inputs).sum().backward() 2025-07-17T07:11:27.8862868Z >>> opt.step() 2025-07-17T07:11:27.8862872Z 2025-07-17T07:11:27.8862970Z .. warning:: 2025-07-17T07:11:27.8863178Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-07-17T07:11:27.8863322Z passed-in parameters are the same dense type. 2025-07-17T07:11:27.8863339Z 2025-07-17T07:11:27.8863423Z .. warning:: 2025-07-17T07:11:27.8863635Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-07-17T07:11:27.8863848Z the way that overlapping :class:`DistributedDataParallel` with 2025-07-17T07:11:27.8864080Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-07-17T07:11:27.8864292Z two or three training iterations do not perform parameter updates in 2025-07-17T07:11:27.8864493Z the optimizer step, depending on if ``static_graph=False`` or 2025-07-17T07:11:27.8864677Z ``static_graph=True``, respectively. This is because it needs 2025-07-17T07:11:27.8864861Z information about the gradient bucketing strategy used by 2025-07-17T07:11:27.8865097Z :class:`DistributedDataParallel`, which is not finalized until the 2025-07-17T07:11:27.8865297Z second forward pass if ``static_graph=False`` or until the third 2025-07-17T07:11:27.8865567Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-07-17T07:11:27.8865672Z is to prepend dummy inputs. 2025-07-17T07:11:27.8865677Z 2025-07-17T07:11:27.8865936Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-07-17T07:11:27.8865941Z 2025-07-17T07:11:27.8866206Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.8866210Z 2025-07-17T07:11:27.9104653Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=29. 2025-07-17T07:11:27.9104944Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.9104949Z 2025-07-17T07:11:27.9105178Z Custom reducer class that can be used to specify a custom operation that 2025-07-17T07:11:27.9105360Z reduces losses of multiple microbatches into one value. 2025-07-17T07:11:27.9105460Z 2025-07-17T07:11:27.9105546Z Example: 2025-07-17T07:11:27.9105641Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.9105761Z >>> sum_reducer = _CustomReducer( 2025-07-17T07:11:27.9105858Z >>> torch.tensor(0.0), 2025-07-17T07:11:27.9106033Z >>> lambda a, b: a + b 2025-07-17T07:11:27.9106128Z >>> ) 2025-07-17T07:11:27.9106132Z 2025-07-17T07:11:27.9106386Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.9106390Z 2025-07-17T07:11:27.9542511Z 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. 2025-07-17T07:11:27.9542850Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.9542855Z 2025-07-17T07:11:27.9543099Z A decorator for a function indicating that the return value of the function 2025-07-17T07:11:27.9543307Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-07-17T07:11:27.9543572Z function can run asynchronously on the RPC callee. More specifically, the 2025-07-17T07:11:27.9543810Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-07-17T07:11:27.9544051Z function and installs subsequent processing steps as a callback to that 2025-07-17T07:11:27.9544297Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-07-17T07:11:27.9544498Z from the :class:`~torch.futures.Future` when completed and send the 2025-07-17T07:11:27.9544690Z value back as the RPC response. That also means the returned 2025-07-17T07:11:27.9544918Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-07-17T07:11:27.9545141Z sent through RPC. This decorator is useful when the wrapped function's 2025-07-17T07:11:27.9545350Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-07-17T07:11:27.9545574Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-07-17T07:11:27.9545582Z 2025-07-17T07:11:27.9545818Z .. note:: To enable asynchronous execution, applications must pass the 2025-07-17T07:11:27.9546048Z function object returned by this decorator to RPC APIs. If RPC detected 2025-07-17T07:11:27.9546271Z attributes installed by this decorator, it knows that this function 2025-07-17T07:11:27.9546465Z returns a ``Future`` object and will handle that accordingly. 2025-07-17T07:11:27.9546678Z However, this does not mean this decorator has to be outmost one when 2025-07-17T07:11:27.9546902Z defining a function. For example, when combined with ``@staticmethod`` 2025-07-17T07:11:27.9547124Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-07-17T07:11:27.9547342Z inner decorator to allow the target function be recognized as a static 2025-07-17T07:11:27.9547580Z or class function. This target function can still execute asynchronously 2025-07-17T07:11:27.9547805Z because, when accessed, the static or class method preserves attributes 2025-07-17T07:11:27.9548078Z installed by ``@rpc.functions.async_execution``. 2025-07-17T07:11:27.9548083Z 2025-07-17T07:11:27.9548087Z 2025-07-17T07:11:27.9548195Z Example:: 2025-07-17T07:11:27.9548398Z The returned :class:`~torch.futures.Future` object can come from 2025-07-17T07:11:27.9548531Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-07-17T07:11:27.9548766Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-07-17T07:11:27.9548940Z constructor. The example below shows directly using the 2025-07-17T07:11:27.9549084Z :class:`~torch.futures.Future` returned by 2025-07-17T07:11:27.9549203Z :meth:`~torch.futures.Future.then`. 2025-07-17T07:11:27.9549208Z 2025-07-17T07:11:27.9549328Z >>> from torch.distributed import rpc 2025-07-17T07:11:27.9549426Z >>> 2025-07-17T07:11:27.9549538Z >>> # omitting setup and shutdown RPC 2025-07-17T07:11:27.9549620Z >>> 2025-07-17T07:11:27.9549728Z >>> # On all workers 2025-07-17T07:11:27.9549896Z >>> @rpc.functions.async_execution 2025-07-17T07:11:27.9550006Z >>> def async_add_chained(to, x, y, z): 2025-07-17T07:11:27.9550208Z >>> # This function runs on "worker1" and returns immediately when 2025-07-17T07:11:27.9550465Z >>> # the callback is installed through the `then(cb)` API. In the 2025-07-17T07:11:27.9550660Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-07-17T07:11:27.9550817Z >>> # When the return value of that `rpc_async` arrives at 2025-07-17T07:11:27.9551003Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-07-17T07:11:27.9551200Z >>> # and set the value for the previously returned `Future`, which 2025-07-17T07:11:27.9551382Z >>> # will then trigger RPC to send the result back to "worker0". 2025-07-17T07:11:27.9551549Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T07:11:27.9551677Z >>> lambda fut: fut.wait() + z 2025-07-17T07:11:27.9551764Z >>> ) 2025-07-17T07:11:27.9551857Z >>> 2025-07-17T07:11:27.9551943Z >>> # On worker0 2025-07-17T07:11:27.9552039Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.9552149Z >>> ret = rpc.rpc_sync( 2025-07-17T07:11:27.9552239Z >>> "worker1", 2025-07-17T07:11:27.9552335Z >>> async_add_chained, 2025-07-17T07:11:27.9552466Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-07-17T07:11:27.9552549Z >>> ) 2025-07-17T07:11:27.9552664Z >>> print(ret) # prints tensor([3., 3.]) 2025-07-17T07:11:27.9552669Z 2025-07-17T07:11:27.9552904Z When combined with TorchScript decorators, this decorator must be the 2025-07-17T07:11:27.9552992Z outmost one. 2025-07-17T07:11:27.9552997Z 2025-07-17T07:11:27.9553110Z >>> from torch import Tensor 2025-07-17T07:11:27.9553223Z >>> from torch.futures import Future 2025-07-17T07:11:27.9553337Z >>> from torch.distributed import rpc 2025-07-17T07:11:27.9553433Z >>> 2025-07-17T07:11:27.9553544Z >>> # omitting setup and shutdown RPC 2025-07-17T07:11:27.9553628Z >>> 2025-07-17T07:11:27.9553732Z >>> # On all workers 2025-07-17T07:11:27.9553831Z >>> @torch.jit.script 2025-07-17T07:11:27.9553981Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-07-17T07:11:27.9554085Z >>> return x + y 2025-07-17T07:11:27.9554166Z >>> 2025-07-17T07:11:27.9554279Z >>> @rpc.functions.async_execution 2025-07-17T07:11:27.9554387Z >>> @torch.jit.script 2025-07-17T07:11:27.9554577Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-07-17T07:11:27.9554732Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-07-17T07:11:27.9554815Z >>> 2025-07-17T07:11:27.9554904Z >>> # On worker0 2025-07-17T07:11:27.9555011Z >>> ret = rpc.rpc_sync( 2025-07-17T07:11:27.9555100Z >>> "worker1", 2025-07-17T07:11:27.9555187Z >>> async_add, 2025-07-17T07:11:27.9555319Z >>> args=("worker2", torch.ones(2), 1) 2025-07-17T07:11:27.9555404Z >>> ) 2025-07-17T07:11:27.9555548Z >>> print(ret) # prints tensor([2., 2.]) 2025-07-17T07:11:27.9555553Z 2025-07-17T07:11:27.9555786Z When combined with static or class method, this decorator must be the 2025-07-17T07:11:27.9555875Z inner one. 2025-07-17T07:11:27.9555879Z 2025-07-17T07:11:27.9556007Z >>> from torch.distributed import rpc 2025-07-17T07:11:27.9556088Z >>> 2025-07-17T07:11:27.9556198Z >>> # omitting setup and shutdown RPC 2025-07-17T07:11:27.9556291Z >>> 2025-07-17T07:11:27.9556380Z >>> # On all workers 2025-07-17T07:11:27.9556490Z >>> class AsyncExecutionClass: 2025-07-17T07:11:27.9556584Z >>> 2025-07-17T07:11:27.9556674Z >>> @staticmethod 2025-07-17T07:11:27.9556789Z >>> @rpc.functions.async_execution 2025-07-17T07:11:27.9556913Z >>> def static_async_add(to, x, y, z): 2025-07-17T07:11:27.9557082Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T07:11:27.9557246Z >>> lambda fut: fut.wait() + z 2025-07-17T07:11:27.9557334Z >>> ) 2025-07-17T07:11:27.9557415Z >>> 2025-07-17T07:11:27.9557518Z >>> @classmethod 2025-07-17T07:11:27.9557632Z >>> @rpc.functions.async_execution 2025-07-17T07:11:27.9557808Z >>> def class_async_add(cls, to, x, y, z): 2025-07-17T07:11:27.9557945Z >>> ret_fut = torch.futures.Future() 2025-07-17T07:11:27.9558095Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T07:11:27.9558245Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-07-17T07:11:27.9558342Z >>> ) 2025-07-17T07:11:27.9558438Z >>> return ret_fut 2025-07-17T07:11:27.9558520Z >>> 2025-07-17T07:11:27.9558649Z >>> @rpc.functions.async_execution 2025-07-17T07:11:27.9558771Z >>> def bound_async_add(self, to, x, y, z): 2025-07-17T07:11:27.9558952Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T07:11:27.9559067Z >>> lambda fut: fut.wait() + z 2025-07-17T07:11:27.9559151Z >>> ) 2025-07-17T07:11:27.9559248Z >>> 2025-07-17T07:11:27.9559335Z >>> # On worker0 2025-07-17T07:11:27.9559431Z >>> ret = rpc.rpc_sync( 2025-07-17T07:11:27.9559538Z >>> "worker1", 2025-07-17T07:11:27.9559668Z >>> AsyncExecutionClass.static_async_add, 2025-07-17T07:11:27.9559785Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-07-17T07:11:27.9559883Z >>> ) 2025-07-17T07:11:27.9560000Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:27.9560082Z >>> 2025-07-17T07:11:27.9560193Z >>> ret = rpc.rpc_sync( 2025-07-17T07:11:27.9560282Z >>> "worker1", 2025-07-17T07:11:27.9560423Z >>> AsyncExecutionClass.class_async_add, 2025-07-17T07:11:27.9560538Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-07-17T07:11:27.9560622Z >>> ) 2025-07-17T07:11:27.9560748Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:27.9560753Z 2025-07-17T07:11:27.9560913Z This decorator also works with RRef helpers, i.e., . 2025-07-17T07:11:27.9561054Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-07-17T07:11:27.9561222Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-07-17T07:11:27.9561361Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-07-17T07:11:27.9561365Z 2025-07-17T07:11:27.9561494Z >>> from torch.distributed import rpc 2025-07-17T07:11:27.9561577Z >>> 2025-07-17T07:11:27.9561707Z >>> # reuse the AsyncExecutionClass class above 2025-07-17T07:11:27.9561871Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T07:11:27.9562082Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-07-17T07:11:27.9562196Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:27.9562290Z >>> 2025-07-17T07:11:27.9562437Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T07:11:27.9562675Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-07-17T07:11:27.9562829Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:27.9562910Z >>> 2025-07-17T07:11:27.9563071Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T07:11:27.9563308Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-07-17T07:11:27.9563421Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:27.9563425Z 2025-07-17T07:11:27.9563690Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.9563695Z 2025-07-17T07:11:27.9579934Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=113. 2025-07-17T07:11:27.9580219Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.9580224Z 2025-07-17T07:11:27.9580424Z Set device mapping between each RPC caller and callee pair. This 2025-07-17T07:11:27.9580709Z function can be called multiple times to incrementally add 2025-07-17T07:11:27.9580840Z device placement configurations. 2025-07-17T07:11:27.9580844Z 2025-07-17T07:11:27.9580925Z Args: 2025-07-17T07:11:27.9581039Z to (str): Callee name. 2025-07-17T07:11:27.9581307Z device_map (Dict of int, str, or torch.device): Device placement 2025-07-17T07:11:27.9581491Z mappings from this worker to the callee. This map must be 2025-07-17T07:11:27.9581589Z invertible. 2025-07-17T07:11:27.9581593Z 2025-07-17T07:11:27.9581677Z Example: 2025-07-17T07:11:27.9581787Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:27.9581889Z >>> # both workers 2025-07-17T07:11:27.9581977Z >>> def add(x, y): 2025-07-17T07:11:27.9582110Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-07-17T07:11:27.9582228Z >>> return x + y, (x + y).to(2) 2025-07-17T07:11:27.9582312Z >>> 2025-07-17T07:11:27.9582416Z >>> # on worker 0 2025-07-17T07:11:27.9582553Z >>> options = TensorPipeRpcBackendOptions( 2025-07-17T07:11:27.9582655Z >>> num_worker_threads=8, 2025-07-17T07:11:27.9582780Z >>> device_maps={"worker1": {0: 1}} 2025-07-17T07:11:27.9582905Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-07-17T07:11:27.9583002Z >>> ) 2025-07-17T07:11:27.9583142Z >>> options.set_device_map("worker1", {1: 2}) 2025-07-17T07:11:27.9583265Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-07-17T07:11:27.9583356Z >>> 2025-07-17T07:11:27.9583447Z >>> rpc.init_rpc( 2025-07-17T07:11:27.9583533Z >>> "worker0", 2025-07-17T07:11:27.9583627Z >>> rank=0, 2025-07-17T07:11:27.9583720Z >>> world_size=2, 2025-07-17T07:11:27.9583846Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-07-17T07:11:27.9583965Z >>> rpc_backend_options=options 2025-07-17T07:11:27.9584046Z >>> ) 2025-07-17T07:11:27.9584126Z >>> 2025-07-17T07:11:27.9584229Z >>> x = torch.ones(2) 2025-07-17T07:11:27.9584384Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-07-17T07:11:27.9584572Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-07-17T07:11:27.9584769Z >>> # sending the return value back, it will follow the invert of 2025-07-17T07:11:27.9584946Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-07-17T07:11:27.9585050Z >>> # cuda:1 on worker0 2025-07-17T07:11:27.9585193Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-07-17T07:11:27.9585334Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-07-17T07:11:27.9585338Z 2025-07-17T07:11:27.9585603Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.9585608Z 2025-07-17T07:11:27.9601087Z 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. 2025-07-17T07:11:27.9601369Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:27.9601443Z 2025-07-17T07:11:27.9601646Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-07-17T07:11:27.9601924Z except that it enables profiling on all threads running RPC server request callbacks. 2025-07-17T07:11:27.9601946Z 2025-07-17T07:11:27.9602228Z Context manager that manages autograd profiler state and holds a summary of results. 2025-07-17T07:11:27.9602461Z Under the hood it just records events of functions being executed in C++ and 2025-07-17T07:11:27.9602705Z exposes those events to Python. You can wrap any code into it and it will 2025-07-17T07:11:27.9602831Z only report runtime of PyTorch functions. 2025-07-17T07:11:27.9603097Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-07-17T07:11:27.9603134Z 2025-07-17T07:11:27.9603215Z Args: 2025-07-17T07:11:27.9603481Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-07-17T07:11:27.9603620Z Default: ``True``. 2025-07-17T07:11:27.9603627Z 2025-07-17T07:11:27.9603911Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-07-17T07:11:27.9604121Z Adds approximately 4us of overhead to each tensor operation. 2025-07-17T07:11:27.9604271Z Default: ``False`` 2025-07-17T07:11:27.9604276Z 2025-07-17T07:11:27.9604500Z record_shapes (bool, optional): If shapes recording is set, information 2025-07-17T07:11:27.9604744Z about input dimensions will be collected. This allows one to see which 2025-07-17T07:11:27.9604955Z dimensions have been used under the hood and further group by them 2025-07-17T07:11:27.9605182Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-07-17T07:11:27.9605408Z shape recording might skew your profiling data. It is recommended to 2025-07-17T07:11:27.9605644Z use separate runs with and without shape recording to validate the timing. 2025-07-17T07:11:27.9605882Z Most likely the skew will be negligible for bottom most events (in a case 2025-07-17T07:11:27.9606095Z of nested function calls). But for higher level functions the total 2025-07-17T07:11:27.9606299Z self cpu time might be artificially increased because of the shape 2025-07-17T07:11:27.9606400Z collection. 2025-07-17T07:11:27.9606404Z 2025-07-17T07:11:27.9606671Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-07-17T07:11:27.9606676Z 2025-07-17T07:11:27.9606781Z .. warning:: 2025-07-17T07:11:27.9606983Z Enabling memory profiling incurs additional profiler overhead 2025-07-17T07:11:27.9606988Z 2025-07-17T07:11:27.9607074Z .. warning:: 2025-07-17T07:11:27.9607374Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-07-17T07:11:27.9607569Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-07-17T07:11:27.9607802Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-07-17T07:11:27.9607979Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-07-17T07:11:27.9607984Z 2025-07-17T07:11:27.9608072Z Example: 2025-07-17T07:11:27.9608181Z >>> # xdoctest: +SKIP 2025-07-17T07:11:27.9608273Z >>> # On worker 0: 2025-07-17T07:11:27.9608363Z >>> import torch 2025-07-17T07:11:27.9608497Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:27.9608633Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T07:11:27.9608752Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-07-17T07:11:27.9608875Z >>> outer_profile_rref = rpc.remote( 2025-07-17T07:11:27.9609037Z ... dst_worker_name, rpc._server_process_global_profile 2025-07-17T07:11:27.9609131Z ... ) 2025-07-17T07:11:27.9609257Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-07-17T07:11:27.9609404Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-07-17T07:11:27.9609529Z >>> inner_profile_rref = rpc.remote( 2025-07-17T07:11:27.9609689Z ... dst_worker_name, rpc._server_process_global_profile 2025-07-17T07:11:27.9609798Z ... ) 2025-07-17T07:11:27.9609934Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-07-17T07:11:27.9610085Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-07-17T07:11:27.9610256Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-07-17T07:11:27.9610437Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-07-17T07:11:27.9610592Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-07-17T07:11:27.9610842Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:27.9611158Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-07-17T07:11:27.9611387Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:27.9611621Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-07-17T07:11:27.9611813Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-07-17T07:11:27.9612101Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:27.9612209Z Self CPU time total: 89.667us 2025-07-17T07:11:27.9612369Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-07-17T07:11:27.9612604Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:27.9612912Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-07-17T07:11:27.9613139Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:27.9613336Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-07-17T07:11:27.9613525Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-07-17T07:11:27.9613723Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-07-17T07:11:27.9613948Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:27.9614054Z Self CPU time total: 213.926us 2025-07-17T07:11:27.9614161Z >>> rpc.shutdown() 2025-07-17T07:11:27.9614166Z 2025-07-17T07:11:27.9614253Z >>> # On worker 1: 2025-07-17T07:11:27.9614390Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:27.9614526Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T07:11:27.9614682Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-07-17T07:11:27.9614785Z >>> rpc.shutdown() 2025-07-17T07:11:27.9614791Z 2025-07-17T07:11:27.9615047Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:27.9615053Z 2025-07-17T07:11:28.0608993Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=35. 2025-07-17T07:11:28.0610022Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.0610426Z 2025-07-17T07:11:28.0610692Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-07-17T07:11:28.0611370Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-07-17T07:11:28.0612047Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-07-17T07:11:28.0612586Z :class:`DTensor` according to the ``out_placements``. 2025-07-17T07:11:28.0612866Z 2025-07-17T07:11:28.0612947Z Args: 2025-07-17T07:11:28.0613277Z func (Callable): the function to be applied on each local shard of 2025-07-17T07:11:28.0613794Z :class:`DTensor` s. 2025-07-17T07:11:28.0614207Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-07-17T07:11:28.0614824Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-07-17T07:11:28.0615517Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-07-17T07:11:28.0616120Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-07-17T07:11:28.0616743Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-07-17T07:11:28.0617241Z mapping to the flattened ``output``. 2025-07-17T07:11:28.0617708Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-07-17T07:11:28.0618318Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-07-17T07:11:28.0618811Z should be `None`. 2025-07-17T07:11:28.0619286Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-07-17T07:11:28.0619878Z in. In this case, even if `out_placements` is not `None`, the result function 2025-07-17T07:11:28.0620551Z should ignore the desired placements because the function is not running with 2025-07-17T07:11:28.0621037Z :class:`DTensor` s. 2025-07-17T07:11:28.0621382Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-07-17T07:11:28.0621935Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-07-17T07:11:28.0638420Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-07-17T07:11:28.0639062Z placements of each :class:`DTensor` argument is the same as the required 2025-07-17T07:11:28.0639609Z placements or not. If the placements are not the same and 2025-07-17T07:11:28.0640152Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-07-17T07:11:28.0640796Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-07-17T07:11:28.0641433Z the required sharding placements before passing its local tensor to ``func``. 2025-07-17T07:11:28.0642054Z The only exception is when required placements are not ``None`` and the 2025-07-17T07:11:28.0642639Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-07-17T07:11:28.0643229Z will be skipped and the argument will be directly passed to ``func``. 2025-07-17T07:11:28.0643802Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-07-17T07:11:28.0644239Z Default: None 2025-07-17T07:11:28.0644585Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-07-17T07:11:28.0645101Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-07-17T07:11:28.0645648Z to the flattened input DTensor. This argument is the hint that user 2025-07-17T07:11:28.0646162Z can give to :meth:`to_local` in case the gradient layout of the 2025-07-17T07:11:28.0646685Z local tensor input does not match its :class:`DTensor` input layout. 2025-07-17T07:11:28.0647229Z If not specified, we will assume the gradient layout of the local 2025-07-17T07:11:28.0647760Z tensor input remains the same as the original :class:`DTensor` input 2025-07-17T07:11:28.0648266Z and use that for gradient computation. Default: None. 2025-07-17T07:11:28.0648681Z device_mesh (:class:`DeviceMesh`, optional): 2025-07-17T07:11:28.0649141Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-07-17T07:11:28.0649728Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-07-17T07:11:28.0650204Z mesh. Default: None. 2025-07-17T07:11:28.0650385Z 2025-07-17T07:11:28.0650477Z Keyword Args: 2025-07-17T07:11:28.0650710Z redistribute_inputs (bool, optional): 2025-07-17T07:11:28.0651269Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-07-17T07:11:28.0651897Z their placements are different from the required input placements. If this 2025-07-17T07:11:28.0652508Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-07-17T07:11:28.0652994Z an exception will be raised. Default: False. 2025-07-17T07:11:28.0653260Z 2025-07-17T07:11:28.0653346Z Returns: 2025-07-17T07:11:28.0653728Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-07-17T07:11:28.0654335Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-07-17T07:11:28.0654703Z 2025-07-17T07:11:28.0654783Z Raises: 2025-07-17T07:11:28.0655145Z AssertionError: For any non-DTensor output, we require its corresponding 2025-07-17T07:11:28.0655847Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-07-17T07:11:28.0656379Z if this is not the case. 2025-07-17T07:11:28.0656588Z 2025-07-17T07:11:28.0656848Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-07-17T07:11:28.0657485Z a redistribution according to ``in_placements``. 2025-07-17T07:11:28.0657867Z 2025-07-17T07:11:28.0657987Z Example: 2025-07-17T07:11:28.0658313Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:28.0658870Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-07-17T07:11:28.0659436Z >>> partial_sum_tensor = torch.mm(W, X) 2025-07-17T07:11:28.0660172Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-07-17T07:11:28.0660797Z >>> return reduced_tensor 2025-07-17T07:11:28.0661075Z >>> 2025-07-17T07:11:28.0661310Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-07-17T07:11:28.0661682Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-07-17T07:11:28.0662023Z >>> Y = torch.mm(W, X) 2025-07-17T07:11:28.0662391Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-07-17T07:11:28.0662876Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-07-17T07:11:28.0663261Z >>> 2025-07-17T07:11:28.0663646Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2025-07-17T07:11:28.0664166Z >>> local_mm_allreduce_forward = local_map( 2025-07-17T07:11:28.0664498Z >>> mm_allreduce_forward, 2025-07-17T07:11:28.0664803Z >>> out_placements=[Replicate()], 2025-07-17T07:11:28.0665135Z >>> in_placements=[col_wise, row_wise], 2025-07-17T07:11:28.0665463Z >>> device_mesh=device_mesh, 2025-07-17T07:11:28.0665753Z >>> ) 2025-07-17T07:11:28.0665936Z >>> 2025-07-17T07:11:28.0666151Z >>> W_dt = distribute_tensor( 2025-07-17T07:11:28.0666454Z ... W, device_mesh, (col_wise) 2025-07-17T07:11:28.0666767Z ... ) # col-wisely sharded W tensor 2025-07-17T07:11:28.0667091Z >>> X_dt = distribute_tensor( 2025-07-17T07:11:28.0667399Z ... X, device_mesh, (row_wise) 2025-07-17T07:11:28.0667708Z ... ) # row-wisely sharded X tensor 2025-07-17T07:11:28.0668042Z >>> Y_dt = local_mm_allreduce_forward( 2025-07-17T07:11:28.0668375Z ... device_mesh, W_dt, X_dt 2025-07-17T07:11:28.0668726Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-07-17T07:11:28.0668986Z 2025-07-17T07:11:28.0669195Z .. note:: This API is currently experimental and subject to change 2025-07-17T07:11:28.0669515Z 2025-07-17T07:11:28.0669767Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.0670133Z 2025-07-17T07:11:28.0670912Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2025-07-17T07:11:28.0672282Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.0672670Z 2025-07-17T07:11:28.0673083Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-07-17T07:11:28.0673717Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-07-17T07:11:28.0674329Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-07-17T07:11:28.0674954Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-07-17T07:11:28.0675586Z when users would like to overwrite default sharding strategies of existing operators. 2025-07-17T07:11:28.0675998Z 2025-07-17T07:11:28.0676081Z Args: 2025-07-17T07:11:28.0676330Z op (Union[OpOverload, List[OpOverload]]): 2025-07-17T07:11:28.0676769Z An op or a list of ops to register the customized sharding function. 2025-07-17T07:11:28.0677080Z 2025-07-17T07:11:28.0677166Z Returns: 2025-07-17T07:11:28.0677561Z A function decorator which can be used to wrap a function that defines the sharding 2025-07-17T07:11:28.0678279Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-07-17T07:11:28.0678944Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-07-17T07:11:28.0679726Z already implemented the operator. The customized sharding function takes the same inputs 2025-07-17T07:11:28.0680401Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-07-17T07:11:28.0681042Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-07-17T07:11:28.0681707Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-07-17T07:11:28.0682227Z corresponding intput placements. 2025-07-17T07:11:28.0682445Z 2025-07-17T07:11:28.0682547Z Example: 2025-07-17T07:11:28.0682776Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:28.0683137Z >>> @register_sharding(aten._softmax.default) 2025-07-17T07:11:28.0683543Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-07-17T07:11:28.0683960Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-07-17T07:11:28.0684319Z >>> acceptable_shardings = [] 2025-07-17T07:11:28.0684619Z >>> 2025-07-17T07:11:28.0684961Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-07-17T07:11:28.0685424Z >>> acceptable_shardings.append(all_replicate) 2025-07-17T07:11:28.0685767Z >>> 2025-07-17T07:11:28.0686001Z >>> for sharding_dim in range(x.ndim): 2025-07-17T07:11:28.0686338Z >>> if sharding_dim != softmax_dim: 2025-07-17T07:11:28.0686668Z >>> all_sharded = ( 2025-07-17T07:11:28.0686977Z >>> [Shard(sharding_dim)], 2025-07-17T07:11:28.0687324Z >>> [Shard(sharding_dim), None, None], 2025-07-17T07:11:28.0687639Z >>> ) 2025-07-17T07:11:28.0687933Z >>> acceptable_shardings.append(all_sharded) 2025-07-17T07:11:28.0688278Z >>> 2025-07-17T07:11:28.0688493Z >>> return acceptable_shardings 2025-07-17T07:11:28.0688720Z 2025-07-17T07:11:28.0688918Z .. note:: This API is currently experimental and subject to change 2025-07-17T07:11:28.0689223Z 2025-07-17T07:11:28.0689490Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.0689858Z 2025-07-17T07:11:28.0824865Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=428. 2025-07-17T07:11:28.0825904Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.0826298Z 2025-07-17T07:11:28.0826683Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-07-17T07:11:28.0827514Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-07-17T07:11:28.0827954Z 2025-07-17T07:11:28.0828061Z Keyword Args: 2025-07-17T07:11:28.0828480Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T07:11:28.0829140Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-07-17T07:11:28.0830092Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-07-17T07:11:28.0830698Z as a placeholder. default: None. 2025-07-17T07:11:28.0831145Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T07:11:28.0831885Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T07:11:28.0832785Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-07-17T07:11:28.0833426Z input_kwarg_layouts (Dict[str, Placement]): 2025-07-17T07:11:28.0834056Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-07-17T07:11:28.0834722Z default: None 2025-07-17T07:11:28.0835040Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-07-17T07:11:28.0835801Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T07:11:28.0836450Z have the desired DTensor layouts. default: None. 2025-07-17T07:11:28.0836833Z use_local_output (bool, optional): 2025-07-17T07:11:28.0837409Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-07-17T07:11:28.0837984Z Returns: 2025-07-17T07:11:28.0838558Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-07-17T07:11:28.0838999Z 2025-07-17T07:11:28.0839106Z Example:: 2025-07-17T07:11:28.0839326Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:28.0839852Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-07-17T07:11:28.0840491Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T07:11:28.0840876Z >>> ... 2025-07-17T07:11:28.0841319Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T07:11:28.0841904Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T07:11:28.0842232Z >>> 2025-07-17T07:11:28.0842697Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-07-17T07:11:28.0843298Z >>> # and then redistributed to Replicated DTensor. 2025-07-17T07:11:28.0843665Z >>> parallelize_module( 2025-07-17T07:11:28.0843987Z >>> block, # this can be a submodule or module 2025-07-17T07:11:28.0844315Z >>> tp_mesh, 2025-07-17T07:11:28.0844577Z >>> parallelize_plan={ 2025-07-17T07:11:28.0844884Z >>> "attn": PrepareModuleInput( 2025-07-17T07:11:28.0845238Z >>> input_layouts=(Shard(0), None, None, ...), 2025-07-17T07:11:28.0845663Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-07-17T07:11:28.0846039Z >>> ), 2025-07-17T07:11:28.0846263Z >>> } 2025-07-17T07:11:28.0846470Z >>> ) 2025-07-17T07:11:28.0846598Z 2025-07-17T07:11:28.0846850Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.0847219Z 2025-07-17T07:11:28.0847907Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=597. 2025-07-17T07:11:28.0848938Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.0849328Z 2025-07-17T07:11:28.0849722Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-07-17T07:11:28.0850580Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-07-17T07:11:28.0851081Z 2025-07-17T07:11:28.0851183Z Keyword Args: 2025-07-17T07:11:28.0851479Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T07:11:28.0852110Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-07-17T07:11:28.0852964Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-07-17T07:11:28.0853614Z ``None`` need to be specified as a placeholder. 2025-07-17T07:11:28.0854056Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T07:11:28.0854769Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-07-17T07:11:28.0855486Z have the desired DTensor layouts. 2025-07-17T07:11:28.0855816Z use_local_output (bool, optional): 2025-07-17T07:11:28.0856407Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-07-17T07:11:28.0857017Z Returns: 2025-07-17T07:11:28.0857441Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-07-17T07:11:28.0857857Z 2025-07-17T07:11:28.0858568Z Example:: 2025-07-17T07:11:28.0858810Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:28.0859339Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-07-17T07:11:28.0859967Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T07:11:28.0860366Z >>> ... 2025-07-17T07:11:28.0860804Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T07:11:28.0861358Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T07:11:28.0861670Z >>> 2025-07-17T07:11:28.0862206Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-07-17T07:11:28.0862879Z >>> # and then redistributed to Sharded DTensor. 2025-07-17T07:11:28.0863224Z >>> parallelize_module( 2025-07-17T07:11:28.0863539Z >>> block, # this can be a submodule or module 2025-07-17T07:11:28.0863879Z >>> tp_mesh, 2025-07-17T07:11:28.0864157Z >>> parallelize_plan = PrepareModuleOutput( 2025-07-17T07:11:28.0864525Z >>> output_layouts=Replicate(), 2025-07-17T07:11:28.0864869Z >>> desired_output_layouts=Shard(0) 2025-07-17T07:11:28.0865190Z >>> ) 2025-07-17T07:11:28.0865394Z >>> ) 2025-07-17T07:11:28.0865521Z 2025-07-17T07:11:28.0865773Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.0866144Z 2025-07-17T07:11:28.0866858Z msg = Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-07-17T07:11:28.0867923Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.0868321Z 2025-07-17T07:11:28.0868757Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-07-17T07:11:28.0869748Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-07-17T07:11:28.0870682Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-07-17T07:11:28.0871396Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-07-17T07:11:28.0871716Z 2025-07-17T07:11:28.0871974Z Keyword Args: 2025-07-17T07:11:28.0872325Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T07:11:28.0872980Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-07-17T07:11:28.0873788Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-07-17T07:11:28.0874474Z as a placeholder. default: None. 2025-07-17T07:11:28.0874936Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T07:11:28.0875684Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T07:11:28.0876588Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-07-17T07:11:28.0877240Z input_kwarg_layouts (Dict[str, Placement]): 2025-07-17T07:11:28.0877877Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-07-17T07:11:28.0878462Z default: None 2025-07-17T07:11:28.0878783Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-07-17T07:11:28.0879436Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T07:11:28.0880127Z have the desired DTensor layouts. default: None. 2025-07-17T07:11:28.0880487Z use_local_input (bool, optional): 2025-07-17T07:11:28.0881141Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-07-17T07:11:28.0881803Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T07:11:28.0882413Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-07-17T07:11:28.0883262Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-07-17T07:11:28.0883909Z ``None`` need to be specified as a placeholder. 2025-07-17T07:11:28.0884366Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T07:11:28.0885067Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-07-17T07:11:28.0885706Z have the desired DTensor layouts. 2025-07-17T07:11:28.0886050Z use_local_output (bool, optional): 2025-07-17T07:11:28.0886646Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-07-17T07:11:28.0887214Z Returns: 2025-07-17T07:11:28.0887712Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-07-17T07:11:28.0888203Z 2025-07-17T07:11:28.0888304Z Example:: 2025-07-17T07:11:28.0888537Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:28.0889083Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-07-17T07:11:28.0889732Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T07:11:28.0890126Z >>> ... 2025-07-17T07:11:28.0890562Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T07:11:28.0891194Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T07:11:28.0891524Z >>> 2025-07-17T07:11:28.0891993Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-07-17T07:11:28.0892809Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-07-17T07:11:28.0893510Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-07-17T07:11:28.0893946Z >>> parallelize_module( 2025-07-17T07:11:28.0894265Z >>> block, # this can be a submodule or module 2025-07-17T07:11:28.0894591Z >>> tp_mesh, 2025-07-17T07:11:28.0894846Z >>> parallelize_plan={ 2025-07-17T07:11:28.0895219Z >>> "attn": PrepareModuleInputOutput( 2025-07-17T07:11:28.0895594Z >>> input_layouts=(Shard(0), None, None, ...), 2025-07-17T07:11:28.0896027Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-07-17T07:11:28.0896446Z >>> output_layouts=Replicate(), 2025-07-17T07:11:28.0896857Z >>> desired_output_layouts=Shard(0), 2025-07-17T07:11:28.0897174Z >>> ), 2025-07-17T07:11:28.0897403Z >>> } 2025-07-17T07:11:28.0897624Z >>> ) 2025-07-17T07:11:28.0897736Z 2025-07-17T07:11:28.0897989Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.0898373Z 2025-07-17T07:11:28.1388046Z msg = Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-07-17T07:11:28.1389175Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.1389552Z 2025-07-17T07:11:28.1389869Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-07-17T07:11:28.1390491Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-07-17T07:11:28.1390921Z 2025-07-17T07:11:28.1391102Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-07-17T07:11:28.1391405Z 2025-07-17T07:11:28.1391492Z Example: 2025-07-17T07:11:28.1391765Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-07-17T07:11:28.1392240Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:28.1392619Z >>> m = LowRankMultivariateNormal( 2025-07-17T07:11:28.1393029Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-07-17T07:11:28.1393405Z ... ) 2025-07-17T07:11:28.1393826Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-07-17T07:11:28.1394350Z tensor([-0.2102, -0.5429]) 2025-07-17T07:11:28.1394530Z 2025-07-17T07:11:28.1394626Z Args: 2025-07-17T07:11:28.1394974Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-07-17T07:11:28.1395598Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-07-17T07:11:28.1396111Z `batch_shape + event_shape + (rank,)` 2025-07-17T07:11:28.1396591Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-07-17T07:11:28.1397086Z `batch_shape + event_shape` 2025-07-17T07:11:28.1397304Z 2025-07-17T07:11:28.1397385Z Note: 2025-07-17T07:11:28.1397773Z The computation for determinant and inverse of covariance matrix is avoided when 2025-07-17T07:11:28.1398398Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-07-17T07:11:28.1398980Z `_ and 2025-07-17T07:11:28.1399591Z `matrix determinant lemma `_. 2025-07-17T07:11:28.1400247Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-07-17T07:11:28.1400749Z the small size "capacitance" matrix:: 2025-07-17T07:11:28.1400984Z 2025-07-17T07:11:28.1401168Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-07-17T07:11:28.1401469Z 2025-07-17T07:11:28.1401734Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.1402102Z 2025-07-17T07:11:28.1406414Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=15. 2025-07-17T07:11:28.1407439Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.1407835Z 2025-07-17T07:11:28.1408061Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-07-17T07:11:28.1408669Z distribution where all component are from different parameterizations of 2025-07-17T07:11:28.1409259Z the same distribution type. It is parameterized by a `Categorical` 2025-07-17T07:11:28.1409779Z "selecting distribution" (over `k` component) and a component 2025-07-17T07:11:28.1410301Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-07-17T07:11:28.1410872Z (equal to `[k]`) which indexes each (batch of) component. 2025-07-17T07:11:28.1411142Z 2025-07-17T07:11:28.1411235Z Examples:: 2025-07-17T07:11:28.1411374Z 2025-07-17T07:11:28.1411489Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:28.1411927Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-07-17T07:11:28.1412367Z >>> # weighted normal distributions 2025-07-17T07:11:28.1412694Z >>> mix = D.Categorical(torch.ones(5,)) 2025-07-17T07:11:28.1413071Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-07-17T07:11:28.1413451Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T07:11:28.1413675Z 2025-07-17T07:11:28.1413874Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-07-17T07:11:28.1414335Z >>> # weighted bivariate normal distributions 2025-07-17T07:11:28.1414696Z >>> mix = D.Categorical(torch.ones(5,)) 2025-07-17T07:11:28.1415034Z >>> comp = D.Independent(D.Normal( 2025-07-17T07:11:28.1415463Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-07-17T07:11:28.1415837Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T07:11:28.1416065Z 2025-07-17T07:11:28.1416261Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-07-17T07:11:28.1416819Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-07-17T07:11:28.1417261Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-07-17T07:11:28.1417599Z >>> comp = D.Independent(D.Normal( 2025-07-17T07:11:28.1417935Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-07-17T07:11:28.1418303Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T07:11:28.1418536Z 2025-07-17T07:11:28.1418616Z Args: 2025-07-17T07:11:28.1418944Z mixture_distribution: `torch.distributions.Categorical`-like 2025-07-17T07:11:28.1419448Z instance. Manages the probability of selecting component. 2025-07-17T07:11:28.1419926Z The number of categories must match the rightmost batch 2025-07-17T07:11:28.1420410Z dimension of the `component_distribution`. Must have either 2025-07-17T07:11:28.1420847Z scalar `batch_shape` or `batch_shape` matching 2025-07-17T07:11:28.1421239Z `component_distribution.batch_shape[:-1]` 2025-07-17T07:11:28.1421713Z component_distribution: `torch.distributions.Distribution`-like 2025-07-17T07:11:28.1422237Z instance. Right-most batch dimension indexes component. 2025-07-17T07:11:28.1422528Z 2025-07-17T07:11:28.1422781Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.1423161Z 2025-07-17T07:11:28.1517087Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=120. 2025-07-17T07:11:28.1518098Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.1518476Z 2025-07-17T07:11:28.1518671Z Creates a RelaxedBernoulli distribution, parametrized by 2025-07-17T07:11:28.1519165Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-07-17T07:11:28.1519688Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-07-17T07:11:28.1520211Z so the values are in (0, 1), and has reparametrizable samples. 2025-07-17T07:11:28.1520504Z 2025-07-17T07:11:28.1520610Z Example:: 2025-07-17T07:11:28.1520729Z 2025-07-17T07:11:28.1520869Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:28.1521251Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-07-17T07:11:28.1521626Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-07-17T07:11:28.1521951Z >>> m.sample() 2025-07-17T07:11:28.1522209Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-07-17T07:11:28.1522448Z 2025-07-17T07:11:28.1522532Z Args: 2025-07-17T07:11:28.1522794Z temperature (Tensor): relaxation temperature 2025-07-17T07:11:28.1523213Z probs (Number, Tensor): the probability of sampling `1` 2025-07-17T07:11:28.1523670Z logits (Number, Tensor): the log-odds of sampling `1` 2025-07-17T07:11:28.1524024Z 2025-07-17T07:11:28.1524292Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.1524666Z 2025-07-17T07:11:28.1531596Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=109. 2025-07-17T07:11:28.1532661Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.1533055Z 2025-07-17T07:11:28.1533270Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-07-17T07:11:28.1533801Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-07-17T07:11:28.1534351Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-07-17T07:11:28.1534877Z its samples are on simplex, and are reparametrizable. 2025-07-17T07:11:28.1535211Z 2025-07-17T07:11:28.1535413Z Example:: 2025-07-17T07:11:28.1535539Z 2025-07-17T07:11:28.1535692Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:28.1536095Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-07-17T07:11:28.1536577Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-07-17T07:11:28.1536921Z >>> m.sample() 2025-07-17T07:11:28.1537173Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-07-17T07:11:28.1537412Z 2025-07-17T07:11:28.1537497Z Args: 2025-07-17T07:11:28.1537757Z temperature (Tensor): relaxation temperature 2025-07-17T07:11:28.1538128Z probs (Tensor): event probabilities 2025-07-17T07:11:28.1538534Z logits (Tensor): unnormalized log probability for each event 2025-07-17T07:11:28.1538852Z 2025-07-17T07:11:28.1539108Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.1539476Z 2025-07-17T07:11:28.4887646Z 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. 2025-07-17T07:11:28.4888724Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.4889463Z Return a new dict with new, potentially nested, key value pair 2025-07-17T07:11:28.4889797Z 2025-07-17T07:11:28.4889891Z >>> purchase = { 2025-07-17T07:11:28.4890152Z ... "name": "Alice", 2025-07-17T07:11:28.4890508Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T07:11:28.4890918Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T07:11:28.4891239Z ... } 2025-07-17T07:11:28.4891600Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-07-17T07:11:28.4892085Z {'credit card': '5555-1234-1234-1234', 2025-07-17T07:11:28.4892397Z 'name': 'Alice', 2025-07-17T07:11:28.4892722Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-07-17T07:11:28.4893077Z 2025-07-17T07:11:28.4893453Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.4893823Z 2025-07-17T07:11:28.4894574Z 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. 2025-07-17T07:11:28.4895735Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.4896275Z Update value in a (potentially) nested dictionary 2025-07-17T07:11:28.4896535Z 2025-07-17T07:11:28.4896621Z inputs: 2025-07-17T07:11:28.4896872Z d - dictionary on which to operate 2025-07-17T07:11:28.4897315Z keys - list or tuple giving the location of the value to be changed in d 2025-07-17T07:11:28.4897774Z func - function to operate on that value 2025-07-17T07:11:28.4898019Z 2025-07-17T07:11:28.4898207Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-07-17T07:11:28.4898761Z original dictionary with v replaced by func(v), but does not mutate the 2025-07-17T07:11:28.4899369Z original dictionary. 2025-07-17T07:11:28.4899541Z 2025-07-17T07:11:28.4899750Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-07-17T07:11:28.4900313Z specified by the keys, with the innermost value set to func(default). 2025-07-17T07:11:28.4900657Z 2025-07-17T07:11:28.4900758Z >>> inc = lambda x: x + 1 2025-07-17T07:11:28.4901051Z >>> update_in({"a": 0}, ["a"], inc) 2025-07-17T07:11:28.4901338Z {'a': 1} 2025-07-17T07:11:28.4901476Z 2025-07-17T07:11:28.4901569Z >>> transaction = { 2025-07-17T07:11:28.4901829Z ... "name": "Alice", 2025-07-17T07:11:28.4902184Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T07:11:28.4902615Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T07:11:28.4902931Z ... } 2025-07-17T07:11:28.4903260Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-07-17T07:11:28.4903761Z {'credit card': '5555-1234-1234-1234', 2025-07-17T07:11:28.4904076Z 'name': 'Alice', 2025-07-17T07:11:28.4904391Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-07-17T07:11:28.4904688Z 2025-07-17T07:11:28.4904899Z >>> # updating a value when k0 is not in d 2025-07-17T07:11:28.4905267Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-07-17T07:11:28.4905605Z {1: {2: {3: 'bar'}}} 2025-07-17T07:11:28.4905867Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-07-17T07:11:28.4906195Z {1: 'foo', 2: {3: {4: 1}}} 2025-07-17T07:11:28.4906461Z 2025-07-17T07:11:28.4906823Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.4907206Z 2025-07-17T07:11:28.4907855Z 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. 2025-07-17T07:11:28.4908877Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.4909430Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-07-17T07:11:28.4909704Z 2025-07-17T07:11:28.4909884Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-07-17T07:11:28.4910393Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-07-17T07:11:28.4910711Z 2025-07-17T07:11:28.4910929Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-07-17T07:11:28.4911391Z structures such as dictionaries and lists. 2025-07-17T07:11:28.4911629Z 2025-07-17T07:11:28.4911723Z >>> transaction = { 2025-07-17T07:11:28.4911980Z ... "name": "Alice", 2025-07-17T07:11:28.4912344Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T07:11:28.4912761Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T07:11:28.4913074Z ... } 2025-07-17T07:11:28.4913332Z >>> get_in(["purchase", "items", 0], transaction) 2025-07-17T07:11:28.4913659Z 'Apple' 2025-07-17T07:11:28.4913895Z >>> get_in(["name"], transaction) 2025-07-17T07:11:28.4914189Z 'Alice' 2025-07-17T07:11:28.4914431Z >>> get_in(["purchase", "total"], transaction) 2025-07-17T07:11:28.4914826Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-07-17T07:11:28.4915224Z >>> get_in(["purchase", "items", 10], transaction) 2025-07-17T07:11:28.4915611Z >>> get_in(["purchase", "total"], transaction, 0) 2025-07-17T07:11:28.4915933Z 0 2025-07-17T07:11:28.4916159Z >>> get_in(["y"], {}, no_default=True) 2025-07-17T07:11:28.4916491Z Traceback (most recent call last): 2025-07-17T07:11:28.4916785Z ... 2025-07-17T07:11:28.4917002Z KeyError: 'y' 2025-07-17T07:11:28.4917144Z 2025-07-17T07:11:28.4917242Z See Also: 2025-07-17T07:11:28.4917456Z itertoolz.get 2025-07-17T07:11:28.4917714Z operator.getitem 2025-07-17T07:11:28.4917964Z 2025-07-17T07:11:28.4918317Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.4918767Z 2025-07-17T07:11:28.4919458Z 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. 2025-07-17T07:11:28.4920502Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.4921003Z Group a collection by a key function 2025-07-17T07:11:28.4921215Z 2025-07-17T07:11:28.4921392Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-07-17T07:11:28.4921793Z >>> groupby(len, names) # doctest: +SKIP 2025-07-17T07:11:28.4922196Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-07-17T07:11:28.4922471Z 2025-07-17T07:11:28.4922594Z >>> iseven = lambda x: x % 2 == 0 2025-07-17T07:11:28.4922961Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-07-17T07:11:28.4923364Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-07-17T07:11:28.4923631Z 2025-07-17T07:11:28.4923773Z Non-callable keys imply grouping on a member. 2025-07-17T07:11:28.4924026Z 2025-07-17T07:11:28.4924127Z >>> groupby( 2025-07-17T07:11:28.4924347Z ... "gender", 2025-07-17T07:11:28.4924587Z ... [ 2025-07-17T07:11:28.4924899Z ... {"name": "Alice", "gender": "F"}, 2025-07-17T07:11:28.4925237Z ... {"name": "Bob", "gender": "M"}, 2025-07-17T07:11:28.4925592Z ... {"name": "Charlie", "gender": "M"}, 2025-07-17T07:11:28.4925921Z ... ], 2025-07-17T07:11:28.4926141Z ... ) # doctest:+SKIP 2025-07-17T07:11:28.4926433Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-07-17T07:11:28.4926768Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-07-17T07:11:28.4927101Z {'gender': 'M', 'name': 'Charlie'}]} 2025-07-17T07:11:28.4927322Z 2025-07-17T07:11:28.4927458Z Not to be confused with ``itertools.groupby`` 2025-07-17T07:11:28.4927717Z 2025-07-17T07:11:28.4927801Z See Also: 2025-07-17T07:11:28.4928018Z countby 2025-07-17T07:11:28.4928227Z 2025-07-17T07:11:28.4928595Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.4928963Z 2025-07-17T07:11:28.7497229Z msg = Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py line=142. 2025-07-17T07:11:28.7498110Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.7498728Z Return the recommended gain value for the given nonlinearity function. 2025-07-17T07:11:28.7499076Z 2025-07-17T07:11:28.7499195Z The values are as follows: 2025-07-17T07:11:28.7499383Z 2025-07-17T07:11:28.7499510Z ================= ==================================================== 2025-07-17T07:11:28.7499846Z nonlinearity gain 2025-07-17T07:11:28.7500139Z ================= ==================================================== 2025-07-17T07:11:28.7500485Z Linear / Identity :math:`1` 2025-07-17T07:11:28.7500767Z Conv{1,2,3}D :math:`1` 2025-07-17T07:11:28.7501052Z Sigmoid :math:`1` 2025-07-17T07:11:28.7501342Z Tanh :math:`\frac{5}{3}` 2025-07-17T07:11:28.7501652Z ReLU :math:`\sqrt{2}` 2025-07-17T07:11:28.7502057Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-07-17T07:11:28.7502477Z SELU :math:`\frac{3}{4}` 2025-07-17T07:11:28.7502808Z ================= ==================================================== 2025-07-17T07:11:28.7503044Z 2025-07-17T07:11:28.7503148Z .. warning:: 2025-07-17T07:11:28.7503480Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-07-17T07:11:28.7504033Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-07-17T07:11:28.7504557Z This gives the initial weights a variance of ``1 / N``, 2025-07-17T07:11:28.7505087Z which is necessary to induce a stable fixed point in the forward pass. 2025-07-17T07:11:28.7505662Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-07-17T07:11:28.7506330Z effect for more stable gradient flow in rectangular layers. 2025-07-17T07:11:28.7506632Z 2025-07-17T07:11:28.7506718Z Args: 2025-07-17T07:11:28.7507054Z nonlinearity: the non-linear function (`nn.functional` name) 2025-07-17T07:11:28.7507548Z param: optional parameter for the non-linear function 2025-07-17T07:11:28.7507832Z 2025-07-17T07:11:28.7507921Z Examples: 2025-07-17T07:11:28.7508174Z >>> gain = nn.init.calculate_gain( 2025-07-17T07:11:28.7508498Z ... "leaky_relu", 0.2 2025-07-17T07:11:28.7508818Z ... ) # leaky_relu with negative_slope=0.2 2025-07-17T07:11:28.7509056Z 2025-07-17T07:11:28.7509551Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-07-17T07:11:28.7510234Z 2025-07-17T07:11:28.7510600Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.7511032Z 2025-07-17T07:11:28.8074273Z 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. 2025-07-17T07:11:28.8075316Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.8075879Z Applies Batch Normalization over a N-Dimensional input. 2025-07-17T07:11:28.8076178Z 2025-07-17T07:11:28.8076524Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-07-17T07:11:28.8077221Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-07-17T07:11:28.8077775Z Internal Covariate Shift `__ . 2025-07-17T07:11:28.8078112Z 2025-07-17T07:11:28.8078212Z .. math:: 2025-07-17T07:11:28.8078340Z 2025-07-17T07:11:28.8078581Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-07-17T07:11:28.8078925Z 2025-07-17T07:11:28.8079166Z The mean and standard-deviation are calculated per-dimension over all 2025-07-17T07:11:28.8079741Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-07-17T07:11:28.8080351Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-07-17T07:11:28.8080900Z By default, the elements of :math:`\gamma` are sampled from 2025-07-17T07:11:28.8081388Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-07-17T07:11:28.8081973Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-07-17T07:11:28.8082474Z `torch.var(input, unbiased=False)`. 2025-07-17T07:11:28.8082694Z 2025-07-17T07:11:28.8082942Z Also by default, during training this layer keeps running estimates of its 2025-07-17T07:11:28.8083560Z computed mean and variance, which are then used for normalization during 2025-07-17T07:11:28.8084145Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-07-17T07:11:28.8084604Z of 0.1. 2025-07-17T07:11:28.8084723Z 2025-07-17T07:11:28.8084963Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-07-17T07:11:28.8085537Z keep running estimates, and batch statistics are instead used during 2025-07-17T07:11:28.8085972Z evaluation time as well. 2025-07-17T07:11:28.8086168Z 2025-07-17T07:11:28.8086255Z .. note:: 2025-07-17T07:11:28.8086615Z This :attr:`momentum` argument is different from one used in optimizer 2025-07-17T07:11:28.8087177Z classes and the conventional notion of momentum. Mathematically, the 2025-07-17T07:11:28.8087658Z update rule for running statistics here is 2025-07-17T07:11:28.8088177Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-07-17T07:11:28.8088759Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-07-17T07:11:28.8089213Z new observed value. 2025-07-17T07:11:28.8089445Z 2025-07-17T07:11:28.8089761Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-07-17T07:11:28.8090439Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-07-17T07:11:28.8090999Z Normalization or Spatio-temporal Batch Normalization. 2025-07-17T07:11:28.8091286Z 2025-07-17T07:11:28.8091445Z Currently :class:`SyncBatchNorm` only supports 2025-07-17T07:11:28.8091984Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-07-17T07:11:28.8092617Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-07-17T07:11:28.8093169Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-07-17T07:11:28.8093602Z Network with DDP. 2025-07-17T07:11:28.8093759Z 2025-07-17T07:11:28.8093843Z Args: 2025-07-17T07:11:28.8094139Z num_features: :math:`C` from an expected input of size 2025-07-17T07:11:28.8094568Z :math:`(N, C, +)` 2025-07-17T07:11:28.8094933Z eps: a value added to the denominator for numerical stability. 2025-07-17T07:11:28.8095414Z Default: ``1e-5`` 2025-07-17T07:11:28.8095896Z momentum: the value used for the running_mean and running_var 2025-07-17T07:11:28.8096411Z computation. Can be set to ``None`` for cumulative moving average 2025-07-17T07:11:28.8096872Z (i.e. simple average). Default: 0.1 2025-07-17T07:11:28.8097315Z affine: a boolean value that when set to ``True``, this module has 2025-07-17T07:11:28.8097800Z learnable affine parameters. Default: ``True`` 2025-07-17T07:11:28.8098272Z track_running_stats: a boolean value that when set to ``True``, this 2025-07-17T07:11:28.8098841Z module tracks the running mean and variance, and when set to ``False``, 2025-07-17T07:11:28.8099424Z this module does not track such statistics, and initializes statistics 2025-07-17T07:11:28.8099971Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-07-17T07:11:28.8100521Z When these buffers are ``None``, this module always uses batch statistics. 2025-07-17T07:11:28.8101031Z in both training and eval modes. Default: ``True`` 2025-07-17T07:11:28.8101556Z process_group: synchronization of stats happen within each process group 2025-07-17T07:11:28.8102140Z individually. Default behavior is synchronization across the whole 2025-07-17T07:11:28.8102576Z world 2025-07-17T07:11:28.8102713Z 2025-07-17T07:11:28.8102807Z Shape: 2025-07-17T07:11:28.8103025Z - Input: :math:`(N, C, +)` 2025-07-17T07:11:28.8103377Z - Output: :math:`(N, C, +)` (same shape as input) 2025-07-17T07:11:28.8103645Z 2025-07-17T07:11:28.8103736Z .. note:: 2025-07-17T07:11:28.8104117Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-07-17T07:11:28.8104679Z synchronization is disabled when ``model.eval()`` is set or if 2025-07-17T07:11:28.8105132Z ``self.training`` is otherwise ``False``. 2025-07-17T07:11:28.8105367Z 2025-07-17T07:11:28.8105473Z Examples:: 2025-07-17T07:11:28.8105609Z 2025-07-17T07:11:28.8105714Z >>> # xdoctest: +SKIP 2025-07-17T07:11:28.8106012Z >>> # With Learnable Parameters 2025-07-17T07:11:28.8106346Z >>> m = nn.SyncBatchNorm(100) 2025-07-17T07:11:28.8106685Z >>> # creating process group (optional) 2025-07-17T07:11:28.8107055Z >>> # ranks is a list of int identifying rank ids. 2025-07-17T07:11:28.8107412Z >>> ranks = list(range(8)) 2025-07-17T07:11:28.8107717Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-07-17T07:11:28.8108066Z >>> # Note: every rank calls into new_group for every 2025-07-17T07:11:28.8108482Z >>> # process group created, even if that rank is not 2025-07-17T07:11:28.8108856Z >>> # part of the group. 2025-07-17T07:11:28.8109284Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-07-17T07:11:28.8109900Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-07-17T07:11:28.8110342Z >>> # Without Learnable Parameters 2025-07-17T07:11:28.8110778Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-07-17T07:11:28.8111223Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-07-17T07:11:28.8111565Z >>> output = m(input) 2025-07-17T07:11:28.8111749Z 2025-07-17T07:11:28.8111880Z >>> # network is nn.BatchNorm layer 2025-07-17T07:11:28.8112372Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-07-17T07:11:28.8112943Z >>> # only single gpu per process is currently supported 2025-07-17T07:11:28.8113445Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-07-17T07:11:28.8113899Z >>> sync_bn_network, 2025-07-17T07:11:28.8114277Z >>> device_ids=[args.local_rank], 2025-07-17T07:11:28.8114657Z >>> output_device=args.local_rank) 2025-07-17T07:11:28.8114989Z 2025-07-17T07:11:28.8115405Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.8115784Z 2025-07-17T07:11:28.8116446Z 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. 2025-07-17T07:11:28.8117494Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.8118193Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-07-17T07:11:28.8118622Z 2025-07-17T07:11:28.8118706Z Args: 2025-07-17T07:11:28.8119089Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-07-17T07:11:28.8119687Z process_group (optional): process group to scope synchronization, 2025-07-17T07:11:28.8120140Z default is the whole world 2025-07-17T07:11:28.8120354Z 2025-07-17T07:11:28.8120439Z Returns: 2025-07-17T07:11:28.8120838Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-07-17T07:11:28.8121437Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-07-17T07:11:28.8121976Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-07-17T07:11:28.8122395Z instead. 2025-07-17T07:11:28.8122540Z 2025-07-17T07:11:28.8122641Z Example:: 2025-07-17T07:11:28.8122778Z 2025-07-17T07:11:28.8122907Z >>> # Network with nn.BatchNorm layer 2025-07-17T07:11:28.8123272Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:28.8123644Z >>> module = torch.nn.Sequential( 2025-07-17T07:11:28.8123992Z >>> torch.nn.Linear(20, 100), 2025-07-17T07:11:28.8124342Z >>> torch.nn.BatchNorm1d(100), 2025-07-17T07:11:28.8124674Z >>> ).cuda() 2025-07-17T07:11:28.8124989Z >>> # creating process group (optional) 2025-07-17T07:11:28.8125371Z >>> # ranks is a list of int identifying rank ids. 2025-07-17T07:11:28.8125740Z >>> ranks = list(range(8)) 2025-07-17T07:11:28.8126066Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-07-17T07:11:28.8126439Z >>> # Note: every rank calls into new_group for every 2025-07-17T07:11:28.8126846Z >>> # process group created, even if that rank is not 2025-07-17T07:11:28.8127222Z >>> # part of the group. 2025-07-17T07:11:28.8127548Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:28.8128020Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-07-17T07:11:28.8128602Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-07-17T07:11:28.8129268Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-07-17T07:11:28.8129688Z 2025-07-17T07:11:28.8129781Z 2025-07-17T07:11:28.8130150Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.8130530Z 2025-07-17T07:11:28.8327346Z 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. 2025-07-17T07:11:28.8328256Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.8328642Z 2025-07-17T07:11:28.8328967Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-07-17T07:11:28.8329402Z 2025-07-17T07:11:28.8329675Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-07-17T07:11:28.8330323Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-07-17T07:11:28.8330768Z 2025-07-17T07:11:28.8331086Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-07-17T07:11:28.8331867Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-07-17T07:11:28.8332421Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-07-17T07:11:28.8332701Z 2025-07-17T07:11:28.8332786Z Shape: 2025-07-17T07:11:28.8333133Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-07-17T07:11:28.8333732Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-07-17T07:11:28.8334318Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-07-17T07:11:28.8334797Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-07-17T07:11:28.8335038Z 2025-07-17T07:11:28.8335139Z Args: 2025-07-17T07:11:28.8335454Z dim (Union[int, str]): Dimension to be unflattened 2025-07-17T07:11:28.8336068Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-07-17T07:11:28.8336544Z 2025-07-17T07:11:28.8336635Z Examples: 2025-07-17T07:11:28.8336870Z >>> input = torch.randn(2, 50) 2025-07-17T07:11:28.8337164Z >>> # With tuple of ints 2025-07-17T07:11:28.8337449Z >>> m = nn.Sequential( 2025-07-17T07:11:28.8337719Z >>> nn.Linear(50, 50), 2025-07-17T07:11:28.8337995Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-07-17T07:11:28.8338292Z >>> ) 2025-07-17T07:11:28.8338512Z >>> output = m(input) 2025-07-17T07:11:28.8338768Z >>> output.size() 2025-07-17T07:11:28.8339022Z torch.Size([2, 2, 5, 5]) 2025-07-17T07:11:28.8339300Z >>> # With torch.Size 2025-07-17T07:11:28.8339550Z >>> m = nn.Sequential( 2025-07-17T07:11:28.8339817Z >>> nn.Linear(50, 50), 2025-07-17T07:11:28.8340120Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-07-17T07:11:28.8340441Z >>> ) 2025-07-17T07:11:28.8340646Z >>> output = m(input) 2025-07-17T07:11:28.8340910Z >>> output.size() 2025-07-17T07:11:28.8341166Z torch.Size([2, 2, 5, 5]) 2025-07-17T07:11:28.8341454Z >>> # With namedshape (tuple of tuples) 2025-07-17T07:11:28.8341838Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-07-17T07:11:28.8342310Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-07-17T07:11:28.8342735Z >>> output = unflatten(input) 2025-07-17T07:11:28.8343041Z >>> output.size() 2025-07-17T07:11:28.8343298Z torch.Size([2, 2, 5, 5]) 2025-07-17T07:11:28.8343473Z 2025-07-17T07:11:28.8343725Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.8344104Z 2025-07-17T07:11:28.8653858Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1718. 2025-07-17T07:11:28.8654875Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.8655603Z Creates a criterion that measures the triplet loss given input 2025-07-17T07:11:28.8656150Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-07-17T07:11:28.8656687Z positive, and negative examples, respectively), and a nonnegative, 2025-07-17T07:11:28.8657257Z real-valued function ("distance function") used to compute the relationship 2025-07-17T07:11:28.8657856Z between the anchor and positive example ("positive distance") and the 2025-07-17T07:11:28.8658353Z anchor and negative example ("negative distance"). 2025-07-17T07:11:28.8658618Z 2025-07-17T07:11:28.8658823Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-07-17T07:11:28.8659248Z can be described as: 2025-07-17T07:11:28.8659414Z 2025-07-17T07:11:28.8659519Z .. math:: 2025-07-17T07:11:28.8659791Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-07-17T07:11:28.8660186Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-07-17T07:11:28.8660514Z 2025-07-17T07:11:28.8660757Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-07-17T07:11:28.8661502Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-07-17T07:11:28.8662153Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-07-17T07:11:28.8662771Z between the positive and negative distances that is required for the loss to 2025-07-17T07:11:28.8663379Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-07-17T07:11:28.8663854Z that the distance function can handle. 2025-07-17T07:11:28.8664079Z 2025-07-17T07:11:28.8664196Z If :attr:`reduction` is not ``'none'`` 2025-07-17T07:11:28.8664529Z (default ``'mean'``), then: 2025-07-17T07:11:28.8664715Z 2025-07-17T07:11:28.8664812Z .. math:: 2025-07-17T07:11:28.8665023Z \ell(x, y) = 2025-07-17T07:11:28.8665267Z \begin{cases} 2025-07-17T07:11:28.8665628Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-07-17T07:11:28.8666140Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-07-17T07:11:28.8666532Z \end{cases} 2025-07-17T07:11:28.8666684Z 2025-07-17T07:11:28.8666916Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-07-17T07:11:28.8667723Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-07-17T07:11:28.8668295Z 2025-07-17T07:11:28.8668388Z Args: 2025-07-17T07:11:28.8668788Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-07-17T07:11:28.8669377Z quantifies the closeness of two tensors. If not specified, 2025-07-17T07:11:28.8669864Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-07-17T07:11:28.8670415Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-07-17T07:11:28.8671093Z between the positive and negative distances required for the loss to be 0. Larger 2025-07-17T07:11:28.8672020Z margins penalize cases where the negative examples are not distant enough from the 2025-07-17T07:11:28.8672601Z anchors, relative to the positives. Default: :math:`1`. 2025-07-17T07:11:28.8673147Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-07-17T07:11:28.8673783Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-07-17T07:11:28.8674415Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-07-17T07:11:28.8675039Z negative example than the anchor is, swaps the positive example and the anchor in 2025-07-17T07:11:28.8675576Z the loss computation. Default: ``False``. 2025-07-17T07:11:28.8676113Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-07-17T07:11:28.8676792Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-07-17T07:11:28.8677272Z ``'mean'``: the sum of the output will be divided by the number of 2025-07-17T07:11:28.8677825Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-07-17T07:11:28.8678187Z 2025-07-17T07:11:28.8678191Z 2025-07-17T07:11:28.8678287Z Shape: 2025-07-17T07:11:28.8678650Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-07-17T07:11:28.8679149Z as supported by the distance function. 2025-07-17T07:11:28.8679646Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-07-17T07:11:28.8680117Z otherwise. 2025-07-17T07:11:28.8680274Z 2025-07-17T07:11:28.8680361Z Examples: 2025-07-17T07:11:28.8680504Z 2025-07-17T07:11:28.8680608Z >>> # Initialize embeddings 2025-07-17T07:11:28.8680982Z >>> embedding = nn.Embedding(1000, 128) 2025-07-17T07:11:28.8681327Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-07-17T07:11:28.8681702Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-07-17T07:11:28.8682152Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-07-17T07:11:28.8682513Z >>> anchor = embedding(anchor_ids) 2025-07-17T07:11:28.8682843Z >>> positive = embedding(positive_ids) 2025-07-17T07:11:28.8683192Z >>> negative = embedding(negative_ids) 2025-07-17T07:11:28.8683508Z >>> 2025-07-17T07:11:28.8683723Z >>> # Built-in Distance Function 2025-07-17T07:11:28.8684034Z >>> triplet_loss = \ 2025-07-17T07:11:28.8684490Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-07-17T07:11:28.8685129Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T07:11:28.8685508Z >>> output.backward() 2025-07-17T07:11:28.8685767Z >>> 2025-07-17T07:11:28.8685977Z >>> # Custom Distance Function 2025-07-17T07:11:28.8686287Z >>> def l_infinity(x1, x2): 2025-07-17T07:11:28.8686631Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-07-17T07:11:28.8686988Z >>> 2025-07-17T07:11:28.8687287Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-07-17T07:11:28.8687693Z >>> triplet_loss = ( 2025-07-17T07:11:28.8688145Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-07-17T07:11:28.8688694Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T07:11:28.8689061Z >>> output.backward() 2025-07-17T07:11:28.8689316Z >>> 2025-07-17T07:11:28.8689535Z >>> # Custom Distance Function (Lambda) 2025-07-17T07:11:28.8689857Z >>> triplet_loss = ( 2025-07-17T07:11:28.8690158Z >>> nn.TripletMarginWithDistanceLoss( 2025-07-17T07:11:28.8690608Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-07-17T07:11:28.8691099Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T07:11:28.8691471Z >>> output.backward() 2025-07-17T07:11:28.8691638Z 2025-07-17T07:11:28.8691740Z Reference: 2025-07-17T07:11:28.8692177Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-07-17T07:11:28.8692835Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-07-17T07:11:28.8693259Z 2025-07-17T07:11:28.8693618Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-07-17T07:11:28.8694002Z 2025-07-17T07:11:28.8694550Z msg = Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1852. 2025-07-17T07:11:28.8695518Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.8696051Z The Connectionist Temporal Classification loss. 2025-07-17T07:11:28.8696316Z 2025-07-17T07:11:28.8696694Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-07-17T07:11:28.8697612Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-07-17T07:11:28.8698428Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-07-17T07:11:28.8699192Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-07-17T07:11:28.8699624Z 2025-07-17T07:11:28.8699709Z Args: 2025-07-17T07:11:28.8700002Z blank (int, optional): blank label. Default :math:`0`. 2025-07-17T07:11:28.8700526Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-07-17T07:11:28.8701085Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-07-17T07:11:28.8701606Z ``'mean'``: the output losses will be divided by the target lengths and 2025-07-17T07:11:28.8702179Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-07-17T07:11:28.8702687Z Default: ``'mean'`` 2025-07-17T07:11:28.8702989Z zero_infinity (bool, optional): 2025-07-17T07:11:28.8703461Z Whether to zero infinite losses and the associated gradients. 2025-07-17T07:11:28.8703867Z Default: ``False`` 2025-07-17T07:11:28.8704240Z Infinite losses mainly occur when the inputs are too short 2025-07-17T07:11:28.8704657Z to be aligned to the targets. 2025-07-17T07:11:28.8704872Z 2025-07-17T07:11:28.8704957Z Shape: 2025-07-17T07:11:28.8705270Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-07-17T07:11:28.8705702Z where :math:`T = \text{input length}`, 2025-07-17T07:11:28.8706055Z :math:`N = \text{batch size}`, and 2025-07-17T07:11:28.8706425Z :math:`C = \text{number of classes (including blank)}`. 2025-07-17T07:11:28.8706925Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-07-17T07:11:28.8707415Z :func:`torch.nn.functional.log_softmax`). 2025-07-17T07:11:28.8707780Z - Targets: Tensor of size :math:`(N, S)` or 2025-07-17T07:11:28.8708184Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-07-17T07:11:28.8708586Z where :math:`N = \text{batch size}` and 2025-07-17T07:11:28.8708979Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-07-17T07:11:28.8709463Z It represents the target sequences. Each element in the target 2025-07-17T07:11:28.8710023Z sequence is a class index. And the target index cannot be blank (default=0). 2025-07-17T07:11:28.8710545Z In the :math:`(N, S)` form, targets are padded to the 2025-07-17T07:11:28.8710944Z length of the longest sequence, and stacked. 2025-07-17T07:11:28.8711389Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-07-17T07:11:28.8711842Z the targets are assumed to be un-padded and 2025-07-17T07:11:28.8712201Z concatenated within 1 dimension. 2025-07-17T07:11:28.8712633Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-07-17T07:11:28.8713167Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-07-17T07:11:28.8713695Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-07-17T07:11:28.8714236Z for each sequence to achieve masking under the assumption that sequences 2025-07-17T07:11:28.8714700Z are padded to equal lengths. 2025-07-17T07:11:28.8715123Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-07-17T07:11:28.8715660Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-07-17T07:11:28.8716228Z Lengths are specified for each sequence to achieve masking under the 2025-07-17T07:11:28.8716824Z assumption that sequences are padded to equal lengths. If target shape is 2025-07-17T07:11:28.8717381Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-07-17T07:11:28.8717955Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-07-17T07:11:28.8718523Z each target in a batch. Lengths must each be :math:`\leq S` 2025-07-17T07:11:28.8719064Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-07-17T07:11:28.8719657Z targets, the target_lengths must add up to the total length of the tensor. 2025-07-17T07:11:28.8720213Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-07-17T07:11:28.8720764Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-07-17T07:11:28.8721324Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-07-17T07:11:28.8721639Z 2025-07-17T07:11:28.8721726Z Examples: 2025-07-17T07:11:28.8721869Z 2025-07-17T07:11:28.8722005Z >>> # Target are to be padded 2025-07-17T07:11:28.8722333Z >>> T = 50 # Input sequence length 2025-07-17T07:11:28.8722691Z >>> C = 20 # Number of classes (including blank) 2025-07-17T07:11:28.8723029Z >>> N = 16 # Batch size 2025-07-17T07:11:28.8723502Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-07-17T07:11:28.8724051Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-07-17T07:11:28.8724434Z >>> 2025-07-17T07:11:28.8724769Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-07-17T07:11:28.8725308Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-07-17T07:11:28.8725717Z >>> 2025-07-17T07:11:28.8726045Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T07:11:28.8726582Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-07-17T07:11:28.8727012Z >>> 2025-07-17T07:11:28.8727349Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-07-17T07:11:28.8727812Z >>> target_lengths = torch.randint( 2025-07-17T07:11:28.8728135Z ... low=S_min, 2025-07-17T07:11:28.8728390Z ... high=S, 2025-07-17T07:11:28.8728642Z ... size=(N,), 2025-07-17T07:11:28.8728911Z ... dtype=torch.long, 2025-07-17T07:11:28.8729178Z ... ) 2025-07-17T07:11:28.8729410Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T07:11:28.8729798Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T07:11:28.8730190Z >>> loss.backward() 2025-07-17T07:11:28.8730448Z >>> 2025-07-17T07:11:28.8730656Z >>> 2025-07-17T07:11:28.8730888Z >>> # Target are to be un-padded 2025-07-17T07:11:28.8731203Z >>> T = 50 # Input sequence length 2025-07-17T07:11:28.8731560Z >>> C = 20 # Number of classes (including blank) 2025-07-17T07:11:28.8731903Z >>> N = 16 # Batch size 2025-07-17T07:11:28.8732167Z >>> 2025-07-17T07:11:28.8732488Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-07-17T07:11:28.8733020Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-07-17T07:11:28.8733558Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-07-17T07:11:28.8733988Z >>> 2025-07-17T07:11:28.8734312Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T07:11:28.8734868Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-07-17T07:11:28.8750713Z >>> target = torch.randint( 2025-07-17T07:11:28.8751068Z ... low=1, 2025-07-17T07:11:28.8751324Z ... high=C, 2025-07-17T07:11:28.8751610Z ... size=(sum(target_lengths),), 2025-07-17T07:11:28.8751932Z ... dtype=torch.long, 2025-07-17T07:11:28.8752219Z ... ) 2025-07-17T07:11:28.8752473Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T07:11:28.8752944Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T07:11:28.8753365Z >>> loss.backward() 2025-07-17T07:11:28.8753633Z >>> 2025-07-17T07:11:28.8753855Z >>> 2025-07-17T07:11:28.8754160Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-07-17T07:11:28.8754584Z >>> T = 50 # Input sequence length 2025-07-17T07:11:28.8754947Z >>> C = 20 # Number of classes (including blank) 2025-07-17T07:11:28.8755271Z >>> 2025-07-17T07:11:28.8755593Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-07-17T07:11:28.8756052Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-07-17T07:11:28.8756495Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-07-17T07:11:28.8756984Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-07-17T07:11:28.8757381Z >>> 2025-07-17T07:11:28.8757708Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T07:11:28.8758254Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-07-17T07:11:28.8758790Z >>> target = torch.randint( 2025-07-17T07:11:28.8759095Z ... low=1, 2025-07-17T07:11:28.8759326Z ... high=C, 2025-07-17T07:11:28.8759587Z ... size=(target_lengths,), 2025-07-17T07:11:28.8759904Z ... dtype=torch.long, 2025-07-17T07:11:28.8760169Z ... ) 2025-07-17T07:11:28.8760402Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T07:11:28.8760791Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T07:11:28.8761189Z >>> loss.backward() 2025-07-17T07:11:28.8761381Z 2025-07-17T07:11:28.8761468Z Reference: 2025-07-17T07:11:28.8761784Z A. Graves et al.: Connectionist Temporal Classification: 2025-07-17T07:11:28.8762311Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-07-17T07:11:28.8762828Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-07-17T07:11:28.8763112Z 2025-07-17T07:11:28.8763197Z Note: 2025-07-17T07:11:28.8763571Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-07-17T07:11:28.8764186Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-07-17T07:11:28.8764796Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-07-17T07:11:28.8765254Z dtype :attr:`torch.int32`. 2025-07-17T07:11:28.8765456Z 2025-07-17T07:11:28.8765744Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-07-17T07:11:28.8766139Z 2025-07-17T07:11:28.8766144Z 2025-07-17T07:11:28.8766226Z Note: 2025-07-17T07:11:28.8766601Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-07-17T07:11:28.8767238Z may select a nondeterministic algorithm to increase performance. If this is 2025-07-17T07:11:28.8767878Z undesirable, you can try to make the operation deterministic (potentially at 2025-07-17T07:11:28.8768477Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-07-17T07:11:28.8768923Z True``. 2025-07-17T07:11:28.8769262Z Please see the notes on :doc:`/notes/randomness` for background. 2025-07-17T07:11:28.8769654Z 2025-07-17T07:11:28.8770028Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.8770400Z 2025-07-17T07:11:28.9186078Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=406. 2025-07-17T07:11:28.9187018Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.9187551Z Computes a partial inverse of :class:`MaxPool2d`. 2025-07-17T07:11:28.9187825Z 2025-07-17T07:11:28.9188100Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-07-17T07:11:28.9188595Z 2025-07-17T07:11:28.9188822Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-07-17T07:11:28.9189427Z including the indices of the maximal values and computes a partial inverse 2025-07-17T07:11:28.9189933Z in which all non-maximal values are set to zero. 2025-07-17T07:11:28.9190207Z 2025-07-17T07:11:28.9190294Z Note: 2025-07-17T07:11:28.9190748Z This operation may behave nondeterministically when the input indices has repeat values. 2025-07-17T07:11:28.9191582Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-07-17T07:11:28.9192081Z 2025-07-17T07:11:28.9192315Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-07-17T07:11:28.9192849Z sizes. Hence, the inversion process can get ambiguous. 2025-07-17T07:11:28.9193387Z To accommodate this, you can provide the needed output size 2025-07-17T07:11:28.9193916Z as an additional argument :attr:`output_size` in the forward call. 2025-07-17T07:11:28.9194363Z See the Inputs and Example below. 2025-07-17T07:11:28.9194608Z 2025-07-17T07:11:28.9194761Z Args: 2025-07-17T07:11:28.9195074Z kernel_size (int or tuple): Size of the max pooling window. 2025-07-17T07:11:28.9195540Z stride (int or tuple): Stride of the max pooling window. 2025-07-17T07:11:28.9195968Z It is set to :attr:`kernel_size` by default. 2025-07-17T07:11:28.9196407Z padding (int or tuple): Padding that was added to the input 2025-07-17T07:11:28.9196705Z 2025-07-17T07:11:28.9196810Z Inputs: 2025-07-17T07:11:28.9197049Z - `input`: the input Tensor to invert 2025-07-17T07:11:28.9197491Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-07-17T07:11:28.9197979Z - `output_size` (optional): the targeted output size 2025-07-17T07:11:28.9198256Z 2025-07-17T07:11:28.9198341Z Shape: 2025-07-17T07:11:28.9198654Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-07-17T07:11:28.9199185Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-07-17T07:11:28.9199519Z 2025-07-17T07:11:28.9199622Z .. math:: 2025-07-17T07:11:28.9200027Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-07-17T07:11:28.9200426Z 2025-07-17T07:11:28.9200514Z .. math:: 2025-07-17T07:11:28.9200914Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-07-17T07:11:28.9201296Z 2025-07-17T07:11:28.9201468Z or as given by :attr:`output_size` in the call operator 2025-07-17T07:11:28.9201759Z 2025-07-17T07:11:28.9201849Z Example:: 2025-07-17T07:11:28.9201991Z 2025-07-17T07:11:28.9202153Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-07-17T07:11:28.9202562Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-07-17T07:11:28.9202922Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-07-17T07:11:28.9203283Z [ 5., 6., 7., 8.], 2025-07-17T07:11:28.9203621Z [ 9., 10., 11., 12.], 2025-07-17T07:11:28.9203958Z [13., 14., 15., 16.]]]]) 2025-07-17T07:11:28.9204291Z >>> output, indices = pool(input) 2025-07-17T07:11:28.9204625Z >>> unpool(output, indices) 2025-07-17T07:11:28.9204934Z tensor([[[[ 0., 0., 0., 0.], 2025-07-17T07:11:28.9205232Z [ 0., 6., 0., 8.], 2025-07-17T07:11:28.9205535Z [ 0., 0., 0., 0.], 2025-07-17T07:11:28.9205841Z [ 0., 14., 0., 16.]]]]) 2025-07-17T07:11:28.9206254Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-07-17T07:11:28.9206733Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-07-17T07:11:28.9207139Z [ 6., 7., 8., 9., 10.], 2025-07-17T07:11:28.9207476Z [11., 12., 13., 14., 15.], 2025-07-17T07:11:28.9207806Z [16., 17., 18., 19., 20.]]]]) 2025-07-17T07:11:28.9208152Z >>> output, indices = pool(input) 2025-07-17T07:11:28.9208556Z >>> # This call will not work without specifying output_size 2025-07-17T07:11:28.9208993Z >>> unpool(output, indices, output_size=input.size()) 2025-07-17T07:11:28.9209377Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-07-17T07:11:28.9209687Z [ 0., 7., 0., 9., 0.], 2025-07-17T07:11:28.9209990Z [ 0., 0., 0., 0., 0.], 2025-07-17T07:11:28.9210286Z [ 0., 17., 0., 19., 0.]]]]) 2025-07-17T07:11:28.9210507Z 2025-07-17T07:11:28.9210511Z 2025-07-17T07:11:28.9210618Z 2025-07-17T07:11:28.9210985Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.9211358Z 2025-07-17T07:11:28.9450533Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=272. 2025-07-17T07:11:28.9451448Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.9452151Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-07-17T07:11:28.9452587Z 2025-07-17T07:11:28.9452923Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-07-17T07:11:28.9453476Z and with 2D inputs, this class 2025-07-17T07:11:28.9453677Z 2025-07-17T07:11:28.9453983Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-07-17T07:11:28.9454725Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-07-17T07:11:28.9455558Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-07-17T07:11:28.9455980Z 2025-07-17T07:11:28.9456373Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-07-17T07:11:28.9456959Z operations. 2025-07-17T07:11:28.9457096Z 2025-07-17T07:11:28.9457356Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-07-17T07:11:28.9457977Z pass. This scales the output of the Embedding before performing a weighted 2025-07-17T07:11:28.9458596Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-07-17T07:11:28.9459205Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-07-17T07:11:28.9459675Z :attr:`per_sample_weights`. 2025-07-17T07:11:28.9459879Z 2025-07-17T07:11:28.9459962Z Args: 2025-07-17T07:11:28.9460270Z num_embeddings (int): size of the dictionary of embeddings 2025-07-17T07:11:28.9460732Z embedding_dim (int): the size of each embedding vector 2025-07-17T07:11:28.9461346Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-07-17T07:11:28.9461940Z is renormalized to have norm :attr:`max_norm`. 2025-07-17T07:11:28.9462547Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-07-17T07:11:28.9463351Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-07-17T07:11:28.9463975Z the words in the mini-batch. Default ``False``. 2025-07-17T07:11:28.9464457Z Note: this option is not supported when ``mode="max"``. 2025-07-17T07:11:28.9465010Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-07-17T07:11:28.9465666Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-07-17T07:11:28.9466253Z into consideration. ``"mean"`` computes the average of the values 2025-07-17T07:11:28.9466784Z in the bag, ``"max"`` computes the max value over each bag. 2025-07-17T07:11:28.9467196Z Default: ``"mean"`` 2025-07-17T07:11:28.9467757Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-07-17T07:11:28.9468466Z Notes for more details regarding sparse gradients. Note: this option is not 2025-07-17T07:11:28.9468984Z supported when ``mode="max"``. 2025-07-17T07:11:28.9469602Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-07-17T07:11:28.9470375Z is equivalent to the size of `indices`. This matches the CSR format. 2025-07-17T07:11:28.9471130Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-07-17T07:11:28.9472023Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-07-17T07:11:28.9472669Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-07-17T07:11:28.9473320Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-07-17T07:11:28.9473972Z zeros, but can be updated to another value to be used as the padding vector. 2025-07-17T07:11:28.9474584Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-07-17T07:11:28.9475080Z reduction. 2025-07-17T07:11:28.9475312Z 2025-07-17T07:11:28.9475402Z Attributes: 2025-07-17T07:11:28.9475867Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-07-17T07:11:28.9476443Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-07-17T07:11:28.9476713Z 2025-07-17T07:11:28.9476811Z Examples:: 2025-07-17T07:11:28.9476944Z 2025-07-17T07:11:28.9477129Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-07-17T07:11:28.9477580Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-07-17T07:11:28.9477970Z >>> # a batch of 2 samples of 4 indices each 2025-07-17T07:11:28.9478400Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-07-17T07:11:28.9478860Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-07-17T07:11:28.9479256Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:28.9479635Z >>> embedding_sum(input, offsets) 2025-07-17T07:11:28.9479747Z tensor([[-0.8861, -5.4350, -0.0523], 2025-07-17T07:11:28.9479859Z [ 1.1306, -2.5798, -1.0044]]) 2025-07-17T07:11:28.9479864Z 2025-07-17T07:11:28.9479976Z >>> # Example with padding_idx 2025-07-17T07:11:28.9480201Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-07-17T07:11:28.9480384Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-07-17T07:11:28.9480533Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-07-17T07:11:28.9480659Z >>> embedding_sum(input, offsets) 2025-07-17T07:11:28.9480761Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-07-17T07:11:28.9480859Z [-0.7082, 3.2145, -2.6251]]) 2025-07-17T07:11:28.9480876Z 2025-07-17T07:11:28.9481048Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-07-17T07:11:28.9481194Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-07-17T07:11:28.9481411Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-07-17T07:11:28.9481514Z embedding.weight, 2025-07-17T07:11:28.9481644Z padding_idx=embedding.padding_idx, 2025-07-17T07:11:28.9481752Z mode='sum') 2025-07-17T07:11:28.9481833Z 2025-07-17T07:11:28.9482085Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.9482090Z 2025-07-17T07:11:28.9520675Z msg = Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py line=186. 2025-07-17T07:11:28.9520944Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.9521103Z Take in and process masked source/target sequences. 2025-07-17T07:11:28.9521108Z 2025-07-17T07:11:28.9521199Z .. note:: 2025-07-17T07:11:28.9521203Z 2025-07-17T07:11:28.9521678Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-07-17T07:11:28.9521827Z not allowed to participate in the attention, 2025-07-17T07:11:28.9522086Z which is the opposite of the definition for :attr:`attn_mask` 2025-07-17T07:11:28.9522288Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-07-17T07:11:28.9522292Z 2025-07-17T07:11:28.9522377Z Args: 2025-07-17T07:11:28.9522513Z src: the sequence to the encoder (required). 2025-07-17T07:11:28.9522661Z tgt: the sequence to the decoder (required). 2025-07-17T07:11:28.9522864Z src_mask: the additive mask for the src sequence (optional). 2025-07-17T07:11:28.9523059Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-07-17T07:11:28.9523267Z memory_mask: the additive mask for the encoder output (optional). 2025-07-17T07:11:28.9523511Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-07-17T07:11:28.9523737Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-07-17T07:11:28.9523993Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-07-17T07:11:28.9524212Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-07-17T07:11:28.9524357Z Default: ``None``; try to detect a causal mask. 2025-07-17T07:11:28.9524448Z Warning: 2025-07-17T07:11:28.9524620Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-07-17T07:11:28.9524798Z the causal mask. Providing incorrect hints can result in 2025-07-17T07:11:28.9524974Z incorrect execution, including forward and backward 2025-07-17T07:11:28.9525075Z compatibility. 2025-07-17T07:11:28.9525278Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-07-17T07:11:28.9525439Z Default: ``None``; try to detect a causal mask. 2025-07-17T07:11:28.9525527Z Warning: 2025-07-17T07:11:28.9525683Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-07-17T07:11:28.9525876Z the causal mask. Providing incorrect hints can result in 2025-07-17T07:11:28.9526038Z incorrect execution, including forward and backward 2025-07-17T07:11:28.9526148Z compatibility. 2025-07-17T07:11:28.9526324Z memory_is_causal: If specified, applies a causal mask as 2025-07-17T07:11:28.9526420Z ``memory_mask``. 2025-07-17T07:11:28.9526531Z Default: ``False``. 2025-07-17T07:11:28.9526619Z Warning: 2025-07-17T07:11:28.9526749Z ``memory_is_causal`` provides a hint that 2025-07-17T07:11:28.9526928Z ``memory_mask`` is the causal mask. Providing incorrect 2025-07-17T07:11:28.9527083Z hints can result in incorrect execution, including 2025-07-17T07:11:28.9527240Z forward and backward compatibility. 2025-07-17T07:11:28.9527257Z 2025-07-17T07:11:28.9527343Z Shape: 2025-07-17T07:11:28.9527596Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-07-17T07:11:28.9527724Z `(N, S, E)` if `batch_first=True`. 2025-07-17T07:11:28.9527960Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-07-17T07:11:28.9528070Z `(N, T, E)` if `batch_first=True`. 2025-07-17T07:11:28.9528274Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-07-17T07:11:28.9528460Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-07-17T07:11:28.9528580Z - memory_mask: :math:`(T, S)`. 2025-07-17T07:11:28.9528824Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-07-17T07:11:28.9529091Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-07-17T07:11:28.9529363Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-07-17T07:11:28.9529416Z 2025-07-17T07:11:28.9529730Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-07-17T07:11:28.9529940Z positions. If a BoolTensor is provided, positions with ``True`` 2025-07-17T07:11:28.9530214Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-07-17T07:11:28.9530380Z is provided, it will be added to the attention weight. 2025-07-17T07:11:28.9530694Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-07-17T07:11:28.9530905Z the attention. If a BoolTensor is provided, the positions with the 2025-07-17T07:11:28.9531219Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-07-17T07:11:28.9531226Z 2025-07-17T07:11:28.9531494Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-07-17T07:11:28.9531610Z `(N, T, E)` if `batch_first=True`. 2025-07-17T07:11:28.9531615Z 2025-07-17T07:11:28.9531869Z Note: Due to the multi-head attention architecture in the transformer model, 2025-07-17T07:11:28.9532103Z the output sequence length of a transformer is same as the input sequence 2025-07-17T07:11:28.9532218Z (i.e. target) length of the decoder. 2025-07-17T07:11:28.9532223Z 2025-07-17T07:11:28.9532558Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-07-17T07:11:28.9532690Z batch size, :math:`E` is the feature number 2025-07-17T07:11:28.9532695Z 2025-07-17T07:11:28.9532801Z Examples: 2025-07-17T07:11:28.9532903Z >>> # xdoctest: +SKIP 2025-07-17T07:11:28.9533015Z >>> output = transformer_model( 2025-07-17T07:11:28.9533174Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-07-17T07:11:28.9533260Z ... ) 2025-07-17T07:11:28.9533344Z 2025-07-17T07:11:28.9533610Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.9533615Z 2025-07-17T07:11:28.9780572Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1766. 2025-07-17T07:11:28.9780880Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.9780885Z 2025-07-17T07:11:28.9781122Z Context manager for training with uneven inputs across processes in DDP. 2025-07-17T07:11:28.9781126Z 2025-07-17T07:11:28.9781360Z This context manager will keep track of already-joined DDP processes, 2025-07-17T07:11:28.9781570Z and "shadow" the forward and backward passes by inserting collective 2025-07-17T07:11:28.9781894Z communication operations to match with the ones created by non-joined 2025-07-17T07:11:28.9782131Z DDP processes. This will ensure each collective call has a corresponding 2025-07-17T07:11:28.9782348Z call by already-joined DDP processes, preventing hangs or errors that 2025-07-17T07:11:28.9782558Z would otherwise happen when training with uneven inputs across 2025-07-17T07:11:28.9782794Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-07-17T07:11:28.9783003Z specified to be ``True``, all trainers will throw an error once one rank 2025-07-17T07:11:28.9783214Z runs out of inputs, allowing these errors to be caught and handled 2025-07-17T07:11:28.9783328Z according to application logic. 2025-07-17T07:11:28.9783332Z 2025-07-17T07:11:28.9783562Z Once all DDP processes have joined, the context manager will broadcast 2025-07-17T07:11:28.9783787Z the model corresponding to the last joined process to all processes to 2025-07-17T07:11:28.9783976Z ensure the model is the same across all processes 2025-07-17T07:11:28.9784099Z (which is guaranteed by DDP). 2025-07-17T07:11:28.9784103Z 2025-07-17T07:11:28.9784368Z To use this to enable training with uneven inputs across processes, 2025-07-17T07:11:28.9784589Z simply wrap this context manager around your training loop. No further 2025-07-17T07:11:28.9784773Z modifications to the model or data loading is required. 2025-07-17T07:11:28.9784778Z 2025-07-17T07:11:28.9784873Z .. warning:: 2025-07-17T07:11:28.9785092Z If the model or training loop this context manager is wrapped around 2025-07-17T07:11:28.9785275Z has additional distributed collective operations, such as 2025-07-17T07:11:28.9785492Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-07-17T07:11:28.9785769Z ``throw_on_early_termination`` must be enabled. This is because this 2025-07-17T07:11:28.9785979Z context manager is not aware of non-DDP collective communication. 2025-07-17T07:11:28.9786151Z This flag will cause all ranks to throw when any one rank 2025-07-17T07:11:28.9786371Z exhausts inputs, allowing these errors to be caught and recovered 2025-07-17T07:11:28.9786473Z from across all ranks. 2025-07-17T07:11:28.9786478Z 2025-07-17T07:11:28.9786573Z Args: 2025-07-17T07:11:28.9786754Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-07-17T07:11:28.9786959Z gradients by the initial ``world_size`` DDP training was launched 2025-07-17T07:11:28.9787134Z with. If ``False``, will compute the effective world size 2025-07-17T07:11:28.9787319Z (number of ranks that have not depleted their inputs yet) and 2025-07-17T07:11:28.9787463Z divide gradients by that during allreduce. Set 2025-07-17T07:11:28.9787652Z ``divide_by_initial_world_size=True`` to ensure every input 2025-07-17T07:11:28.9787861Z sample including the uneven inputs have equal weight in terms of 2025-07-17T07:11:28.9788048Z how much they contribute to the global gradient. This is 2025-07-17T07:11:28.9788223Z achieved by always dividing the gradient by the initial 2025-07-17T07:11:28.9788412Z ``world_size`` even when we encounter uneven inputs. If you set 2025-07-17T07:11:28.9788597Z this to ``False``, we divide the gradient by the remaining 2025-07-17T07:11:28.9788792Z number of nodes. This ensures parity with training on a smaller 2025-07-17T07:11:28.9788973Z ``world_size`` although it also means the uneven inputs would 2025-07-17T07:11:28.9789180Z contribute more towards the global gradient. Typically, you 2025-07-17T07:11:28.9789366Z would want to set this to ``True`` for cases where the last few 2025-07-17T07:11:28.9789572Z inputs of your training job are uneven. In extreme cases, where 2025-07-17T07:11:28.9789755Z there is a large discrepancy in the number of inputs, setting 2025-07-17T07:11:28.9789898Z this to ``False`` might provide better results. 2025-07-17T07:11:28.9790169Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-07-17T07:11:28.9790339Z in ``enable=False`` to disable in cases where you know that 2025-07-17T07:11:28.9790530Z inputs are even across participating processes. Default is 2025-07-17T07:11:28.9790626Z ``True``. 2025-07-17T07:11:28.9790807Z throw_on_early_termination (bool): Whether to throw an error 2025-07-17T07:11:28.9790992Z or continue training when at least one rank has exhausted 2025-07-17T07:11:28.9791175Z inputs. If ``True``, will throw upon the first rank reaching end 2025-07-17T07:11:28.9791342Z of data. If ``False``, will continue training with a smaller 2025-07-17T07:11:28.9791545Z effective world size until all ranks are joined. Note that if 2025-07-17T07:11:28.9791666Z this flag is specified, then the flag 2025-07-17T07:11:28.9791839Z ``divide_by_initial_world_size`` would be ignored. Default 2025-07-17T07:11:28.9791984Z is ``False``. 2025-07-17T07:11:28.9791988Z 2025-07-17T07:11:28.9791993Z 2025-07-17T07:11:28.9792083Z Example:: 2025-07-17T07:11:28.9792088Z 2025-07-17T07:11:28.9792211Z >>> # xdoctest: +SKIP("Distributed") 2025-07-17T07:11:28.9792353Z >>> import torch 2025-07-17T07:11:28.9792469Z >>> import torch.distributed as dist 2025-07-17T07:11:28.9792571Z >>> import os 2025-07-17T07:11:28.9792693Z >>> import torch.multiprocessing as mp 2025-07-17T07:11:28.9792794Z >>> import torch.nn as nn 2025-07-17T07:11:28.9792909Z >>> # On each spawned worker 2025-07-17T07:11:28.9793005Z >>> def worker(rank): 2025-07-17T07:11:28.9793180Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-07-17T07:11:28.9793303Z >>> torch.cuda.set_device(rank) 2025-07-17T07:11:28.9793437Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-07-17T07:11:28.9793620Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-07-17T07:11:28.9793761Z >>> model, device_ids=[rank], output_device=rank 2025-07-17T07:11:28.9793846Z >>> ) 2025-07-17T07:11:28.9793988Z >>> # Rank 1 gets one more input than rank 0. 2025-07-17T07:11:28.9794178Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-07-17T07:11:28.9794278Z >>> with model.join(): 2025-07-17T07:11:28.9794387Z >>> for _ in range(5): 2025-07-17T07:11:28.9794487Z >>> for inp in inputs: 2025-07-17T07:11:28.9794600Z >>> loss = model(inp).sum() 2025-07-17T07:11:28.9794714Z >>> loss.backward() 2025-07-17T07:11:28.9794901Z >>> # Without the join() API, the below synchronization will hang 2025-07-17T07:11:28.9795056Z >>> # blocking for rank 1's allreduce to complete. 2025-07-17T07:11:28.9795185Z >>> torch.cuda.synchronize(device=rank) 2025-07-17T07:11:28.9795189Z 2025-07-17T07:11:28.9795442Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.9795450Z 2025-07-17T07:11:28.9808028Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2057. 2025-07-17T07:11:28.9808298Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:28.9808303Z 2025-07-17T07:11:28.9808622Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-07-17T07:11:28.9808627Z 2025-07-17T07:11:28.9808835Z Registers an optimizer with DDP such that the optimization for a 2025-07-17T07:11:28.9809054Z parameter will run immediately when that parameter's gradient is 2025-07-17T07:11:28.9809257Z finished with reduction, instead of waiting for all parameters' 2025-07-17T07:11:28.9809473Z gradients to finish reduction. This can result in a training speedup 2025-07-17T07:11:28.9809702Z depending on your workload since the optimizer can run while gradient 2025-07-17T07:11:28.9809980Z reduction for other parameters are still ongoing. In addition, this has 2025-07-17T07:11:28.9810203Z the potential to reduce peak memory consumption during training, as it 2025-07-17T07:11:28.9810416Z only needs to load the per-parameter optimizer states of a single 2025-07-17T07:11:28.9810628Z parameter at a time, instead of loading all per-parameter optimizer 2025-07-17T07:11:28.9810719Z states at once. 2025-07-17T07:11:28.9810738Z 2025-07-17T07:11:28.9810820Z Args: 2025-07-17T07:11:28.9811015Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-07-17T07:11:28.9811128Z as a fused optimizer. 2025-07-17T07:11:28.9811293Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-07-17T07:11:28.9811505Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-07-17T07:11:28.9811741Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-07-17T07:11:28.9811946Z Optimizers. If this is omitted, all DDP model parameters will be 2025-07-17T07:11:28.9812093Z optimized. 2025-07-17T07:11:28.9812287Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-07-17T07:11:28.9812291Z 2025-07-17T07:11:28.9812382Z .. warning :: 2025-07-17T07:11:28.9812656Z _register_fused_optim should only be called once on a DDP instance, 2025-07-17T07:11:28.9812865Z and registering multiple fused optimizers for the same DDP model 2025-07-17T07:11:28.9813004Z is not currently supported. Please ping 2025-07-17T07:11:28.9813236Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T07:11:28.9813328Z for your use case. 2025-07-17T07:11:28.9813333Z 2025-07-17T07:11:28.9813431Z .. warning :: 2025-07-17T07:11:28.9813622Z _register_fused_optim and register_comm_hook currently do not 2025-07-17T07:11:28.9813834Z compose together, meaning that custom DDP communication hooks are 2025-07-17T07:11:28.9814015Z not supported with overlapped optimizers. Please ping 2025-07-17T07:11:28.9814244Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T07:11:28.9814335Z for your use case. 2025-07-17T07:11:28.9814351Z 2025-07-17T07:11:28.9814436Z .. warning :: 2025-07-17T07:11:28.9814663Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-07-17T07:11:28.9814798Z with overlapped optimizer. Please ping 2025-07-17T07:11:28.9815022Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T07:11:28.9815112Z for your use case. 2025-07-17T07:11:28.9815116Z 2025-07-17T07:11:28.9815300Z Example:: 2025-07-17T07:11:28.9815305Z 2025-07-17T07:11:28.9815435Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-07-17T07:11:28.9815745Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-07-17T07:11:28.9815946Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-07-17T07:11:28.9816037Z >>> lr = 1e-2 2025-07-17T07:11:28.9816147Z >>> betas = (0.9, 0.99) 2025-07-17T07:11:28.9816237Z >>> eps = 1e-6 2025-07-17T07:11:28.9816460Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-07-17T07:11:28.9816596Z >>> # Example with subset of parameters 2025-07-17T07:11:28.9816728Z >>> params_to_opt = [list(net.parameters())[0]] 2025-07-17T07:11:28.9816835Z >>> net._register_fused_optim( 2025-07-17T07:11:28.9817082Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-07-17T07:11:28.9817165Z ... ) 2025-07-17T07:11:28.9817169Z 2025-07-17T07:11:28.9817436Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:28.9817440Z 2025-07-17T07:11:29.0056888Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=14. 2025-07-17T07:11:29.0057151Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.0057462Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-07-17T07:11:29.0057466Z 2025-07-17T07:11:29.0057741Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-07-17T07:11:29.0058030Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-07-17T07:11:29.0058286Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-07-17T07:11:29.0058595Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-07-17T07:11:29.0058613Z 2025-07-17T07:11:29.0058702Z .. note:: 2025-07-17T07:11:29.0058940Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-07-17T07:11:29.0059192Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-07-17T07:11:29.0059401Z layer with 4d weight will be affected by ``model.to``, which does not 2025-07-17T07:11:29.0059679Z necessarily benefit from conversion to specified ``memory_format``. 2025-07-17T07:11:29.0059899Z One place we are confident in is that NHWC(channels_last) conversion for 2025-07-17T07:11:29.0060190Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-07-17T07:11:29.0060407Z even in cases where we have to apply permutation to input tensors. 2025-07-17T07:11:29.0060411Z 2025-07-17T07:11:29.0060635Z Hence our strategy here is to convert only the weight of convolution to 2025-07-17T07:11:29.0060750Z channels_last. This ensures that; 2025-07-17T07:11:29.0060978Z 1. Fast convolution kernels will be used, the benefit of which could 2025-07-17T07:11:29.0061208Z outweigh overhead of permutation (if input is not in the same format). 2025-07-17T07:11:29.0061455Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-07-17T07:11:29.0061569Z from memory_format conversion. 2025-07-17T07:11:29.0061579Z 2025-07-17T07:11:29.0061804Z The optimal case is that, layers between convolution layers are channels 2025-07-17T07:11:29.0062057Z last compatible. Input tensor would be permuted to channels last when it 2025-07-17T07:11:29.0062289Z encounters the first convolution layer and stay in that memory format. 2025-07-17T07:11:29.0062539Z Hence following convolutions will not need to permute its input tensor. 2025-07-17T07:11:29.0062544Z 2025-07-17T07:11:29.0062764Z In case where a channels last incompatible layer is between convolution 2025-07-17T07:11:29.0062978Z layers, we need to permute the input tensor back to contiguous format 2025-07-17T07:11:29.0063215Z for that layer. The input tensor will go through the remaining layers in 2025-07-17T07:11:29.0063439Z contiguous format and be permuted to channels last when it encounters 2025-07-17T07:11:29.0063646Z another convolution layer. There's no point in propagating that 2025-07-17T07:11:29.0063880Z permutation to an earlier layer, as most layers are quite agnostic to 2025-07-17T07:11:29.0063978Z ``memory_format``. 2025-07-17T07:11:29.0063983Z 2025-07-17T07:11:29.0064227Z This claim might change when PyTorch supports fusion of permutation, as 2025-07-17T07:11:29.0064447Z there might have been a better spot to fuse the permutation other than 2025-07-17T07:11:29.0064562Z immediately before a convolution. 2025-07-17T07:11:29.0064566Z 2025-07-17T07:11:29.0064660Z Args: 2025-07-17T07:11:29.0064872Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-07-17T07:11:29.0064982Z ``nn.Module`` 2025-07-17T07:11:29.0065131Z memory_format: user specified ``memory_format``, 2025-07-17T07:11:29.0065310Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-07-17T07:11:29.0065314Z 2025-07-17T07:11:29.0065413Z Returns: 2025-07-17T07:11:29.0065555Z The original module with updated ``nn.Conv2d`` 2025-07-17T07:11:29.0065593Z 2025-07-17T07:11:29.0065677Z Example: 2025-07-17T07:11:29.0065826Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:29.0065985Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-07-17T07:11:29.0066088Z >>> input = torch.randint( 2025-07-17T07:11:29.0066249Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-07-17T07:11:29.0066332Z ... ) 2025-07-17T07:11:29.0066445Z >>> model = nn.Sequential( 2025-07-17T07:11:29.0066556Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-07-17T07:11:29.0066658Z >>> # This is identical to: 2025-07-17T07:11:29.0066913Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-07-17T07:11:29.0067078Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-07-17T07:11:29.0067189Z ... model, torch.channels_last 2025-07-17T07:11:29.0067311Z ... ) 2025-07-17T07:11:29.0067411Z >>> out = model(input) 2025-07-17T07:11:29.0067492Z 2025-07-17T07:11:29.0067757Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.0067761Z 2025-07-17T07:11:29.0068487Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=93. 2025-07-17T07:11:29.0068750Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.0068960Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-07-17T07:11:29.0069246Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-07-17T07:11:29.0069516Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-07-17T07:11:29.0069782Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-07-17T07:11:29.0070095Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-07-17T07:11:29.0070099Z 2025-07-17T07:11:29.0070187Z .. note:: 2025-07-17T07:11:29.0070448Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-07-17T07:11:29.0070669Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-07-17T07:11:29.0070878Z layer with 4d weight will be affected by ``model.to``, which does not 2025-07-17T07:11:29.0071114Z necessarily benefit from conversion to specified ``memory_format``. 2025-07-17T07:11:29.0071349Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-07-17T07:11:29.0071580Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-07-17T07:11:29.0071942Z even in cases where we have to apply permutation to input tensors. 2025-07-17T07:11:29.0071947Z 2025-07-17T07:11:29.0072175Z Hence our strategy here is to convert only the weight of convolution to 2025-07-17T07:11:29.0072309Z channels_last_3d. This ensures that; 2025-07-17T07:11:29.0072526Z 1. Fast convolution kernels will be used, the benefit of which could 2025-07-17T07:11:29.0072777Z outweigh overhead of permutation (if input is not in the same format). 2025-07-17T07:11:29.0073010Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-07-17T07:11:29.0073123Z from memory_format conversion. 2025-07-17T07:11:29.0073127Z 2025-07-17T07:11:29.0073367Z The optimal case is that, layers between convolution layers are channels 2025-07-17T07:11:29.0073600Z last compatible. Input tensor would be permuted to channels last when it 2025-07-17T07:11:29.0073826Z encounters the first convolution layer and stay in that memory format. 2025-07-17T07:11:29.0074078Z Hence following convolutions will not need to permute its input tensor. 2025-07-17T07:11:29.0074084Z 2025-07-17T07:11:29.0074368Z In case where a channels last incompatible layer is between convolution 2025-07-17T07:11:29.0074593Z layers, we need to permute the input tensor back to contiguous format 2025-07-17T07:11:29.0074822Z for that layer. The input tensor will go through the remaining layers in 2025-07-17T07:11:29.0075047Z contiguous format and be permuted to channels last when it encounters 2025-07-17T07:11:29.0075270Z another convolution layer. There's no point in propagating that 2025-07-17T07:11:29.0075490Z permutation to an earlier layer, as most layers are quite agnostic to 2025-07-17T07:11:29.0075601Z ``memory_format``. 2025-07-17T07:11:29.0075606Z 2025-07-17T07:11:29.0075836Z This claim might change when PyTorch supports fusion of permutation, as 2025-07-17T07:11:29.0076054Z there might have been a better spot to fuse the permutation other than 2025-07-17T07:11:29.0076181Z immediately before a convolution. 2025-07-17T07:11:29.0076223Z 2025-07-17T07:11:29.0076307Z Args: 2025-07-17T07:11:29.0076521Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-07-17T07:11:29.0076630Z ``nn.Module`` 2025-07-17T07:11:29.0076840Z memory_format: user specified ``memory_format``, 2025-07-17T07:11:29.0077034Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-07-17T07:11:29.0077038Z 2025-07-17T07:11:29.0077124Z Returns: 2025-07-17T07:11:29.0077265Z The original module with updated ``nn.Conv3d`` 2025-07-17T07:11:29.0077270Z 2025-07-17T07:11:29.0077375Z Example: 2025-07-17T07:11:29.0077510Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:29.0077662Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-07-17T07:11:29.0077824Z >>> input = torch.randint( 2025-07-17T07:11:29.0078014Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-07-17T07:11:29.0078110Z ... ) 2025-07-17T07:11:29.0078213Z >>> model = nn.Sequential( 2025-07-17T07:11:29.0078322Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-07-17T07:11:29.0078431Z >>> # This is identical to: 2025-07-17T07:11:29.0078688Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-07-17T07:11:29.0078850Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-07-17T07:11:29.0078973Z ... model, torch.channels_last_3d 2025-07-17T07:11:29.0079055Z ... ) 2025-07-17T07:11:29.0079148Z >>> out = model(input) 2025-07-17T07:11:29.0079241Z 2025-07-17T07:11:29.0079494Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.0079499Z 2025-07-17T07:11:29.0164826Z msg = Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py line=424. 2025-07-17T07:11:29.0165121Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.0165287Z Register a parametrization to a tensor in a module. 2025-07-17T07:11:29.0165292Z 2025-07-17T07:11:29.0165568Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-07-17T07:11:29.0165844Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-07-17T07:11:29.0166114Z If the original tensor requires a gradient, the backward pass will differentiate 2025-07-17T07:11:29.0166398Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-07-17T07:11:29.0166403Z 2025-07-17T07:11:29.0166718Z The first time that a module registers a parametrization, this function will add an attribute 2025-07-17T07:11:29.0166962Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-07-17T07:11:29.0166966Z 2025-07-17T07:11:29.0167216Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-07-17T07:11:29.0167395Z ``module.parametrizations.weight``. 2025-07-17T07:11:29.0167400Z 2025-07-17T07:11:29.0167533Z The original tensor will be accessible under 2025-07-17T07:11:29.0167695Z ``module.parametrizations.weight.original``. 2025-07-17T07:11:29.0167700Z 2025-07-17T07:11:29.0167960Z Parametrizations may be concatenated by registering several parametrizations 2025-07-17T07:11:29.0168057Z on the same attribute. 2025-07-17T07:11:29.0168061Z 2025-07-17T07:11:29.0168309Z The training mode of a registered parametrization is updated on registration 2025-07-17T07:11:29.0168446Z to match the training mode of the host module 2025-07-17T07:11:29.0168451Z 2025-07-17T07:11:29.0168765Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-07-17T07:11:29.0168887Z using the context manager :func:`cached`. 2025-07-17T07:11:29.0168891Z 2025-07-17T07:11:29.0169131Z A :attr:`parametrization` may optionally implement a method with signature 2025-07-17T07:11:29.0169164Z 2025-07-17T07:11:29.0169278Z .. code-block:: python 2025-07-17T07:11:29.0169282Z 2025-07-17T07:11:29.0169496Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-07-17T07:11:29.0169548Z 2025-07-17T07:11:29.0169822Z This method is called on the unparametrized tensor when the first parametrization 2025-07-17T07:11:29.0170026Z is registered to compute the initial value of the original tensor. 2025-07-17T07:11:29.0170333Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-07-17T07:11:29.0170337Z 2025-07-17T07:11:29.0170661Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-07-17T07:11:29.0170946Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-07-17T07:11:29.0170950Z 2025-07-17T07:11:29.0171188Z It is possible for the first parametrization to depend on several inputs. 2025-07-17T07:11:29.0171431Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-07-17T07:11:29.0171661Z (see the example implementation of a ``RankOne`` parametrization below). 2025-07-17T07:11:29.0171669Z 2025-07-17T07:11:29.0172142Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-07-17T07:11:29.0172269Z with names ``original0``, ``original1``,... 2025-07-17T07:11:29.0172273Z 2025-07-17T07:11:29.0172372Z .. note:: 2025-07-17T07:11:29.0172377Z 2025-07-17T07:11:29.0172645Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-07-17T07:11:29.0172786Z once to perform a number of consistency checks. 2025-07-17T07:11:29.0173067Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-07-17T07:11:29.0173185Z and nothing will be called otherwise. 2025-07-17T07:11:29.0173192Z 2025-07-17T07:11:29.0173288Z .. note:: 2025-07-17T07:11:29.0173292Z 2025-07-17T07:11:29.0173494Z In most situations, ``right_inverse`` will be a function such that 2025-07-17T07:11:29.0173613Z ``forward(right_inverse(X)) == X`` (see 2025-07-17T07:11:29.0173905Z `right inverse `_). 2025-07-17T07:11:29.0174155Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-07-17T07:11:29.0174247Z to relax this. 2025-07-17T07:11:29.0174262Z 2025-07-17T07:11:29.0174345Z .. warning:: 2025-07-17T07:11:29.0174350Z 2025-07-17T07:11:29.0174622Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-07-17T07:11:29.0174890Z will register a number of new parameters. If such parametrization is registered 2025-07-17T07:11:29.0175152Z after the optimizer is created, these new parameters will need to be added manually 2025-07-17T07:11:29.0175425Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-07-17T07:11:29.0175500Z 2025-07-17T07:11:29.0175579Z Args: 2025-07-17T07:11:29.0175786Z module (nn.Module): module on which to register the parametrization 2025-07-17T07:11:29.0176015Z tensor_name (str): name of the parameter or buffer on which to register 2025-07-17T07:11:29.0176115Z the parametrization 2025-07-17T07:11:29.0176313Z parametrization (nn.Module): the parametrization to register 2025-07-17T07:11:29.0176410Z Keyword args: 2025-07-17T07:11:29.0176624Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-07-17T07:11:29.0176814Z may change the dtype and shape of the tensor. Default: `False` 2025-07-17T07:11:29.0177085Z Warning: the parametrization is not checked for consistency upon registration. 2025-07-17T07:11:29.0177203Z Enable this flag at your own risk. 2025-07-17T07:11:29.0177207Z 2025-07-17T07:11:29.0177333Z Raises: 2025-07-17T07:11:29.0177621Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-07-17T07:11:29.0177625Z 2025-07-17T07:11:29.0177709Z Examples: 2025-07-17T07:11:29.0177926Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-07-17T07:11:29.0178018Z >>> import torch 2025-07-17T07:11:29.0178119Z >>> import torch.nn as nn 2025-07-17T07:11:29.0178262Z >>> import torch.nn.utils.parametrize as P 2025-07-17T07:11:29.0178342Z >>> 2025-07-17T07:11:29.0178514Z >>> class Symmetric(nn.Module): 2025-07-17T07:11:29.0178643Z >>> def forward(self, X): 2025-07-17T07:11:29.0178820Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-07-17T07:11:29.0178903Z >>> 2025-07-17T07:11:29.0179010Z >>> def right_inverse(self, A): 2025-07-17T07:11:29.0179106Z >>> return A.triu() 2025-07-17T07:11:29.0179188Z >>> 2025-07-17T07:11:29.0179285Z >>> m = nn.Linear(5, 5) 2025-07-17T07:11:29.0179453Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-07-17T07:11:29.0179699Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-07-17T07:11:29.0179784Z True 2025-07-17T07:11:29.0179890Z >>> A = torch.rand(5, 5) 2025-07-17T07:11:29.0179998Z >>> A = A + A.T # A is now symmetric 2025-07-17T07:11:29.0180196Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-07-17T07:11:29.0180321Z >>> print(torch.allclose(m.weight, A)) 2025-07-17T07:11:29.0180402Z True 2025-07-17T07:11:29.0180407Z 2025-07-17T07:11:29.0180511Z >>> class RankOne(nn.Module): 2025-07-17T07:11:29.0180624Z >>> def forward(self, x, y): 2025-07-17T07:11:29.0180762Z >>> # Form a rank 1 matrix multiplying two vectors 2025-07-17T07:11:29.0180894Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-07-17T07:11:29.0180981Z >>> 2025-07-17T07:11:29.0181091Z >>> def right_inverse(self, Z): 2025-07-17T07:11:29.0181215Z >>> # Project Z onto the rank 1 matrices 2025-07-17T07:11:29.0181366Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-07-17T07:11:29.0181484Z >>> # Return rescaled singular vectors 2025-07-17T07:11:29.0181613Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-07-17T07:11:29.0181756Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-07-17T07:11:29.0181835Z >>> 2025-07-17T07:11:29.0181986Z >>> linear_rank_one = P.register_parametrization( 2025-07-17T07:11:29.0182106Z ... nn.Linear(4, 4), "weight", RankOne() 2025-07-17T07:11:29.0182183Z ... ) 2025-07-17T07:11:29.0182390Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-07-17T07:11:29.0182470Z 1 2025-07-17T07:11:29.0182475Z 2025-07-17T07:11:29.0182561Z 2025-07-17T07:11:29.0182812Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.0182853Z 2025-07-17T07:11:29.0267909Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=979. 2025-07-17T07:11:29.0268203Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.0268511Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-07-17T07:11:29.0268516Z 2025-07-17T07:11:29.0268756Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-07-17T07:11:29.0268970Z by removing the specified ``amount`` of (currently unpruned) channels 2025-07-17T07:11:29.0269153Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-07-17T07:11:29.0269347Z Modifies module in place (and also return the modified module) 2025-07-17T07:11:29.0269433Z by: 2025-07-17T07:11:29.0269476Z 2025-07-17T07:11:29.0269694Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T07:11:29.0269911Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T07:11:29.0270190Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T07:11:29.0270405Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T07:11:29.0270497Z ``name+'_orig'``. 2025-07-17T07:11:29.0270501Z 2025-07-17T07:11:29.0270596Z Args: 2025-07-17T07:11:29.0270772Z module (nn.Module): module containing the tensor to prune 2025-07-17T07:11:29.0270953Z name (str): parameter name within ``module`` on which pruning 2025-07-17T07:11:29.0271057Z will act. 2025-07-17T07:11:29.0271224Z amount (int or float): quantity of parameters to prune. 2025-07-17T07:11:29.0271396Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-07-17T07:11:29.0271608Z fraction of parameters to prune. If ``int``, it represents the 2025-07-17T07:11:29.0271736Z absolute number of parameters to prune. 2025-07-17T07:11:29.0272057Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-07-17T07:11:29.0272211Z entries for argument ``p`` in :func:`torch.norm`. 2025-07-17T07:11:29.0272405Z dim (int): index of the dim along which we define channels to prune. 2025-07-17T07:11:29.0272648Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-07-17T07:11:29.0272833Z shape as module parameter) used to compute mask for pruning. 2025-07-17T07:11:29.0273049Z The values in this tensor indicate the importance of the corresponding 2025-07-17T07:11:29.0273186Z elements in the parameter being pruned. 2025-07-17T07:11:29.0273406Z If unspecified or None, the module parameter will be used in its place. 2025-07-17T07:11:29.0273411Z 2025-07-17T07:11:29.0273512Z Returns: 2025-07-17T07:11:29.0273733Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-07-17T07:11:29.0273739Z 2025-07-17T07:11:29.0273825Z Examples: 2025-07-17T07:11:29.0273953Z >>> from torch.nn.utils import prune 2025-07-17T07:11:29.0274058Z >>> m = prune.ln_structured( 2025-07-17T07:11:29.0274237Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-07-17T07:11:29.0274328Z ... ) 2025-07-17T07:11:29.0274409Z 2025-07-17T07:11:29.0274671Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.0274676Z 2025-07-17T07:11:29.0275248Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1026. 2025-07-17T07:11:29.0275523Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.0275527Z 2025-07-17T07:11:29.0275959Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-07-17T07:11:29.0276020Z 2025-07-17T07:11:29.0276125Z Modifies modules in place by: 2025-07-17T07:11:29.0276142Z 2025-07-17T07:11:29.0276352Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T07:11:29.0276562Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T07:11:29.0276782Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T07:11:29.0276985Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T07:11:29.0277070Z ``name+'_orig'``. 2025-07-17T07:11:29.0277074Z 2025-07-17T07:11:29.0277163Z Args: 2025-07-17T07:11:29.0277361Z parameters (Iterable of (module, name) tuples): parameters of 2025-07-17T07:11:29.0277558Z the model to prune in a global fashion, i.e. by aggregating all 2025-07-17T07:11:29.0277762Z weights prior to deciding which ones to prune. module must be of 2025-07-17T07:11:29.0277944Z type :class:`nn.Module`, and name must be a string. 2025-07-17T07:11:29.0278183Z pruning_method (function): a valid pruning function from this module, 2025-07-17T07:11:29.0278354Z or a custom one implemented by the user that satisfies the 2025-07-17T07:11:29.0278645Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-07-17T07:11:29.0278888Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-07-17T07:11:29.0279107Z the corresponding parameter's importance scores tensor. The tensor 2025-07-17T07:11:29.0279323Z should be the same shape as the parameter, and is used for computing 2025-07-17T07:11:29.0279418Z mask for pruning. 2025-07-17T07:11:29.0279618Z If unspecified or None, the parameter will be used in place of its 2025-07-17T07:11:29.0279727Z importance scores. 2025-07-17T07:11:29.0279848Z kwargs: other keyword arguments such as: 2025-07-17T07:11:29.0280042Z amount (int or float): quantity of parameters to prune across the 2025-07-17T07:11:29.0280165Z specified parameters. 2025-07-17T07:11:29.0280335Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-07-17T07:11:29.0280536Z fraction of parameters to prune. If ``int``, it represents the 2025-07-17T07:11:29.0280677Z absolute number of parameters to prune. 2025-07-17T07:11:29.0280682Z 2025-07-17T07:11:29.0280761Z Raises: 2025-07-17T07:11:29.0280921Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-07-17T07:11:29.0280925Z 2025-07-17T07:11:29.0281007Z Note: 2025-07-17T07:11:29.0281218Z Since global structured pruning doesn't make much sense unless the 2025-07-17T07:11:29.0281427Z norm is normalized by the size of the parameter, we now limit the 2025-07-17T07:11:29.0281574Z scope of global pruning to unstructured methods. 2025-07-17T07:11:29.0281578Z 2025-07-17T07:11:29.0281676Z Examples: 2025-07-17T07:11:29.0281789Z >>> from torch.nn.utils import prune 2025-07-17T07:11:29.0281907Z >>> from collections import OrderedDict 2025-07-17T07:11:29.0282023Z >>> net = nn.Sequential( 2025-07-17T07:11:29.0282116Z ... OrderedDict( 2025-07-17T07:11:29.0282198Z ... [ 2025-07-17T07:11:29.0282327Z ... ("first", nn.Linear(10, 4)), 2025-07-17T07:11:29.0282441Z ... ("second", nn.Linear(4, 1)), 2025-07-17T07:11:29.0282519Z ... ] 2025-07-17T07:11:29.0282611Z ... ) 2025-07-17T07:11:29.0282694Z ... ) 2025-07-17T07:11:29.0282794Z >>> parameters_to_prune = ( 2025-07-17T07:11:29.0282907Z ... (net.first, "weight"), 2025-07-17T07:11:29.0283007Z ... (net.second, "weight"), 2025-07-17T07:11:29.0283084Z ... ) 2025-07-17T07:11:29.0283201Z >>> prune.global_unstructured( 2025-07-17T07:11:29.0283304Z ... parameters_to_prune, 2025-07-17T07:11:29.0283440Z ... pruning_method=prune.L1Unstructured, 2025-07-17T07:11:29.0283528Z ... amount=10, 2025-07-17T07:11:29.0283610Z ... ) 2025-07-17T07:11:29.0283869Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-07-17T07:11:29.0283955Z tensor(10) 2025-07-17T07:11:29.0283960Z 2025-07-17T07:11:29.0283964Z 2025-07-17T07:11:29.0284223Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.0284237Z 2025-07-17T07:11:29.0284765Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1149. 2025-07-17T07:11:29.0285025Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.0285430Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-07-17T07:11:29.0285435Z 2025-07-17T07:11:29.0285644Z Modifies module in place (and also return the modified module) by: 2025-07-17T07:11:29.0285648Z 2025-07-17T07:11:29.0285862Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T07:11:29.0286106Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T07:11:29.0286312Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T07:11:29.0286580Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T07:11:29.0286674Z ``name+'_orig'``. 2025-07-17T07:11:29.0286679Z 2025-07-17T07:11:29.0286768Z Args: 2025-07-17T07:11:29.0286943Z module (nn.Module): module containing the tensor to prune 2025-07-17T07:11:29.0287120Z name (str): parameter name within ``module`` on which pruning 2025-07-17T07:11:29.0287216Z will act. 2025-07-17T07:11:29.0287387Z mask (Tensor): binary mask to be applied to the parameter. 2025-07-17T07:11:29.0287391Z 2025-07-17T07:11:29.0287470Z Returns: 2025-07-17T07:11:29.0287695Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-07-17T07:11:29.0287702Z 2025-07-17T07:11:29.0287782Z Examples: 2025-07-17T07:11:29.0287910Z >>> from torch.nn.utils import prune 2025-07-17T07:11:29.0288016Z >>> m = prune.custom_from_mask( 2025-07-17T07:11:29.0288180Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-07-17T07:11:29.0288277Z ... ) 2025-07-17T07:11:29.0288378Z >>> print(m.bias_mask) 2025-07-17T07:11:29.0288467Z tensor([0., 1., 0.]) 2025-07-17T07:11:29.0288471Z 2025-07-17T07:11:29.0288559Z 2025-07-17T07:11:29.0288809Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.0288814Z 2025-07-17T07:11:29.0334538Z msg = Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py line=342. 2025-07-17T07:11:29.0334806Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.0334944Z Pad a packed batch of variable length sequences. 2025-07-17T07:11:29.0334965Z 2025-07-17T07:11:29.0335143Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-07-17T07:11:29.0335147Z 2025-07-17T07:11:29.0335477Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-07-17T07:11:29.0335728Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-07-17T07:11:29.0335840Z sequence and ``B`` is the batch size. 2025-07-17T07:11:29.0335844Z 2025-07-17T07:11:29.0335923Z Example: 2025-07-17T07:11:29.0336176Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-07-17T07:11:29.0336325Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-07-17T07:11:29.0336433Z >>> lens = [2, 1, 3] 2025-07-17T07:11:29.0336547Z >>> packed = pack_padded_sequence( 2025-07-17T07:11:29.0336700Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-07-17T07:11:29.0336795Z ... ) 2025-07-17T07:11:29.0336883Z >>> packed 2025-07-17T07:11:29.0337164Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-07-17T07:11:29.0337391Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-07-17T07:11:29.0337639Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-07-17T07:11:29.0337743Z >>> seq_unpacked 2025-07-17T07:11:29.0337833Z tensor([[1, 2, 0], 2025-07-17T07:11:29.0337919Z [3, 0, 0], 2025-07-17T07:11:29.0338015Z [4, 5, 6]]) 2025-07-17T07:11:29.0338104Z >>> lens_unpacked 2025-07-17T07:11:29.0338191Z tensor([2, 1, 3]) 2025-07-17T07:11:29.0338195Z 2025-07-17T07:11:29.0338294Z .. note:: 2025-07-17T07:11:29.0338437Z :attr:`total_length` is useful to implement the 2025-07-17T07:11:29.0338660Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-07-17T07:11:29.0338922Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-07-17T07:11:29.0339156Z See :ref:`this FAQ section ` for 2025-07-17T07:11:29.0339257Z details. 2025-07-17T07:11:29.0339262Z 2025-07-17T07:11:29.0339397Z Args: 2025-07-17T07:11:29.0339527Z sequence (PackedSequence): batch to pad 2025-07-17T07:11:29.0339776Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-07-17T07:11:29.0339888Z format, ``T x B x *`` otherwise. 2025-07-17T07:11:29.0340081Z padding_value (float, optional): values for padded elements. 2025-07-17T07:11:29.0340327Z total_length (int, optional): if not ``None``, the output will be padded to 2025-07-17T07:11:29.0340566Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-07-17T07:11:29.0340766Z if :attr:`total_length` is less than the max sequence length in 2025-07-17T07:11:29.0340866Z :attr:`sequence`. 2025-07-17T07:11:29.0340873Z 2025-07-17T07:11:29.0340959Z Returns: 2025-07-17T07:11:29.0341159Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-07-17T07:11:29.0341359Z containing the list of lengths of each sequence in the batch. 2025-07-17T07:11:29.0341582Z Batch elements will be re-ordered as they were ordered originally when 2025-07-17T07:11:29.0341808Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-07-17T07:11:29.0341889Z 2025-07-17T07:11:29.0342152Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.0342156Z 2025-07-17T07:11:29.0938913Z msg = Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=808. 2025-07-17T07:11:29.0939855Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.0940577Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-07-17T07:11:29.0941043Z 2025-07-17T07:11:29.0941431Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-07-17T07:11:29.0942155Z intervals by which each scheduler should be called at a given epoch. 2025-07-17T07:11:29.0942486Z 2025-07-17T07:11:29.0942572Z Args: 2025-07-17T07:11:29.0942836Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:29.0943232Z schedulers (list): List of chained schedulers. 2025-07-17T07:11:29.0943699Z milestones (list): List of integers that reflects milestone points. 2025-07-17T07:11:29.0944205Z last_epoch (int): The index of last epoch. Default: -1. 2025-07-17T07:11:29.0944496Z 2025-07-17T07:11:29.0944583Z Example: 2025-07-17T07:11:29.0944817Z >>> # xdoctest: +SKIP 2025-07-17T07:11:29.0945144Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-07-17T07:11:29.0945520Z >>> # lr = 0.005 if epoch == 0 2025-07-17T07:11:29.0945931Z >>> # lr = 0.005 if epoch == 1 2025-07-17T07:11:29.0946240Z >>> # lr = 0.005 if epoch == 2 2025-07-17T07:11:29.0946548Z >>> # ... 2025-07-17T07:11:29.0946801Z >>> # lr = 0.05 if epoch == 20 2025-07-17T07:11:29.0947111Z >>> # lr = 0.045 if epoch == 21 2025-07-17T07:11:29.0947434Z >>> # lr = 0.0405 if epoch == 22 2025-07-17T07:11:29.0947858Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-07-17T07:11:29.0948330Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-07-17T07:11:29.0948715Z >>> scheduler = SequentialLR( 2025-07-17T07:11:29.0949025Z ... optimizer, 2025-07-17T07:11:29.0949330Z ... schedulers=[scheduler1, scheduler2], 2025-07-17T07:11:29.0949667Z ... milestones=[20], 2025-07-17T07:11:29.0949941Z ... ) 2025-07-17T07:11:29.0950176Z >>> for epoch in range(100): 2025-07-17T07:11:29.0950504Z >>> train(...) 2025-07-17T07:11:29.0950775Z >>> validate(...) 2025-07-17T07:11:29.0951057Z >>> scheduler.step() 2025-07-17T07:11:29.0951245Z 2025-07-17T07:11:29.0951526Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-07-17T07:11:29.0951888Z 2025-07-17T07:11:29.0952260Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.0952627Z 2025-07-17T07:11:29.0973646Z msg = Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1233. 2025-07-17T07:11:29.0974590Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:29.0975131Z Reduce learning rate when a metric has stopped improving. 2025-07-17T07:11:29.0975484Z 2025-07-17T07:11:29.0975689Z Models often benefit from reducing the learning rate by a factor 2025-07-17T07:11:29.0976204Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-07-17T07:11:29.0976703Z quantity and if no improvement is seen for a 'patience' number 2025-07-17T07:11:29.0977142Z of epochs, the learning rate is reduced. 2025-07-17T07:11:29.0977379Z 2025-07-17T07:11:29.0977465Z Args: 2025-07-17T07:11:29.0977728Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:29.0978141Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-07-17T07:11:29.0978572Z be reduced when the quantity monitored has stopped 2025-07-17T07:11:29.0979008Z decreasing; in `max` mode it will be reduced when the 2025-07-17T07:11:29.0979485Z quantity monitored has stopped increasing. Default: 'min'. 2025-07-17T07:11:29.0979966Z factor (float): Factor by which the learning rate will be 2025-07-17T07:11:29.0980395Z reduced. new_lr = lr * factor. Default: 0.1. 2025-07-17T07:11:29.0980866Z patience (int): The number of allowed epochs with no improvement after 2025-07-17T07:11:29.0981328Z which the learning rate will be reduced. 2025-07-17T07:11:29.0981805Z For example, consider the case of having no patience (`patience = 0`). 2025-07-17T07:11:29.0982528Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-07-17T07:11:29.0983223Z In the second epoch, if the performance is worse than the baseline, 2025-07-17T07:11:29.0983700Z we have what is considered an intolerable epoch. 2025-07-17T07:11:29.0984233Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-07-17T07:11:29.0984792Z the learning rate is reduced at the end of this epoch. 2025-07-17T07:11:29.0985394Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-07-17T07:11:29.0986159Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-07-17T07:11:29.0986788Z the learning rate is not adjusted. 2025-07-17T07:11:29.0987119Z Default: 10. 2025-07-17T07:11:29.0987466Z threshold (float): Threshold for measuring the new optimum, 2025-07-17T07:11:29.0987947Z to only focus on significant changes. Default: 1e-4. 2025-07-17T07:11:29.0988402Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-07-17T07:11:29.0988845Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-07-17T07:11:29.0989269Z mode or best * ( 1 - threshold ) in `min` mode. 2025-07-17T07:11:29.0989688Z In `abs` mode, dynamic_threshold = best + threshold in 2025-07-17T07:11:29.0990147Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-07-17T07:11:29.0990610Z cooldown (int): Number of epochs to wait before resuming 2025-07-17T07:11:29.0991076Z normal operation after lr has been reduced. Default: 0. 2025-07-17T07:11:29.0991591Z min_lr (float or list): A scalar or a list of scalars. A 2025-07-17T07:11:29.0992025Z lower bound on the learning rate of all param groups 2025-07-17T07:11:29.0992436Z or each group respectively. Default: 0. 2025-07-17T07:11:29.0992934Z eps (float): Minimal decay applied to lr. If the difference 2025-07-17T07:11:29.0993413Z between new and old lr is smaller than eps, the update is 2025-07-17T07:11:29.0993808Z ignored. Default: 1e-8. 2025-07-17T07:11:29.0994021Z 2025-07-17T07:11:29.0994109Z Example: 2025-07-17T07:11:29.0994349Z >>> # xdoctest: +SKIP 2025-07-17T07:11:29.0994754Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-07-17T07:11:29.0995272Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-07-17T07:11:29.0995645Z >>> for epoch in range(10): 2025-07-17T07:11:29.0995930Z >>> train(...) 2025-07-17T07:11:29.0996205Z >>> val_loss = validate(...) 2025-07-17T07:11:29.0996574Z >>> # Note that step should be called after validate() 2025-07-17T07:11:29.0996951Z >>> scheduler.step(val_loss) 2025-07-17T07:11:29.0997165Z 2025-07-17T07:11:29.0997367Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-07-17T07:11:29.0997757Z 2025-07-17T07:11:29.0998287Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-07-17T07:11:29.0998818Z 2025-07-17T07:11:29.0998921Z scheduler.step(val_loss) 2025-07-17T07:11:29.0999192Z ^ 2025-07-17T07:11:29.0999853Z msg = Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1430. 2025-07-17T07:11:29.1000749Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.1001458Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-07-17T07:11:29.1001904Z 2025-07-17T07:11:29.1002200Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-07-17T07:11:29.1002857Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-07-17T07:11:29.1003479Z The distance between the two boundaries can be scaled on a per-iteration 2025-07-17T07:11:29.1003933Z or per-cycle basis. 2025-07-17T07:11:29.1004091Z 2025-07-17T07:11:29.1004350Z Cyclical learning rate policy changes the learning rate after every batch. 2025-07-17T07:11:29.1004904Z `step` should be called after a batch has been used for training. 2025-07-17T07:11:29.1005228Z 2025-07-17T07:11:29.1005434Z This class has three built-in policies, as put forth in the paper: 2025-07-17T07:11:29.1005756Z 2025-07-17T07:11:29.1005978Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-07-17T07:11:29.1006604Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-07-17T07:11:29.1007342Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-07-17T07:11:29.1007935Z at each cycle iteration. 2025-07-17T07:11:29.1008118Z 2025-07-17T07:11:29.1008372Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-07-17T07:11:29.1008728Z 2025-07-17T07:11:29.1008811Z Args: 2025-07-17T07:11:29.1009070Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:29.1009499Z base_lr (float or list): Initial learning rate which is the 2025-07-17T07:11:29.1009962Z lower boundary in the cycle for each parameter group. 2025-07-17T07:11:29.1010442Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-07-17T07:11:29.1010904Z for each parameter group. Functionally, 2025-07-17T07:11:29.1011306Z it defines the cycle amplitude (max_lr - base_lr). 2025-07-17T07:11:29.1011695Z The lr at any cycle is the sum of base_lr 2025-07-17T07:11:29.1012115Z and some scaling of the amplitude; therefore 2025-07-17T07:11:29.1012510Z max_lr may not actually be reached depending on 2025-07-17T07:11:29.1012861Z scaling function. 2025-07-17T07:11:29.1013374Z step_size_up (int): Number of training iterations in the 2025-07-17T07:11:29.1013802Z increasing half of a cycle. Default: 2000 2025-07-17T07:11:29.1014226Z step_size_down (int): Number of training iterations in the 2025-07-17T07:11:29.1014679Z decreasing half of a cycle. If step_size_down is None, 2025-07-17T07:11:29.1015101Z it is set to step_size_up. Default: None 2025-07-17T07:11:29.1015610Z mode (str): One of {triangular, triangular2, exp_range}. 2025-07-17T07:11:29.1016030Z Values correspond to policies detailed above. 2025-07-17T07:11:29.1016443Z If scale_fn is not None, this argument is ignored. 2025-07-17T07:11:29.1016820Z Default: 'triangular' 2025-07-17T07:11:29.1017194Z gamma (float): Constant in 'exp_range' scaling function: 2025-07-17T07:11:29.1017580Z gamma**(cycle iterations) 2025-07-17T07:11:29.1017889Z Default: 1.0 2025-07-17T07:11:29.1018256Z scale_fn (function): Custom scaling policy defined by a single 2025-07-17T07:11:29.1018677Z argument lambda function, where 2025-07-17T07:11:29.1019022Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-07-17T07:11:29.1019379Z If specified, then 'mode' is ignored. 2025-07-17T07:11:29.1019697Z Default: None 2025-07-17T07:11:29.1019997Z scale_mode (str): {'cycle', 'iterations'}. 2025-07-17T07:11:29.1020371Z Defines whether scale_fn is evaluated on 2025-07-17T07:11:29.1020754Z cycle number or cycle iterations (training 2025-07-17T07:11:29.1021109Z iterations since start of cycle). 2025-07-17T07:11:29.1021432Z Default: 'cycle' 2025-07-17T07:11:29.1021819Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-07-17T07:11:29.1022324Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-07-17T07:11:29.1022724Z Default: True 2025-07-17T07:11:29.1023117Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-07-17T07:11:29.1023654Z for each parameter group. Note that momentum is cycled inversely 2025-07-17T07:11:29.1024144Z to learning rate; at the peak of a cycle, momentum is 2025-07-17T07:11:29.1024570Z 'base_momentum' and learning rate is 'max_lr'. 2025-07-17T07:11:29.1024914Z Default: 0.8 2025-07-17T07:11:29.1025283Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-07-17T07:11:29.1025748Z for each parameter group. Functionally, 2025-07-17T07:11:29.1026190Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-07-17T07:11:29.1026674Z The momentum at any cycle is the difference of max_momentum 2025-07-17T07:11:29.1027151Z and some scaling of the amplitude; therefore 2025-07-17T07:11:29.1027580Z base_momentum may not actually be reached depending on 2025-07-17T07:11:29.1028055Z scaling function. Note that momentum is cycled inversely 2025-07-17T07:11:29.1028558Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-07-17T07:11:29.1029006Z and learning rate is 'base_lr' 2025-07-17T07:11:29.1029328Z Default: 0.9 2025-07-17T07:11:29.1029706Z last_epoch (int): The index of the last batch. This parameter is used when 2025-07-17T07:11:29.1030272Z resuming a training job. Since `step()` should be invoked after each 2025-07-17T07:11:29.1030834Z batch instead of after each epoch, this number represents the total 2025-07-17T07:11:29.1031384Z number of *batches* computed, not the total number of epochs computed. 2025-07-17T07:11:29.1031947Z When last_epoch=-1, the schedule is started from the beginning. 2025-07-17T07:11:29.1032348Z Default: -1 2025-07-17T07:11:29.1032503Z 2025-07-17T07:11:29.1032600Z Example: 2025-07-17T07:11:29.1032817Z >>> # xdoctest: +SKIP 2025-07-17T07:11:29.1033278Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-07-17T07:11:29.1033780Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-07-17T07:11:29.1034123Z ... optimizer, 2025-07-17T07:11:29.1034393Z ... base_lr=0.01, 2025-07-17T07:11:29.1034666Z ... max_lr=0.1, 2025-07-17T07:11:29.1034923Z ... step_size_up=10, 2025-07-17T07:11:29.1035196Z ... ) 2025-07-17T07:11:29.1035476Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-07-17T07:11:29.1035844Z >>> for epoch in range(10): 2025-07-17T07:11:29.1036143Z >>> for batch in data_loader: 2025-07-17T07:11:29.1036457Z >>> train_batch(...) 2025-07-17T07:11:29.1036765Z >>> scheduler.step() 2025-07-17T07:11:29.1036961Z 2025-07-17T07:11:29.1037121Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-07-17T07:11:29.1037398Z 2025-07-17T07:11:29.1037706Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-07-17T07:11:29.1038293Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-07-17T07:11:29.1038652Z 2025-07-17T07:11:29.1039001Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.1039382Z 2025-07-17T07:11:29.1040014Z msg = Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1713. 2025-07-17T07:11:29.1041023Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.1041650Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-07-17T07:11:29.1042032Z 2025-07-17T07:11:29.1042211Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-07-17T07:11:29.1042748Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-07-17T07:11:29.1043243Z of epochs between two warm restarts in SGDR: 2025-07-17T07:11:29.1043482Z 2025-07-17T07:11:29.1043565Z .. math:: 2025-07-17T07:11:29.1043870Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-07-17T07:11:29.1044305Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-07-17T07:11:29.1044548Z 2025-07-17T07:11:29.1044716Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-07-17T07:11:29.1045173Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-07-17T07:11:29.1045484Z 2025-07-17T07:11:29.1045582Z It has been proposed in 2025-07-17T07:11:29.1045931Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-07-17T07:11:29.1046214Z 2025-07-17T07:11:29.1046299Z Args: 2025-07-17T07:11:29.1046587Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:29.1046994Z T_0 (int): Number of iterations until the first restart. 2025-07-17T07:11:29.1047558Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-07-17T07:11:29.1048140Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-07-17T07:11:29.1048661Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-07-17T07:11:29.1048989Z 2025-07-17T07:11:29.1049170Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-07-17T07:11:29.1049563Z https://arxiv.org/abs/1608.03983 2025-07-17T07:11:29.1049797Z 2025-07-17T07:11:29.1049883Z Example: 2025-07-17T07:11:29.1050116Z >>> # xdoctest: +SKIP 2025-07-17T07:11:29.1050487Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-07-17T07:11:29.1051012Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-07-17T07:11:29.1051526Z ... optimizer, T_0=20 2025-07-17T07:11:29.1051811Z ... ) 2025-07-17T07:11:29.1052035Z >>> for epoch in range(100): 2025-07-17T07:11:29.1052335Z >>> train(...) 2025-07-17T07:11:29.1052650Z >>> validate(...) 2025-07-17T07:11:29.1052916Z >>> scheduler.step() 2025-07-17T07:11:29.1053116Z 2025-07-17T07:11:29.1053355Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-07-17T07:11:29.1053791Z 2025-07-17T07:11:29.1054172Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.1054542Z 2025-07-17T07:11:29.1055127Z msg = Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1863. 2025-07-17T07:11:29.1056110Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.1056806Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-07-17T07:11:29.1057234Z 2025-07-17T07:11:29.1057550Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-07-17T07:11:29.1058277Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-07-17T07:11:29.1058803Z lower than the initial learning rate. 2025-07-17T07:11:29.1059263Z This policy was initially described in the paper `Super-Convergence: 2025-07-17T07:11:29.1059826Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-07-17T07:11:29.1060157Z 2025-07-17T07:11:29.1060407Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-07-17T07:11:29.1060983Z `step` should be called after a batch has been used for training. 2025-07-17T07:11:29.1061308Z 2025-07-17T07:11:29.1061421Z This scheduler is not chainable. 2025-07-17T07:11:29.1061628Z 2025-07-17T07:11:29.1061883Z Note also that the total number of steps in the cycle can be determined in one 2025-07-17T07:11:29.1062378Z of two ways (listed in order of precedence): 2025-07-17T07:11:29.1062633Z 2025-07-17T07:11:29.1062782Z #. A value for total_steps is explicitly provided. 2025-07-17T07:11:29.1063239Z #. A number of epochs (epochs) and a number of steps per epoch 2025-07-17T07:11:29.1063656Z (steps_per_epoch) are provided. 2025-07-17T07:11:29.1064026Z In this case, the number of total steps is inferred by 2025-07-17T07:11:29.1064430Z total_steps = epochs * steps_per_epoch 2025-07-17T07:11:29.1064664Z 2025-07-17T07:11:29.1064915Z You must either provide a value for total_steps or provide a value for both 2025-07-17T07:11:29.1065369Z epochs and steps_per_epoch. 2025-07-17T07:11:29.1065573Z 2025-07-17T07:11:29.1065874Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-07-17T07:11:29.1066597Z claims that "unpublished work has shown even better results by using only two phases". To 2025-07-17T07:11:29.1067337Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-07-17T07:11:29.1067702Z 2025-07-17T07:11:29.1067784Z Args: 2025-07-17T07:11:29.1068049Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:29.1068500Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-07-17T07:11:29.1068922Z for each parameter group. 2025-07-17T07:11:29.1069345Z total_steps (int): The total number of steps in the cycle. Note that 2025-07-17T07:11:29.1069887Z if a value is not provided here, then it must be inferred by providing 2025-07-17T07:11:29.1070352Z a value for epochs and steps_per_epoch. 2025-07-17T07:11:29.1070681Z Default: None 2025-07-17T07:11:29.1071054Z epochs (int): The number of epochs to train for. This is used along 2025-07-17T07:11:29.1071625Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-07-17T07:11:29.1072419Z if a value for total_steps is not provided. 2025-07-17T07:11:29.1072768Z Default: None 2025-07-17T07:11:29.1073178Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-07-17T07:11:29.1073865Z used along with epochs in order to infer the total number of steps in the 2025-07-17T07:11:29.1074364Z cycle if a value for total_steps is not provided. 2025-07-17T07:11:29.1074726Z Default: None 2025-07-17T07:11:29.1075128Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-07-17T07:11:29.1075588Z increasing the learning rate. 2025-07-17T07:11:29.1075909Z Default: 0.3 2025-07-17T07:11:29.1076201Z anneal_strategy (str): {'cos', 'linear'} 2025-07-17T07:11:29.1076679Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-07-17T07:11:29.1077156Z linear annealing. 2025-07-17T07:11:29.1077441Z Default: 'cos' 2025-07-17T07:11:29.1077817Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-07-17T07:11:29.1078312Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-07-17T07:11:29.1078715Z Default: True 2025-07-17T07:11:29.1079107Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-07-17T07:11:29.1079642Z for each parameter group. Note that momentum is cycled inversely 2025-07-17T07:11:29.1080137Z to learning rate; at the peak of a cycle, momentum is 2025-07-17T07:11:29.1080561Z 'base_momentum' and learning rate is 'max_lr'. 2025-07-17T07:11:29.1080912Z Default: 0.85 2025-07-17T07:11:29.1081283Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-07-17T07:11:29.1081750Z for each parameter group. Functionally, 2025-07-17T07:11:29.1082197Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-07-17T07:11:29.1082629Z Note that momentum is cycled inversely 2025-07-17T07:11:29.1083080Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-07-17T07:11:29.1083628Z and learning rate is 'base_lr' 2025-07-17T07:11:29.1083935Z Default: 0.95 2025-07-17T07:11:29.1084291Z div_factor (float): Determines the initial learning rate via 2025-07-17T07:11:29.1084708Z initial_lr = max_lr/div_factor 2025-07-17T07:11:29.1085023Z Default: 25 2025-07-17T07:11:29.1085378Z final_div_factor (float): Determines the minimum learning rate via 2025-07-17T07:11:29.1085822Z min_lr = initial_lr/final_div_factor 2025-07-17T07:11:29.1086150Z Default: 1e4 2025-07-17T07:11:29.1086554Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-07-17T07:11:29.1087182Z learning rate according to 'final_div_factor' instead of modifying the second 2025-07-17T07:11:29.1087868Z phase (the first two phases will be symmetrical about the step indicated by 2025-07-17T07:11:29.1088320Z 'pct_start'). 2025-07-17T07:11:29.1088718Z last_epoch (int): The index of the last batch. This parameter is used when 2025-07-17T07:11:29.1089285Z resuming a training job. Since `step()` should be invoked after each 2025-07-17T07:11:29.1089847Z batch instead of after each epoch, this number represents the total 2025-07-17T07:11:29.1090399Z number of *batches* computed, not the total number of epochs computed. 2025-07-17T07:11:29.1090939Z When last_epoch=-1, the schedule is started from the beginning. 2025-07-17T07:11:29.1091349Z Default: -1 2025-07-17T07:11:29.1091506Z 2025-07-17T07:11:29.1091602Z Example: 2025-07-17T07:11:29.1091820Z >>> # xdoctest: +SKIP 2025-07-17T07:11:29.1092157Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-07-17T07:11:29.1092702Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-07-17T07:11:29.1093209Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-07-17T07:11:29.1093760Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-07-17T07:11:29.1094184Z ... ) 2025-07-17T07:11:29.1094410Z >>> for epoch in range(10): 2025-07-17T07:11:29.1094722Z >>> for batch in data_loader: 2025-07-17T07:11:29.1095037Z >>> train_batch(...) 2025-07-17T07:11:29.1095414Z >>> optimizer.step() 2025-07-17T07:11:29.1095722Z >>> scheduler.step() 2025-07-17T07:11:29.1095932Z 2025-07-17T07:11:29.1096103Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-07-17T07:11:29.1096378Z 2025-07-17T07:11:29.1096686Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-07-17T07:11:29.1097218Z https://arxiv.org/abs/1708.07120 2025-07-17T07:11:29.1097531Z 2025-07-17T07:11:29.1097906Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.1098277Z 2025-07-17T07:11:29.1134991Z msg = Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/optimizer.py line=867. 2025-07-17T07:11:29.1136046Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.1136546Z Load the optimizer state. 2025-07-17T07:11:29.1136740Z 2025-07-17T07:11:29.1136825Z Args: 2025-07-17T07:11:29.1137162Z state_dict (dict): optimizer state. Should be an object returned 2025-07-17T07:11:29.1137598Z from a call to :meth:`state_dict`. 2025-07-17T07:11:29.1137844Z 2025-07-17T07:11:29.1137938Z .. warning:: 2025-07-17T07:11:29.1138435Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-07-17T07:11:29.1139108Z as calling it beforehand will overwrite the loaded learning rates. 2025-07-17T07:11:29.1139454Z 2025-07-17T07:11:29.1139542Z .. note:: 2025-07-17T07:11:29.1139979Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-07-17T07:11:29.1140575Z in :meth:`state_dict`) will not affect the loading process. 2025-07-17T07:11:29.1141193Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-07-17T07:11:29.1141804Z differ from those initialized in the optimizer), 2025-07-17T07:11:29.1142388Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-07-17T07:11:29.1142912Z accordingly. 2025-07-17T07:11:29.1143361Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-07-17T07:11:29.1144099Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-07-17T07:11:29.1144759Z the optimizer ``param_names`` will remain unchanged. 2025-07-17T07:11:29.1145032Z 2025-07-17T07:11:29.1145117Z Example: 2025-07-17T07:11:29.1145365Z >>> # xdoctest: +SKIP 2025-07-17T07:11:29.1145681Z >>> model = torch.nn.Linear(10, 10) 2025-07-17T07:11:29.1146089Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-07-17T07:11:29.1146528Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-07-17T07:11:29.1146899Z ... optim, 2025-07-17T07:11:29.1147170Z ... start_factor=0.1, 2025-07-17T07:11:29.1147459Z ... end_factor=1, 2025-07-17T07:11:29.1147746Z ... total_iters=20, 2025-07-17T07:11:29.1148033Z ... ) 2025-07-17T07:11:29.1148353Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-07-17T07:11:29.1148754Z ... optim, 2025-07-17T07:11:29.1149046Z ... T_max=80, 2025-07-17T07:11:29.1149311Z ... eta_min=3e-5, 2025-07-17T07:11:29.1149588Z ... ) 2025-07-17T07:11:29.1149874Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-07-17T07:11:29.1163811Z ... optim, 2025-07-17T07:11:29.1164233Z ... schedulers=[scheduler1, scheduler2], 2025-07-17T07:11:29.1164598Z ... milestones=[20], 2025-07-17T07:11:29.1164896Z ... ) 2025-07-17T07:11:29.1165180Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-07-17T07:11:29.1165656Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-07-17T07:11:29.1166158Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-07-17T07:11:29.1166441Z 2025-07-17T07:11:29.1166528Z 2025-07-17T07:11:29.1166903Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.1167287Z 2025-07-17T07:11:29.1296105Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=120. 2025-07-17T07:11:29.1297029Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.1297773Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-07-17T07:11:29.1298271Z 2025-07-17T07:11:29.1298516Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-07-17T07:11:29.1299100Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-07-17T07:11:29.1299657Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-07-17T07:11:29.1300074Z (UAI 2018). 2025-07-17T07:11:29.1300222Z 2025-07-17T07:11:29.1300442Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-07-17T07:11:29.1301021Z but using exponential weights instead of equal weights across iterations. 2025-07-17T07:11:29.1301384Z 2025-07-17T07:11:29.1301632Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-07-17T07:11:29.1302206Z on the device :attr:`device` and allows to compute running averages of the 2025-07-17T07:11:29.1302666Z parameters of the :attr:`model`. 2025-07-17T07:11:29.1302873Z 2025-07-17T07:11:29.1302966Z Args: 2025-07-17T07:11:29.1303233Z model (torch.nn.Module): model to use with SWA/EMA 2025-07-17T07:11:29.1303744Z device (torch.device, optional): if provided, the averaged model will be 2025-07-17T07:11:29.1304211Z stored on the :attr:`device` 2025-07-17T07:11:29.1304636Z avg_fn (function, optional): the averaging function used to update 2025-07-17T07:11:29.1305153Z parameters; the function must take in the current value of the 2025-07-17T07:11:29.1305702Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-07-17T07:11:29.1306245Z parameter, and the number of models already averaged; if None, 2025-07-17T07:11:29.1306812Z an equally weighted average is used (default: None) 2025-07-17T07:11:29.1307322Z multi_avg_fn (function, optional): the averaging function used to update 2025-07-17T07:11:29.1307917Z parameters inplace; the function must take in the current values of the 2025-07-17T07:11:29.1308549Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-07-17T07:11:29.1309166Z parameters as a list, and the number of models already averaged; if None, 2025-07-17T07:11:29.1309711Z an equally weighted average is used (default: None) 2025-07-17T07:11:29.1310194Z use_buffers (bool): if ``True``, it will compute running averages for 2025-07-17T07:11:29.1310743Z both the parameters and the buffers of the model. (default: ``False``) 2025-07-17T07:11:29.1311086Z 2025-07-17T07:11:29.1311174Z Example: 2025-07-17T07:11:29.1311438Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:29.1311859Z >>> loader, optimizer, model, loss_fn = ... 2025-07-17T07:11:29.1312275Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-07-17T07:11:29.1312846Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-07-17T07:11:29.1313310Z >>> T_max=300) 2025-07-17T07:11:29.1313633Z >>> swa_start = 160 2025-07-17T07:11:29.1313942Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-07-17T07:11:29.1314305Z >>> for i in range(300): 2025-07-17T07:11:29.1314611Z >>> for input, target in loader: 2025-07-17T07:11:29.1314937Z >>> optimizer.zero_grad() 2025-07-17T07:11:29.1315295Z >>> loss_fn(model(input), target).backward() 2025-07-17T07:11:29.1315648Z >>> optimizer.step() 2025-07-17T07:11:29.1315948Z >>> if i > swa_start: 2025-07-17T07:11:29.1316262Z >>> swa_model.update_parameters(model) 2025-07-17T07:11:29.1316618Z >>> swa_scheduler.step() 2025-07-17T07:11:29.1316924Z >>> else: 2025-07-17T07:11:29.1317167Z >>> scheduler.step() 2025-07-17T07:11:29.1317456Z >>> 2025-07-17T07:11:29.1317740Z >>> # Update bn statistics for the swa_model at the end 2025-07-17T07:11:29.1318162Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-07-17T07:11:29.1318449Z 2025-07-17T07:11:29.1318747Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-07-17T07:11:29.1319369Z If no averaging function is provided, the default is to compute 2025-07-17T07:11:29.1319832Z equally-weighted average of the weights (SWA). 2025-07-17T07:11:29.1320086Z 2025-07-17T07:11:29.1320173Z Example: 2025-07-17T07:11:29.1320429Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:29.1320885Z >>> # Compute exponential moving averages of the weights and buffers 2025-07-17T07:11:29.1321382Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-07-17T07:11:29.1321883Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-07-17T07:11:29.1322235Z 2025-07-17T07:11:29.1322332Z .. note:: 2025-07-17T07:11:29.1322687Z When using SWA/EMA with models containing Batch Normalization you may 2025-07-17T07:11:29.1323223Z need to update the activation statistics for Batch Normalization. 2025-07-17T07:11:29.1323779Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-07-17T07:11:29.1324363Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-07-17T07:11:29.1324951Z statistics in a post-training step by passing data through the model. The 2025-07-17T07:11:29.1325536Z second does it during the parameter update phase by averaging all buffers. 2025-07-17T07:11:29.1326141Z Empirical evidence has shown that updating the statistics in normalization 2025-07-17T07:11:29.1326768Z layers increases accuracy, but you may wish to empirically test which 2025-07-17T07:11:29.1327266Z approach yields the best results in your problem. 2025-07-17T07:11:29.1327548Z 2025-07-17T07:11:29.1327635Z .. note:: 2025-07-17T07:11:29.1328027Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-07-17T07:11:29.1328400Z 2025-07-17T07:11:29.1328499Z .. note:: 2025-07-17T07:11:29.1328820Z When :meth:`update_parameters` is called for the first time (i.e. 2025-07-17T07:11:29.1329327Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-07-17T07:11:29.1329838Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-07-17T07:11:29.1330338Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-07-17T07:11:29.1330755Z to update the parameters. 2025-07-17T07:11:29.1330993Z 2025-07-17T07:11:29.1331219Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-07-17T07:11:29.1331690Z https://arxiv.org/abs/1803.05407 2025-07-17T07:11:29.1332196Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-07-17T07:11:29.1332639Z Average: 2025-07-17T07:11:29.1332893Z https://arxiv.org/abs/1806.05594 2025-07-17T07:11:29.1333310Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-07-17T07:11:29.1333748Z https://arxiv.org/abs/1904.11943 2025-07-17T07:11:29.1334195Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-07-17T07:11:29.1334643Z Generalizes Well: 2025-07-17T07:11:29.1334916Z https://arxiv.org/abs/2001.02312 2025-07-17T07:11:29.1335323Z .. _Polyak averaging: 2025-07-17T07:11:29.1335665Z https://paperswithcode.com/method/polyak-averaging 2025-07-17T07:11:29.1336015Z 2025-07-17T07:11:29.1336382Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.1336748Z 2025-07-17T07:11:29.1337241Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=375. 2025-07-17T07:11:29.1338081Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.1338671Z Anneals the learning rate in each parameter group to a fixed value. 2025-07-17T07:11:29.1339007Z 2025-07-17T07:11:29.1339242Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-07-17T07:11:29.1339811Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-07-17T07:11:29.1340141Z 2025-07-17T07:11:29.1340224Z Args: 2025-07-17T07:11:29.1340521Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-07-17T07:11:29.1341017Z swa_lrs (float or list): the learning rate value for all param groups 2025-07-17T07:11:29.1341473Z together or separately for each group. 2025-07-17T07:11:29.1341911Z annealing_epochs (int): number of epochs in the annealing phase 2025-07-17T07:11:29.1342328Z (default: 10) 2025-07-17T07:11:29.1342706Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-07-17T07:11:29.1343240Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-07-17T07:11:29.1343666Z (default: "cos") 2025-07-17T07:11:29.1344024Z last_epoch (int): the index of the last epoch (default: -1) 2025-07-17T07:11:29.1344314Z 2025-07-17T07:11:29.1344491Z The :class:`SWALR` scheduler can be used together with other 2025-07-17T07:11:29.1345008Z schedulers to switch to a constant learning rate late in the training 2025-07-17T07:11:29.1345444Z as in the example below. 2025-07-17T07:11:29.1345623Z 2025-07-17T07:11:29.1345719Z Example: 2025-07-17T07:11:29.1345968Z >>> # xdoctest: +SKIP("Undefined variables") 2025-07-17T07:11:29.1346324Z >>> loader, optimizer, model = ... 2025-07-17T07:11:29.1346701Z >>> lr_lambda = lambda epoch: 0.9 2025-07-17T07:11:29.1347132Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-07-17T07:11:29.1347580Z >>> lr_lambda=lr_lambda) 2025-07-17T07:11:29.1347973Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-07-17T07:11:29.1348442Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-07-17T07:11:29.1348829Z >>> swa_start = 160 2025-07-17T07:11:29.1349108Z >>> for i in range(300): 2025-07-17T07:11:29.1349411Z >>> for input, target in loader: 2025-07-17T07:11:29.1349732Z >>> optimizer.zero_grad() 2025-07-17T07:11:29.1350087Z >>> loss_fn(model(input), target).backward() 2025-07-17T07:11:29.1350443Z >>> optimizer.step() 2025-07-17T07:11:29.1350736Z >>> if i > swa_start: 2025-07-17T07:11:29.1351043Z >>> swa_scheduler.step() 2025-07-17T07:11:29.1351381Z >>> else: 2025-07-17T07:11:29.1351626Z >>> scheduler.step() 2025-07-17T07:11:29.1351840Z 2025-07-17T07:11:29.1352064Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-07-17T07:11:29.1352577Z https://arxiv.org/abs/1803.05407 2025-07-17T07:11:29.1352885Z 2025-07-17T07:11:29.1353238Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.1353619Z 2025-07-17T07:11:29.5857162Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1331. 2025-07-17T07:11:29.5858363Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:29.5858892Z Asserts that ``actual`` and ``expected`` are close. 2025-07-17T07:11:29.5859168Z 2025-07-17T07:11:29.5859542Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-07-17T07:11:29.5860048Z 2025-07-17T07:11:29.5860163Z .. math:: 2025-07-17T07:11:29.5860292Z 2025-07-17T07:11:29.5860685Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-07-17T07:11:29.5861172Z 2025-07-17T07:11:29.5861517Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-07-17T07:11:29.5862193Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-07-17T07:11:29.5862512Z 2025-07-17T07:11:29.5862724Z In addition, they are only considered close if they have the same 2025-07-17T07:11:29.5863043Z 2025-07-17T07:11:29.5863250Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-07-17T07:11:29.5863679Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-07-17T07:11:29.5864068Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-07-17T07:11:29.5864449Z - stride (if ``check_stride`` is ``True``). 2025-07-17T07:11:29.5864687Z 2025-07-17T07:11:29.5864990Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-07-17T07:11:29.5865422Z 2025-07-17T07:11:29.5865850Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-07-17T07:11:29.5866719Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-07-17T07:11:29.5867437Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-07-17T07:11:29.5868180Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-07-17T07:11:29.5868690Z 2025-07-17T07:11:29.5868983Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-07-17T07:11:29.5869737Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-07-17T07:11:29.5870477Z definition above. 2025-07-17T07:11:29.5870650Z 2025-07-17T07:11:29.5870959Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-07-17T07:11:29.5871936Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-07-17T07:11:29.5872791Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-07-17T07:11:29.5873664Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-07-17T07:11:29.5874400Z their elements are considered close according to the above definition. 2025-07-17T07:11:29.5874751Z 2025-07-17T07:11:29.5874857Z .. note:: 2025-07-17T07:11:29.5874985Z 2025-07-17T07:11:29.5875320Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-07-17T07:11:29.5876177Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-07-17T07:11:29.5877107Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-07-17T07:11:29.5877509Z 2025-07-17T07:11:29.5877605Z Args: 2025-07-17T07:11:29.5877822Z actual (Any): Actual input. 2025-07-17T07:11:29.5878146Z expected (Any): Expected input. 2025-07-17T07:11:29.5878736Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-07-17T07:11:29.5879369Z are allowed. Otherwise type equality is required. 2025-07-17T07:11:29.5880023Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-07-17T07:11:29.5880778Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-07-17T07:11:29.5881543Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-07-17T07:11:29.5882290Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-07-17T07:11:29.5882933Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-07-17T07:11:29.5883602Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-07-17T07:11:29.5884265Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-07-17T07:11:29.5884866Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-07-17T07:11:29.5885580Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-07-17T07:11:29.5886410Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-07-17T07:11:29.5887054Z :func:`torch.promote_types`) before being compared. 2025-07-17T07:11:29.5887685Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-07-17T07:11:29.5888512Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-07-17T07:11:29.5889064Z compared. 2025-07-17T07:11:29.5889577Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-07-17T07:11:29.5890425Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-07-17T07:11:29.5891264Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-07-17T07:11:29.5891869Z should return the new message. 2025-07-17T07:11:29.5892089Z 2025-07-17T07:11:29.5892177Z Raises: 2025-07-17T07:11:29.5892580Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-07-17T07:11:29.5893108Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-07-17T07:11:29.5893735Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-07-17T07:11:29.5894550Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-07-17T07:11:29.5895149Z different types. 2025-07-17T07:11:29.5895780Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-07-17T07:11:29.5896636Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-07-17T07:11:29.5897454Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-07-17T07:11:29.5898206Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-07-17T07:11:29.5898785Z :attr:`~torch.Tensor.layout`. 2025-07-17T07:11:29.5899223Z AssertionError: If only one of corresponding tensors is quantized. 2025-07-17T07:11:29.5900018Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-07-17T07:11:29.5900836Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-07-17T07:11:29.5901376Z :attr:`~torch.Tensor.device`. 2025-07-17T07:11:29.5901939Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-07-17T07:11:29.5902765Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-07-17T07:11:29.5903627Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-07-17T07:11:29.5904117Z 2025-07-17T07:11:29.5904484Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-07-17T07:11:29.5905127Z ``dtype``'s, the maximum of both tolerances is used. 2025-07-17T07:11:29.5905391Z 2025-07-17T07:11:29.5905531Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5905893Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-07-17T07:11:29.5906245Z +===========================+============+==========+ 2025-07-17T07:11:29.5906615Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-07-17T07:11:29.5906999Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5907366Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-07-17T07:11:29.5907745Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5908117Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:29.5908476Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5908849Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-07-17T07:11:29.5909219Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5909600Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-07-17T07:11:29.5909964Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5910344Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:29.5910720Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5911083Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-07-17T07:11:29.5911463Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5911836Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:29.5912196Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5912574Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:29.5912947Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5913358Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:29.5913719Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5914095Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:29.5914469Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5914831Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:29.5915206Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5915569Z | other | ``0.0`` | ``0.0`` | 2025-07-17T07:11:29.5915926Z +---------------------------+------------+----------+ 2025-07-17T07:11:29.5916160Z 2025-07-17T07:11:29.5916256Z .. note:: 2025-07-17T07:11:29.5916396Z 2025-07-17T07:11:29.5916787Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-07-17T07:11:29.5917687Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-07-17T07:11:29.5918416Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-07-17T07:11:29.5918864Z 2025-07-17T07:11:29.5918966Z >>> import functools 2025-07-17T07:11:29.5919409Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-07-17T07:11:29.5919906Z >>> assert_equal(1e-9, 1e-10) 2025-07-17T07:11:29.5920228Z Traceback (most recent call last): 2025-07-17T07:11:29.5920537Z ... 2025-07-17T07:11:29.5920785Z AssertionError: Scalars are not equal! 2025-07-17T07:11:29.5921101Z 2025-07-17T07:11:29.5921355Z Expected 1e-10 but got 1e-09. 2025-07-17T07:11:29.5921691Z Absolute difference: 9.000000000000001e-10 2025-07-17T07:11:29.5922027Z Relative difference: 9.0 2025-07-17T07:11:29.5922234Z 2025-07-17T07:11:29.5922321Z Examples: 2025-07-17T07:11:29.5922565Z >>> # tensor to tensor comparison 2025-07-17T07:11:29.5922919Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-07-17T07:11:29.5923289Z >>> actual = torch.acos(torch.cos(expected)) 2025-07-17T07:11:29.5923690Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:29.5923947Z 2025-07-17T07:11:29.5924068Z >>> # scalar to scalar comparison 2025-07-17T07:11:29.5924371Z >>> import math 2025-07-17T07:11:29.5924645Z >>> expected = math.sqrt(2.0) 2025-07-17T07:11:29.5924967Z >>> actual = 2.0 / math.sqrt(2.0) 2025-07-17T07:11:29.5925317Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:29.5925587Z 2025-07-17T07:11:29.5925709Z >>> # numpy array to numpy array comparison 2025-07-17T07:11:29.5926053Z >>> import numpy as np 2025-07-17T07:11:29.5926361Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-07-17T07:11:29.5926711Z >>> actual = np.arccos(np.cos(expected)) 2025-07-17T07:11:29.5927093Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:29.5927345Z 2025-07-17T07:11:29.5927474Z >>> # sequence to sequence comparison 2025-07-17T07:11:29.5927796Z >>> import numpy as np 2025-07-17T07:11:29.5928240Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-07-17T07:11:29.5928753Z >>> # length and their elements have to match. 2025-07-17T07:11:29.5929154Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-07-17T07:11:29.5929522Z >>> actual = tuple(expected) 2025-07-17T07:11:29.5929879Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:29.5930131Z 2025-07-17T07:11:29.5930261Z >>> # mapping to mapping comparison 2025-07-17T07:11:29.5930603Z >>> from collections import OrderedDict 2025-07-17T07:11:29.5930942Z >>> import numpy as np 2025-07-17T07:11:29.5931233Z >>> foo = torch.tensor(1.0) 2025-07-17T07:11:29.5931560Z >>> bar = 2.0 2025-07-17T07:11:29.5931815Z >>> baz = np.array(3.0) 2025-07-17T07:11:29.5932260Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-07-17T07:11:29.5932856Z >>> # have to have the same set of keys and their elements have to match. 2025-07-17T07:11:29.5933378Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-07-17T07:11:29.5933843Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-07-17T07:11:29.5934243Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:29.5934497Z 2025-07-17T07:11:29.5934621Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-07-17T07:11:29.5934973Z >>> actual = expected.clone() 2025-07-17T07:11:29.5935452Z >>> # By default, directly related instances can be compared 2025-07-17T07:11:29.5935963Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-07-17T07:11:29.5936529Z >>> # This check can be made more strict with allow_subclasses=False 2025-07-17T07:11:29.5936961Z >>> torch.testing.assert_close( 2025-07-17T07:11:29.5937458Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-07-17T07:11:29.5937855Z ... ) 2025-07-17T07:11:29.5938103Z Traceback (most recent call last): 2025-07-17T07:11:29.5938417Z ... 2025-07-17T07:11:29.5938735Z TypeError: No comparison pair was able to handle inputs of type 2025-07-17T07:11:29.5939289Z and . 2025-07-17T07:11:29.5939854Z >>> # If the inputs are not directly related, they are never considered close 2025-07-17T07:11:29.5940379Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-07-17T07:11:29.5940772Z Traceback (most recent call last): 2025-07-17T07:11:29.5941084Z ... 2025-07-17T07:11:29.5941500Z TypeError: No comparison pair was able to handle inputs of type 2025-07-17T07:11:29.5942013Z and . 2025-07-17T07:11:29.5942487Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-07-17T07:11:29.5943003Z >>> # their type if check_dtype=False. 2025-07-17T07:11:29.5943410Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-07-17T07:11:29.5943695Z 2025-07-17T07:11:29.5943793Z >>> # NaN != NaN by default. 2025-07-17T07:11:29.5944125Z >>> expected = torch.tensor(float("Nan")) 2025-07-17T07:11:29.5944484Z >>> actual = expected.clone() 2025-07-17T07:11:29.5944835Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:29.5945218Z Traceback (most recent call last): 2025-07-17T07:11:29.5945530Z ... 2025-07-17T07:11:29.5945764Z AssertionError: Scalars are not close! 2025-07-17T07:11:29.5946089Z 2025-07-17T07:11:29.5946345Z Expected nan but got nan. 2025-07-17T07:11:29.5946690Z Absolute difference: nan (up to 1e-05 allowed) 2025-07-17T07:11:29.5947101Z Relative difference: nan (up to 1.3e-06 allowed) 2025-07-17T07:11:29.5947567Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-07-17T07:11:29.5947887Z 2025-07-17T07:11:29.5948027Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-07-17T07:11:29.5948378Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-07-17T07:11:29.5948763Z >>> # The default error message can be overwritten. 2025-07-17T07:11:29.5949316Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2025-07-17T07:11:29.5949856Z Traceback (most recent call last): 2025-07-17T07:11:29.5950161Z ... 2025-07-17T07:11:29.5950448Z AssertionError: Argh, the tensors are not close! 2025-07-17T07:11:29.5950943Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-07-17T07:11:29.5951423Z >>> # extra information 2025-07-17T07:11:29.5951728Z >>> torch.testing.assert_close( 2025-07-17T07:11:29.5952151Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-07-17T07:11:29.5952553Z ... ) 2025-07-17T07:11:29.5952808Z Traceback (most recent call last): 2025-07-17T07:11:29.5953125Z ... 2025-07-17T07:11:29.5953346Z AssertionError: Header 2025-07-17T07:11:29.5953628Z 2025-07-17T07:11:29.5953882Z Tensor-likes are not close! 2025-07-17T07:11:29.5954167Z 2025-07-17T07:11:29.5954427Z Mismatched elements: 2 / 3 (66.7%) 2025-07-17T07:11:29.5954879Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-07-17T07:11:29.5955459Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-07-17T07:11:29.5955888Z 2025-07-17T07:11:29.5956119Z Footer 2025-07-17T07:11:29.5956366Z 2025-07-17T07:11:29.5956723Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:29.5957105Z 2025-07-17T07:11:30.7976821Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=134. 2025-07-17T07:11:30.7977804Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.7978327Z Register a container-like type as pytree node. 2025-07-17T07:11:30.7978594Z 2025-07-17T07:11:30.7978680Z Args: 2025-07-17T07:11:30.7978999Z cls (type): A Python type to treat as an internal pytree node. 2025-07-17T07:11:30.7979594Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-07-17T07:11:30.7980236Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-07-17T07:11:30.7980900Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-07-17T07:11:30.7981447Z passed to the ``unflatten_fn``. 2025-07-17T07:11:30.7981945Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-07-17T07:11:30.7982618Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-07-17T07:11:30.7983163Z The function should return an instance of ``cls``. 2025-07-17T07:11:30.7983710Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-07-17T07:11:30.7984259Z qualified name used when serializing the tree spec. 2025-07-17T07:11:30.7984857Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-07-17T07:11:30.7985574Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-07-17T07:11:30.7986250Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-07-17T07:11:30.7986946Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-07-17T07:11:30.7987651Z how to convert the custom json dumpable representation of the context back to the 2025-07-17T07:11:30.7988302Z original context. This is used for json deserialization, which is being used in 2025-07-17T07:11:30.7988807Z :mod:`torch.export` right now. 2025-07-17T07:11:30.7989040Z 2025-07-17T07:11:30.7989147Z Example:: 2025-07-17T07:11:30.7989291Z 2025-07-17T07:11:30.7989390Z >>> # xdoctest: +SKIP 2025-07-17T07:11:30.7989718Z >>> # Registry a Python type with lambda functions 2025-07-17T07:11:30.7990070Z >>> register_pytree_node( 2025-07-17T07:11:30.7990357Z ... set, 2025-07-17T07:11:30.7990633Z ... lambda s: (sorted(s), None, None), 2025-07-17T07:11:30.7990984Z ... lambda children, _: set(children), 2025-07-17T07:11:30.7991315Z ... ) 2025-07-17T07:11:30.7991592Z 2025-07-17T07:11:30.7991952Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.7992335Z 2025-07-17T07:11:30.8511678Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1218. 2025-07-17T07:11:30.8512777Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.8513245Z 2025-07-17T07:11:30.8513462Z Context passed to policy function during selective checkpointing. 2025-07-17T07:11:30.8513861Z 2025-07-17T07:11:30.8514101Z This class is used to pass relevant metadata to the policy function during 2025-07-17T07:11:30.8514773Z selective checkpointing. The metadata includes whether the current invocation 2025-07-17T07:11:30.8515381Z of the policy function is during recomputation or not. 2025-07-17T07:11:30.8515654Z 2025-07-17T07:11:30.8515756Z Example: 2025-07-17T07:11:30.8516103Z >>> # xdoctest: +SKIP(stub) 2025-07-17T07:11:30.8516420Z >>> 2025-07-17T07:11:30.8516670Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-07-17T07:11:30.8517084Z >>> print(ctx.is_recompute) 2025-07-17T07:11:30.8517360Z >>> 2025-07-17T07:11:30.8518006Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-07-17T07:11:30.8518499Z >>> 2025-07-17T07:11:30.8518744Z >>> out = torch.utils.checkpoint.checkpoint( 2025-07-17T07:11:30.8519090Z >>> fn, x, y, 2025-07-17T07:11:30.8519351Z >>> use_reentrant=False, 2025-07-17T07:11:30.8519638Z >>> context_fn=context_fn, 2025-07-17T07:11:30.8519927Z >>> ) 2025-07-17T07:11:30.8520040Z 2025-07-17T07:11:30.8520304Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.8520679Z 2025-07-17T07:11:30.8521344Z 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=1358. 2025-07-17T07:11:30.8522354Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.8522748Z 2025-07-17T07:11:30.8522986Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-07-17T07:11:30.8523356Z 2025-07-17T07:11:30.8523574Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-07-17T07:11:30.8524068Z operations are recomputed during the backward pass. 2025-07-17T07:11:30.8524343Z 2025-07-17T07:11:30.8524423Z Args: 2025-07-17T07:11:30.8524662Z policy_fn_or_list (Callable or List): 2025-07-17T07:11:30.8525061Z - If a policy function is provided, it should accept a 2025-07-17T07:11:30.8525589Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-07-17T07:11:30.8526155Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-07-17T07:11:30.8526727Z indicating whether the execution of the op should be recomputed or not. 2025-07-17T07:11:30.8527300Z - If a list of operations is provided, it is equivalent to a policy 2025-07-17T07:11:30.8527799Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-07-17T07:11:30.8528324Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-07-17T07:11:30.8528754Z operations. 2025-07-17T07:11:30.8529104Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-07-17T07:11:30.8529657Z raised if any tensors cached by selective activation checkpoint are 2025-07-17T07:11:30.8530201Z mutated in order to ensure correctness. If set to `True`, this check 2025-07-17T07:11:30.8530623Z is disabled. 2025-07-17T07:11:30.8530850Z Returns: 2025-07-17T07:11:30.8531083Z A tuple of two context managers. 2025-07-17T07:11:30.8531292Z 2025-07-17T07:11:30.8531391Z Example: 2025-07-17T07:11:30.8531605Z >>> # xdoctest: +REQUIRES(LINUX) 2025-07-17T07:11:30.8531908Z >>> import functools 2025-07-17T07:11:30.8532165Z >>> 2025-07-17T07:11:30.8532454Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-07-17T07:11:30.8532819Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-07-17T07:11:30.8533142Z >>> 2025-07-17T07:11:30.8533336Z >>> ops_to_save = [ 2025-07-17T07:11:30.8533614Z >>> torch.ops.aten.mm.default, 2025-07-17T07:11:30.8533915Z >>> ] 2025-07-17T07:11:30.8534113Z >>> 2025-07-17T07:11:30.8534339Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-07-17T07:11:30.8534678Z >>> if op in ops_to_save: 2025-07-17T07:11:30.8534996Z >>> return CheckpointPolicy.MUST_SAVE 2025-07-17T07:11:30.8535387Z >>> else: 2025-07-17T07:11:30.8535672Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-07-17T07:11:30.8536016Z >>> 2025-07-17T07:11:30.8536397Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-07-17T07:11:30.8536882Z >>> 2025-07-17T07:11:30.8537098Z >>> # or equivalently 2025-07-17T07:11:30.8537572Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-07-17T07:11:30.8538062Z >>> 2025-07-17T07:11:30.8538268Z >>> def fn(x, y): 2025-07-17T07:11:30.8538670Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-07-17T07:11:30.8539073Z >>> 2025-07-17T07:11:30.8539327Z >>> out = torch.utils.checkpoint.checkpoint( 2025-07-17T07:11:30.8539668Z >>> fn, x, y, 2025-07-17T07:11:30.8539909Z >>> use_reentrant=False, 2025-07-17T07:11:30.8540204Z >>> context_fn=context_fn, 2025-07-17T07:11:30.8540490Z >>> ) 2025-07-17T07:11:30.8540605Z 2025-07-17T07:11:30.8540854Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.8541236Z 2025-07-17T07:11:30.8743011Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1147. 2025-07-17T07:11:30.8743970Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.8744359Z 2025-07-17T07:11:30.8744513Z Create a :class:`setuptools.Extension` for C++. 2025-07-17T07:11:30.8744765Z 2025-07-17T07:11:30.8745026Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T07:11:30.8745601Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-07-17T07:11:30.8745953Z 2025-07-17T07:11:30.8746159Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T07:11:30.8746631Z constructor. Full list arguments can be found at 2025-07-17T07:11:30.8747216Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-07-17T07:11:30.8747670Z 2025-07-17T07:11:30.8747771Z .. warning:: 2025-07-17T07:11:30.8748134Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T07:11:30.8748695Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T07:11:30.8749218Z the user's responsibility in their library to not use APIs from 2025-07-17T07:11:30.8749890Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T07:11:30.8750492Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T07:11:30.8751046Z example, to give access to custom ops from python, the library should 2025-07-17T07:11:30.8751513Z register the ops through the dispatcher. 2025-07-17T07:11:30.8751758Z 2025-07-17T07:11:30.8751983Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T07:11:30.8752542Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T07:11:30.8753066Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T07:11:30.8753613Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T07:11:30.8754165Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T07:11:30.8754715Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T07:11:30.8755396Z CPython version of choice. 2025-07-17T07:11:30.8755600Z 2025-07-17T07:11:30.8755687Z Example: 2025-07-17T07:11:30.8755908Z >>> # xdoctest: +SKIP 2025-07-17T07:11:30.8756224Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:30.8756598Z >>> from setuptools import setup 2025-07-17T07:11:30.8757033Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-07-17T07:11:30.8757449Z >>> setup( 2025-07-17T07:11:30.8757686Z ... name='extension', 2025-07-17T07:11:30.8757966Z ... ext_modules=[ 2025-07-17T07:11:30.8758219Z ... CppExtension( 2025-07-17T07:11:30.8758506Z ... name='extension', 2025-07-17T07:11:30.8758832Z ... sources=['extension.cpp'], 2025-07-17T07:11:30.8759177Z ... extra_compile_args=['-g'], 2025-07-17T07:11:30.8759579Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-07-17T07:11:30.8759999Z ... ], 2025-07-17T07:11:30.8760234Z ... cmdclass={ 2025-07-17T07:11:30.8760506Z ... 'build_ext': BuildExtension 2025-07-17T07:11:30.8760817Z ... }) 2025-07-17T07:11:30.8760945Z 2025-07-17T07:11:30.8761287Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.8761678Z 2025-07-17T07:11:30.8762294Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1217. 2025-07-17T07:11:30.8763221Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.8763617Z 2025-07-17T07:11:30.8763775Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-07-17T07:11:30.8764043Z 2025-07-17T07:11:30.8764316Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T07:11:30.8765012Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-07-17T07:11:30.8765635Z extension. This includes the CUDA include path, library path and runtime 2025-07-17T07:11:30.8766154Z library. 2025-07-17T07:11:30.8766273Z 2025-07-17T07:11:30.8766494Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T07:11:30.8767030Z constructor. Full list arguments can be found at 2025-07-17T07:11:30.8767693Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-07-17T07:11:30.8768154Z 2025-07-17T07:11:30.8768247Z .. warning:: 2025-07-17T07:11:30.8768683Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T07:11:30.8769306Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T07:11:30.8769848Z the user's responsibility in their library to not use APIs from 2025-07-17T07:11:30.8770470Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T07:11:30.8771106Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T07:11:30.8771735Z example, to give access to custom ops from python, the library should 2025-07-17T07:11:30.8772446Z register the ops through the dispatcher. 2025-07-17T07:11:30.8772684Z 2025-07-17T07:11:30.8772927Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T07:11:30.8773536Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T07:11:30.8774141Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T07:11:30.8774955Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T07:11:30.8775627Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T07:11:30.8776191Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T07:11:30.8776712Z CPython version of choice. 2025-07-17T07:11:30.8776897Z 2025-07-17T07:11:30.8777000Z Example: 2025-07-17T07:11:30.8777272Z >>> # xdoctest: +SKIP 2025-07-17T07:11:30.8777606Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:30.8778135Z >>> from setuptools import setup 2025-07-17T07:11:30.8778563Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-07-17T07:11:30.8779076Z >>> setup( 2025-07-17T07:11:30.8779317Z ... name='cuda_extension', 2025-07-17T07:11:30.8779661Z ... ext_modules=[ 2025-07-17T07:11:30.8779929Z ... CUDAExtension( 2025-07-17T07:11:30.8780226Z ... name='cuda_extension', 2025-07-17T07:11:30.8780692Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T07:11:30.8781097Z ... extra_compile_args={'cxx': ['-g'], 2025-07-17T07:11:30.8781525Z ... 'nvcc': ['-O2']}, 2025-07-17T07:11:30.8781917Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-07-17T07:11:30.8782271Z ... ], 2025-07-17T07:11:30.8782496Z ... cmdclass={ 2025-07-17T07:11:30.8782899Z ... 'build_ext': BuildExtension 2025-07-17T07:11:30.8783200Z ... }) 2025-07-17T07:11:30.8783343Z 2025-07-17T07:11:30.8783442Z Compute capabilities: 2025-07-17T07:11:30.8783603Z 2025-07-17T07:11:30.8783989Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-07-17T07:11:30.8784704Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-07-17T07:11:30.8785479Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-07-17T07:11:30.8786202Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-07-17T07:11:30.8786921Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-07-17T07:11:30.8787440Z support (see below for details on PTX). 2025-07-17T07:11:30.8787672Z 2025-07-17T07:11:30.8787982Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-07-17T07:11:30.8788544Z CCs you want the extension to support: 2025-07-17T07:11:30.8788826Z 2025-07-17T07:11:30.8789031Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-07-17T07:11:30.8789567Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-07-17T07:11:30.8789935Z 2025-07-17T07:11:30.8790254Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-07-17T07:11:30.8791017Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-07-17T07:11:30.8791756Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-07-17T07:11:30.8792463Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-07-17T07:11:30.8793204Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-07-17T07:11:30.8793926Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-07-17T07:11:30.8794648Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-07-17T07:11:30.8795402Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-07-17T07:11:30.8796014Z "8.0 8.6" would be better. 2025-07-17T07:11:30.8796181Z 2025-07-17T07:11:30.8796491Z Note that while it's possible to include all supported archs, the more archs get included the 2025-07-17T07:11:30.8797195Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-07-17T07:11:30.8797619Z 2025-07-17T07:11:30.8797950Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-07-17T07:11:30.8798624Z To workaround the issue, move python binding logic to pure C++ file. 2025-07-17T07:11:30.8798949Z 2025-07-17T07:11:30.8799048Z Example use: 2025-07-17T07:11:30.8799273Z #include 2025-07-17T07:11:30.8799645Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-07-17T07:11:30.8799902Z 2025-07-17T07:11:30.8800001Z Instead of: 2025-07-17T07:11:30.8800236Z #include 2025-07-17T07:11:30.8800584Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-07-17T07:11:30.8800862Z 2025-07-17T07:11:30.8801136Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-07-17T07:11:30.8802047Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-07-17T07:11:30.8802676Z 2025-07-17T07:11:30.8802785Z Relocatable device code linking: 2025-07-17T07:11:30.8802996Z 2025-07-17T07:11:30.8803275Z If you want to reference device symbols across compilation units (across object files), 2025-07-17T07:11:30.8803939Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-07-17T07:11:30.8804720Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-07-17T07:11:30.8805529Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-07-17T07:11:30.8806363Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-07-17T07:11:30.8806990Z helps reduce the protentional perf degradation of `-rdc`. 2025-07-17T07:11:30.8807454Z Note that it needs to be used at both steps to be useful. 2025-07-17T07:11:30.8807730Z 2025-07-17T07:11:30.8808097Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-07-17T07:11:30.8808763Z There is also a case where `-dlink` is used without `-rdc`: 2025-07-17T07:11:30.8809390Z when an extension is linked against a static lib containing rdc-compiled objects 2025-07-17T07:11:30.8809984Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-07-17T07:11:30.8810312Z 2025-07-17T07:11:30.8810517Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-07-17T07:11:30.8810847Z 2025-07-17T07:11:30.8810935Z Example: 2025-07-17T07:11:30.8811161Z >>> # xdoctest: +SKIP 2025-07-17T07:11:30.8811475Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:30.8811839Z >>> CUDAExtension( 2025-07-17T07:11:30.8812109Z ... name='cuda_extension', 2025-07-17T07:11:30.8812473Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T07:11:30.8812852Z ... dlink=True, 2025-07-17T07:11:30.8813149Z ... dlink_libraries=["dlink_lib"], 2025-07-17T07:11:30.8813510Z ... extra_compile_args={'cxx': ['-g'], 2025-07-17T07:11:30.8813863Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-07-17T07:11:30.8814114Z 2025-07-17T07:11:30.8814368Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.8814737Z 2025-07-17T07:11:30.8815364Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1408. 2025-07-17T07:11:30.8816310Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.8816690Z 2025-07-17T07:11:30.8816853Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-07-17T07:11:30.8817139Z 2025-07-17T07:11:30.8817381Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T07:11:30.8817945Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-07-17T07:11:30.8818342Z extension. 2025-07-17T07:11:30.8818478Z 2025-07-17T07:11:30.8818683Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T07:11:30.8819097Z constructor. 2025-07-17T07:11:30.8819228Z 2025-07-17T07:11:30.8819330Z .. warning:: 2025-07-17T07:11:30.8819679Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T07:11:30.8820244Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T07:11:30.8820833Z the user's responsibility in their library to not use APIs from 2025-07-17T07:11:30.8821371Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T07:11:30.8821950Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T07:11:30.8822504Z example, to give access to custom ops from python, the library should 2025-07-17T07:11:30.8822969Z register the ops through the dispatcher. 2025-07-17T07:11:30.8823199Z 2025-07-17T07:11:30.8823421Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T07:11:30.8823973Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T07:11:30.8824500Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T07:11:30.8825036Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T07:11:30.8825618Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T07:11:30.8826175Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T07:11:30.8826625Z CPython version of choice. 2025-07-17T07:11:30.8826810Z 2025-07-17T07:11:30.8826952Z Example: 2025-07-17T07:11:30.8827177Z >>> # xdoctest: +SKIP 2025-07-17T07:11:30.8827494Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:30.8827973Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-07-17T07:11:30.8828406Z >>> setup( 2025-07-17T07:11:30.8828643Z ... name='xpu_extension', 2025-07-17T07:11:30.8828925Z ... ext_modules=[ 2025-07-17T07:11:30.8829189Z ... SyclExtension( 2025-07-17T07:11:30.8829473Z ... name='xpu_extension', 2025-07-17T07:11:30.8829864Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-07-17T07:11:30.8830322Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-07-17T07:11:30.8830718Z ... ], 2025-07-17T07:11:30.8830940Z ... cmdclass={ 2025-07-17T07:11:30.8831196Z ... 'build_ext': BuildExtension 2025-07-17T07:11:30.8831504Z ... }) 2025-07-17T07:11:30.8831628Z 2025-07-17T07:11:30.8831940Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-07-17T07:11:30.8832606Z building process of the extension. If down the road a new card is installed the 2025-07-17T07:11:30.8833243Z extension may need to be recompiled. You can override the default behavior using 2025-07-17T07:11:30.8833930Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-07-17T07:11:30.8834451Z to support: 2025-07-17T07:11:30.8834575Z 2025-07-17T07:11:30.8834785Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-07-17T07:11:30.8835107Z 2025-07-17T07:11:30.8835493Z Note that while it's possible to include all supported archs, the more archs get included the 2025-07-17T07:11:30.8836218Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-07-17T07:11:30.8836631Z 2025-07-17T07:11:30.8836782Z Note: Ninja is required to build SyclExtension. 2025-07-17T07:11:30.8837030Z 2025-07-17T07:11:30.8837278Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.8837658Z 2025-07-17T07:11:30.8838217Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1585. 2025-07-17T07:11:30.8839091Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.8839486Z 2025-07-17T07:11:30.8839628Z Load a PyTorch C++ extension just-in-time (JIT). 2025-07-17T07:11:30.8839895Z 2025-07-17T07:11:30.8840101Z To load an extension, a Ninja build file is emitted, which is used to 2025-07-17T07:11:30.8840634Z compile the given sources into a dynamic library. This library is 2025-07-17T07:11:30.8841176Z subsequently loaded into the current Python process as a module and 2025-07-17T07:11:30.8841687Z returned from this function, ready for use. 2025-07-17T07:11:30.8841915Z 2025-07-17T07:11:30.8842137Z By default, the directory to which the build file is emitted and the 2025-07-17T07:11:30.8842684Z resulting library compiled to is ``/torch_extensions/``, where 2025-07-17T07:11:30.8843247Z ```` is the temporary folder on the current platform and ```` 2025-07-17T07:11:30.8843788Z the name of the extension. This location can be overridden in two ways. 2025-07-17T07:11:30.8844332Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-07-17T07:11:30.8844868Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-07-17T07:11:30.8845423Z into subfolders of this directory. Second, if the ``build_directory`` 2025-07-17T07:11:30.8845997Z argument to this function is supplied, it overrides the entire path, i.e. 2025-07-17T07:11:30.8846542Z the library will be compiled into that folder directly. 2025-07-17T07:11:30.8846828Z 2025-07-17T07:11:30.8847036Z To compile the sources, the default system compiler (``c++``) is used, 2025-07-17T07:11:30.8847656Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-07-17T07:11:30.8848343Z additional arguments to the compilation process, ``extra_cflags`` or 2025-07-17T07:11:30.8848897Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-07-17T07:11:30.8849464Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-07-17T07:11:30.8849956Z ``extra_cflags`` to pass further include directories. 2025-07-17T07:11:30.8850219Z 2025-07-17T07:11:30.8850468Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-07-17T07:11:30.8851002Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-07-17T07:11:30.8851548Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-07-17T07:11:30.8852125Z passing the CUDA lib64 directory as a library directory, and linking 2025-07-17T07:11:30.8852598Z ``cudart``. You can pass additional flags to nvcc via 2025-07-17T07:11:30.8853072Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-07-17T07:11:30.8853635Z heuristics for finding the CUDA install directory are used, which usually 2025-07-17T07:11:30.8854201Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-07-17T07:11:30.8854612Z safest option. 2025-07-17T07:11:30.8854761Z 2025-07-17T07:11:30.8854995Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-07-17T07:11:30.8855617Z files (``.sycl``) along with other sources. Such files will be detected 2025-07-17T07:11:30.8856149Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-07-17T07:11:30.8856700Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-07-17T07:11:30.8857227Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-07-17T07:11:30.8857759Z SYCL compiler is expected to be found via system PATH environment 2025-07-17T07:11:30.8858163Z variable. 2025-07-17T07:11:30.8858302Z 2025-07-17T07:11:30.8858384Z Args: 2025-07-17T07:11:30.8858722Z name: The name of the extension to build. This MUST be the same as the 2025-07-17T07:11:30.8859151Z name of the pybind11 module! 2025-07-17T07:11:30.8859571Z sources: A list of relative or absolute paths to C++ source files. 2025-07-17T07:11:30.8860126Z extra_cflags: optional list of compiler flags to forward to the build. 2025-07-17T07:11:30.8860696Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-07-17T07:11:30.8861139Z when building CUDA sources. 2025-07-17T07:11:30.8861568Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-07-17T07:11:30.8862035Z compiler when building SYCL sources. 2025-07-17T07:11:30.8862471Z extra_ldflags: optional list of linker flags to forward to the build. 2025-07-17T07:11:30.8863071Z extra_include_paths: optional list of include directories to forward 2025-07-17T07:11:30.8863507Z to the build. 2025-07-17T07:11:30.8863832Z build_directory: optional path to use as build workspace. 2025-07-17T07:11:30.8864310Z verbose: If ``True``, turns on verbose logging of load steps. 2025-07-17T07:11:30.8864827Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-07-17T07:11:30.8865335Z the build. If set to ``None`` (default), this value is 2025-07-17T07:11:30.8865806Z automatically determined based on the existence of ``.cu`` or 2025-07-17T07:11:30.8866302Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-07-17T07:11:30.8866699Z and libraries to be included. 2025-07-17T07:11:30.8867119Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-07-17T07:11:30.8867621Z the build. If set to ``None`` (default), this value is 2025-07-17T07:11:30.8868139Z automatically determined based on the existence of ``.sycl`` in 2025-07-17T07:11:30.8868631Z ``sources``. Set it to `True`` to force SYCL headers and 2025-07-17T07:11:30.8869004Z libraries to be included. 2025-07-17T07:11:30.8869997Z is_python_module: If ``True`` (default), imports the produced shared 2025-07-17T07:11:30.8870522Z library as a Python module. If ``False``, behavior depends on 2025-07-17T07:11:30.8870914Z ``is_standalone``. 2025-07-17T07:11:30.8871299Z is_standalone: If ``False`` (default) loads the constructed extension 2025-07-17T07:11:30.8871964Z into the process as a plain dynamic library. If ``True``, build a 2025-07-17T07:11:30.8872382Z standalone executable. 2025-07-17T07:11:30.8872580Z 2025-07-17T07:11:30.8872665Z Returns: 2025-07-17T07:11:30.8872898Z If ``is_python_module`` is ``True``: 2025-07-17T07:11:30.8873296Z Returns the loaded PyTorch extension as a Python module. 2025-07-17T07:11:30.8873590Z 2025-07-17T07:11:30.8873794Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-07-17T07:11:30.8874328Z Returns nothing. (The shared library is loaded into the process as 2025-07-17T07:11:30.8874755Z a side effect.) 2025-07-17T07:11:30.8874912Z 2025-07-17T07:11:30.8875017Z If ``is_standalone`` is ``True``. 2025-07-17T07:11:30.8875428Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-07-17T07:11:30.8875926Z added to the PATH environment variable as a side effect.) 2025-07-17T07:11:30.8876214Z 2025-07-17T07:11:30.8876308Z Example: 2025-07-17T07:11:30.8876513Z >>> # xdoctest: +SKIP 2025-07-17T07:11:30.8876821Z >>> from torch.utils.cpp_extension import load 2025-07-17T07:11:30.8877167Z >>> module = load( 2025-07-17T07:11:30.8877411Z ... name='extension', 2025-07-17T07:11:30.8877756Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T07:11:30.8878139Z ... extra_cflags=['-O2'], 2025-07-17T07:11:30.8878420Z ... verbose=True) 2025-07-17T07:11:30.8878595Z 2025-07-17T07:11:30.8878845Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.8879223Z 2025-07-17T07:11:30.8879758Z 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=1890. 2025-07-17T07:11:30.8880674Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.8881050Z 2025-07-17T07:11:30.8881261Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-07-17T07:11:30.8881598Z 2025-07-17T07:11:30.8881827Z This function behaves exactly like :func:`load`, but takes its sources as 2025-07-17T07:11:30.8882410Z strings rather than filenames. These strings are stored to files in the 2025-07-17T07:11:30.8882967Z build directory, after which the behavior of :func:`load_inline` is 2025-07-17T07:11:30.8883386Z identical to :func:`load`. 2025-07-17T07:11:30.8883637Z 2025-07-17T07:11:30.8883720Z See `the 2025-07-17T07:11:30.8884172Z tests `_ 2025-07-17T07:11:30.8884731Z for good examples of using this function. 2025-07-17T07:11:30.8884977Z 2025-07-17T07:11:30.8885207Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-07-17T07:11:30.8885806Z the necessary header includes, as well as the (pybind11) binding code. More 2025-07-17T07:11:30.8886407Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-07-17T07:11:30.8886945Z single ``.cpp`` file. This file is then prepended with ``#include 2025-07-17T07:11:30.8887448Z `` 2025-07-17T07:11:30.8887604Z 2025-07-17T07:11:30.8887839Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-07-17T07:11:30.8888507Z automatically generated for each function specified. ``functions`` can 2025-07-17T07:11:30.8889126Z either be a list of function names, or a dictionary mapping from function 2025-07-17T07:11:30.8889696Z names to docstrings. If a list is given, the name of each function is used 2025-07-17T07:11:30.8890133Z as its docstring. 2025-07-17T07:11:30.8890275Z 2025-07-17T07:11:30.8890560Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-07-17T07:11:30.8891077Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-07-17T07:11:30.8891576Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-07-17T07:11:30.8892127Z separately, but ultimately linked into a single library. Note that no 2025-07-17T07:11:30.8892690Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-07-17T07:11:30.8893256Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-07-17T07:11:30.8893805Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-07-17T07:11:30.8894238Z include its name in ``functions``). 2025-07-17T07:11:30.8894461Z 2025-07-17T07:11:30.8894684Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-07-17T07:11:30.8895302Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-07-17T07:11:30.8895848Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-07-17T07:11:30.8896373Z linked into a single library. Note that no bindings are generated for 2025-07-17T07:11:30.8896926Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-07-17T07:11:30.8897468Z create a C++ function that calls it, and either declare or define this 2025-07-17T07:11:30.8897977Z C++ function in one of the ``cpp_sources`` (and include its name 2025-07-17T07:11:30.8898373Z in ``functions``). 2025-07-17T07:11:30.8898516Z 2025-07-17T07:11:30.8898532Z 2025-07-17T07:11:30.8898536Z 2025-07-17T07:11:30.8898718Z See :func:`load` for a description of arguments omitted below. 2025-07-17T07:11:30.8899010Z 2025-07-17T07:11:30.8899105Z Args: 2025-07-17T07:11:30.8899425Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-07-17T07:11:30.8899984Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-07-17T07:11:30.8900560Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-07-17T07:11:30.8901100Z functions: A list of function names for which to generate function 2025-07-17T07:11:30.8901641Z bindings. If a dictionary is given, it should map function names to 2025-07-17T07:11:30.8902167Z docstrings (which are otherwise just the function names). 2025-07-17T07:11:30.8902699Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-07-17T07:11:30.8903196Z the build. If set to ``None`` (default), this value is 2025-07-17T07:11:30.8903686Z automatically determined based on whether ``cuda_sources`` is 2025-07-17T07:11:30.8904171Z provided. Set it to ``True`` to force CUDA headers 2025-07-17T07:11:30.8904545Z and libraries to be included. 2025-07-17T07:11:30.8905016Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-07-17T07:11:30.8905521Z the build. If set to ``None`` (default), this value is 2025-07-17T07:11:30.8906011Z automatically determined based on whether ``sycl_sources`` is 2025-07-17T07:11:30.8906476Z provided. Set it to ``True`` to force SYCL headers 2025-07-17T07:11:30.8906860Z and libraries to be included. 2025-07-17T07:11:30.8907286Z with_pytorch_error_handling: Determines whether pytorch error and 2025-07-17T07:11:30.8907810Z warning macros are handled by pytorch instead of pybind. To do 2025-07-17T07:11:30.8908350Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-07-17T07:11:30.8908887Z function. This redirection might cause issues in obscure cases 2025-07-17T07:11:30.8909395Z of cpp. This flag should be set to ``False`` when this redirect 2025-07-17T07:11:30.8909857Z causes issues. 2025-07-17T07:11:30.8910273Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-07-17T07:11:30.8910884Z ``#include `` and ``#include `` lines. 2025-07-17T07:11:30.8911426Z Use this option to improve cold start times when you 2025-07-17T07:11:30.8911969Z already include the necessary headers in your source code. Default: ``False``. 2025-07-17T07:11:30.8912364Z 2025-07-17T07:11:30.8912449Z Example: 2025-07-17T07:11:30.8912716Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:30.8913121Z >>> from torch.utils.cpp_extension import load_inline 2025-07-17T07:11:30.8913483Z >>> source = """ 2025-07-17T07:11:30.8913784Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-07-17T07:11:30.8914126Z return x.sin() + y.sin(); 2025-07-17T07:11:30.8914394Z } 2025-07-17T07:11:30.8914593Z """ 2025-07-17T07:11:30.8914839Z >>> module = load_inline(name='inline_extension', 2025-07-17T07:11:30.8915213Z ... cpp_sources=[source], 2025-07-17T07:11:30.8915563Z ... functions=['sin_add']) 2025-07-17T07:11:30.8915793Z 2025-07-17T07:11:30.8915892Z .. note:: 2025-07-17T07:11:30.8916250Z Since load_inline will just-in-time compile the source code, please ensure 2025-07-17T07:11:30.8930233Z that you have the right toolchains installed in the runtime. For example, 2025-07-17T07:11:30.8930842Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-07-17T07:11:30.8931444Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-07-17T07:11:30.8932059Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-07-17T07:11:30.8932679Z are not included when you install torch and must be additionally installed. 2025-07-17T07:11:30.8933038Z 2025-07-17T07:11:30.8933308Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-07-17T07:11:30.8933926Z the extension. This may use up too many resources on some systems. One 2025-07-17T07:11:30.8934482Z can control the number of workers by setting the `MAX_JOBS` environment 2025-07-17T07:11:30.8934952Z variable to a non-negative number. 2025-07-17T07:11:30.8935261Z 2025-07-17T07:11:30.8935531Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.8935902Z 2025-07-17T07:11:30.8958174Z 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. 2025-07-17T07:11:30.8959176Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.8959573Z 2025-07-17T07:11:30.8959873Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-07-17T07:11:30.8960295Z 2025-07-17T07:11:30.8960590Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-07-17T07:11:30.8961354Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-07-17T07:11:30.8961953Z server like load. It can emulate multiple calling threads to a single module 2025-07-17T07:11:30.8962563Z provided. In the future we plan to enhance this component to support inter and 2025-07-17T07:11:30.8963182Z intra-op parallelism as well as multiple models running in a single process. 2025-07-17T07:11:30.8963546Z 2025-07-17T07:11:30.8963816Z Please note that even though nn.Module is supported, it might incur an overhead 2025-07-17T07:11:30.8964402Z from the need to hold GIL every time we execute Python code or pass around 2025-07-17T07:11:30.8964990Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-07-17T07:11:30.8965590Z model for inference deployment it is better to switch to using it in this 2025-07-17T07:11:30.8966030Z benchmark. 2025-07-17T07:11:30.8966169Z 2025-07-17T07:11:30.8966289Z Example:: 2025-07-17T07:11:30.8966407Z 2025-07-17T07:11:30.8966545Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:30.8966918Z >>> from torch.utils import ThroughputBenchmark 2025-07-17T07:11:30.8967293Z >>> bench = ThroughputBenchmark(my_module) 2025-07-17T07:11:30.8967758Z >>> # Pre-populate benchmark's data set with the inputs 2025-07-17T07:11:30.8968139Z >>> for input in inputs: 2025-07-17T07:11:30.8968532Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-07-17T07:11:30.8969006Z ... bench.add_input(input[0], x2=input[1]) 2025-07-17T07:11:30.8969458Z >>> # Inputs supplied above are randomly used during the execution 2025-07-17T07:11:30.8969885Z >>> stats = bench.benchmark( 2025-07-17T07:11:30.8970177Z ... num_calling_threads=4, 2025-07-17T07:11:30.8970485Z ... num_warmup_iters = 100, 2025-07-17T07:11:30.8970792Z ... num_iters = 1000, 2025-07-17T07:11:30.8971049Z ... ) 2025-07-17T07:11:30.8971359Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-07-17T07:11:30.8971985Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-07-17T07:11:30.8972277Z 2025-07-17T07:11:30.8972535Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.8972919Z 2025-07-17T07:11:30.9858390Z 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. 2025-07-17T07:11:30.9859406Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:30.9859997Z Sampler that restricts data loading to a subset of the dataset. 2025-07-17T07:11:30.9860331Z 2025-07-17T07:11:30.9860472Z It is especially useful in conjunction with 2025-07-17T07:11:30.9860981Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-07-17T07:11:30.9861619Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-07-17T07:11:30.9862243Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-07-17T07:11:30.9862733Z original dataset that is exclusive to it. 2025-07-17T07:11:30.9862968Z 2025-07-17T07:11:30.9863077Z .. note:: 2025-07-17T07:11:30.9863447Z Dataset is assumed to be of constant size and that any instance of it always 2025-07-17T07:11:30.9863962Z returns the same elements in the same order. 2025-07-17T07:11:30.9864226Z 2025-07-17T07:11:30.9864310Z Args: 2025-07-17T07:11:30.9864554Z dataset: Dataset used for sampling. 2025-07-17T07:11:30.9864999Z num_replicas (int, optional): Number of processes participating in 2025-07-17T07:11:30.9865594Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-07-17T07:11:30.9866084Z current distributed group. 2025-07-17T07:11:30.9866537Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-07-17T07:11:30.9867110Z By default, :attr:`rank` is retrieved from the current distributed 2025-07-17T07:11:30.9867676Z group. 2025-07-17T07:11:30.9868054Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-07-17T07:11:30.9868490Z indices. 2025-07-17T07:11:30.9868846Z seed (int, optional): random seed used to shuffle the sampler if 2025-07-17T07:11:30.9869363Z :attr:`shuffle=True`. This number should be identical across all 2025-07-17T07:11:30.9869842Z processes in the distributed group. Default: ``0``. 2025-07-17T07:11:30.9870354Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-07-17T07:11:30.9870906Z tail of the data to make it evenly divisible across the number of 2025-07-17T07:11:30.9871429Z replicas. If ``False``, the sampler will add extra indices to make 2025-07-17T07:11:30.9872109Z the data evenly divisible across the replicas. Default: ``False``. 2025-07-17T07:11:30.9872520Z 2025-07-17T07:11:30.9872615Z .. warning:: 2025-07-17T07:11:30.9872952Z In distributed mode, calling the :meth:`set_epoch` method at 2025-07-17T07:11:30.9873598Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-07-17T07:11:30.9874225Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-07-17T07:11:30.9874737Z the same ordering will be always used. 2025-07-17T07:11:30.9874967Z 2025-07-17T07:11:30.9875074Z Example:: 2025-07-17T07:11:30.9875203Z 2025-07-17T07:11:30.9875304Z >>> # xdoctest: +SKIP 2025-07-17T07:11:30.9875723Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-07-17T07:11:30.9876240Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-07-17T07:11:30.9876643Z ... sampler=sampler) 2025-07-17T07:11:30.9876997Z >>> for epoch in range(start_epoch, n_epochs): 2025-07-17T07:11:30.9877352Z ... if is_distributed: 2025-07-17T07:11:30.9877671Z ... sampler.set_epoch(epoch) 2025-07-17T07:11:30.9877982Z ... train(loader) 2025-07-17T07:11:30.9878248Z 2025-07-17T07:11:30.9878618Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:30.9878987Z 2025-07-17T07:11:31.1725835Z gathering tests 2025-07-17T07:11:31.1737804Z running 719 test(s) 2025-07-17T07:11:31.1743883Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1074 <- wrt source file 2025-07-17T07:11:31.1750898Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0 2025-07-17T07:11:31.1752022Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1110 <- wrt source file 2025-07-17T07:11:31.1755693Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0 2025-07-17T07:11:31.1756983Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1195 <- wrt source file 2025-07-17T07:11:31.1758340Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0 2025-07-17T07:11:31.1759560Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1244 <- wrt source file 2025-07-17T07:11:31.1761015Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0 2025-07-17T07:11:31.1762466Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1281 <- wrt source file 2025-07-17T07:11:31.1763672Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0 2025-07-17T07:11:31.1765215Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1436 <- wrt source file 2025-07-17T07:11:31.1766587Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2025-07-17T07:11:31.1767968Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2559 <- wrt source file 2025-07-17T07:11:31.1769327Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0 2025-07-17T07:11:31.1770553Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2832 <- wrt source file 2025-07-17T07:11:31.1772042Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2025-07-17T07:11:31.1773469Z * 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 2025-07-17T07:11:31.1774945Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0 2025-07-17T07:11:31.1776369Z * 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 2025-07-17T07:11:31.1777762Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0 2025-07-17T07:11:31.1778993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2025-07-17T07:11:31.1780129Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0 2025-07-17T07:11:31.1781240Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 138 <- wrt source file 2025-07-17T07:11:31.1782352Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0 2025-07-17T07:11:31.1783479Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 208 <- wrt source file 2025-07-17T07:11:31.2159040Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0 2025-07-17T07:11:31.2160420Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2025-07-17T07:11:31.2161740Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0 2025-07-17T07:11:31.2162982Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 649 <- wrt source file 2025-07-17T07:11:31.2167377Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0 2025-07-17T07:11:31.2168680Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 706 <- wrt source file 2025-07-17T07:11:31.2184603Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2025-07-17T07:11:31.2185901Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1333 <- wrt source file 2025-07-17T07:11:31.2266390Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0 2025-07-17T07:11:31.2267573Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1378 <- wrt source file 2025-07-17T07:11:31.2271189Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0 2025-07-17T07:11:31.2272594Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1451 <- wrt source file 2025-07-17T07:11:31.2278286Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0 2025-07-17T07:11:31.2279466Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1481 <- wrt source file 2025-07-17T07:11:31.2283488Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2025-07-17T07:11:31.2284695Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2025-07-17T07:11:31.2302513Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0 2025-07-17T07:11:31.2303849Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file 2025-07-17T07:11:31.2308863Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0 2025-07-17T07:11:31.2310060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file 2025-07-17T07:11:31.2311899Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0 2025-07-17T07:11:31.2313050Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 193 <- wrt source file 2025-07-17T07:11:31.2324210Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0 2025-07-17T07:11:31.2325311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 307 <- wrt source file 2025-07-17T07:11:31.2340720Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0 2025-07-17T07:11:31.2341934Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 1041 <- wrt source file 2025-07-17T07:11:31.2352618Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0 2025-07-17T07:11:31.2353833Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1316 <- wrt source file 2025-07-17T07:11:31.2362500Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0 2025-07-17T07:11:31.2363694Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1400 <- wrt source file 2025-07-17T07:11:31.2369722Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0 2025-07-17T07:11:31.2370897Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1434 <- wrt source file 2025-07-17T07:11:31.2380171Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0 2025-07-17T07:11:31.2381344Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1490 <- wrt source file 2025-07-17T07:11:31.2393261Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0 2025-07-17T07:11:31.2394537Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1531 <- wrt source file 2025-07-17T07:11:31.2409462Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0 2025-07-17T07:11:31.2410614Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1567 <- wrt source file 2025-07-17T07:11:31.2426376Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0 2025-07-17T07:11:31.2427509Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1605 <- wrt source file 2025-07-17T07:11:31.2447066Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0 2025-07-17T07:11:31.2448282Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1778 <- wrt source file 2025-07-17T07:11:31.2479502Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0 2025-07-17T07:11:31.2480648Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1946 <- wrt source file 2025-07-17T07:11:31.2505837Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0 2025-07-17T07:11:31.2507110Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2046 <- wrt source file 2025-07-17T07:11:31.2508693Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0 2025-07-17T07:11:31.2509884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2146 <- wrt source file 2025-07-17T07:11:31.2511051Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0 2025-07-17T07:11:31.2512141Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 468 <- wrt source file 2025-07-17T07:11:31.2513430Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0 2025-07-17T07:11:31.2514445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 528 <- wrt source file 2025-07-17T07:11:31.2515457Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0 2025-07-17T07:11:31.2516553Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 152 <- wrt source file 2025-07-17T07:11:31.2517719Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0 2025-07-17T07:11:31.2518961Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 246 <- wrt source file 2025-07-17T07:11:31.2524865Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2025-07-17T07:11:31.2526101Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 306 <- wrt source file 2025-07-17T07:11:31.2529063Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0 2025-07-17T07:11:31.2530175Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 511 <- wrt source file 2025-07-17T07:11:31.2539690Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0 2025-07-17T07:11:31.2554844Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 617 <- wrt source file 2025-07-17T07:11:31.2555949Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0 2025-07-17T07:11:31.2557071Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 798 <- wrt source file 2025-07-17T07:11:31.2558263Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0 2025-07-17T07:11:31.2559428Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0, line 866 <- wrt source file 2025-07-17T07:11:31.2560631Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0 2025-07-17T07:11:31.2561919Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 1217 <- wrt source file 2025-07-17T07:11:31.2631731Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0 2025-07-17T07:11:31.2632923Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1306 <- wrt source file 2025-07-17T07:11:31.2778875Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0 2025-07-17T07:11:31.2780077Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 116 <- wrt source file 2025-07-17T07:11:31.2784697Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0 2025-07-17T07:11:31.2785947Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 422 <- wrt source file 2025-07-17T07:11:31.2820522Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0 2025-07-17T07:11:31.2821777Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1575 <- wrt source file 2025-07-17T07:11:31.2823665Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0 2025-07-17T07:11:31.2824906Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1710 <- wrt source file 2025-07-17T07:11:31.2826702Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0 2025-07-17T07:11:31.2827975Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1958 <- wrt source file 2025-07-17T07:11:31.2851273Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2025-07-17T07:11:31.2852533Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1977 <- wrt source file 2025-07-17T07:11:31.2858176Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0 2025-07-17T07:11:31.2859362Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2025-07-17T07:11:31.2860543Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0 2025-07-17T07:11:31.2861754Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 299 <- wrt source file 2025-07-17T07:11:31.2863109Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0 2025-07-17T07:11:31.2864332Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 324 <- wrt source file 2025-07-17T07:11:31.2865540Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0 2025-07-17T07:11:31.2866895Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 400 <- wrt source file 2025-07-17T07:11:31.2868167Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0 2025-07-17T07:11:31.2869383Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 472 <- wrt source file 2025-07-17T07:11:31.2870800Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0 2025-07-17T07:11:31.2872326Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 950 <- wrt source file 2025-07-17T07:11:31.2873489Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0 2025-07-17T07:11:31.2874650Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file 2025-07-17T07:11:31.2875846Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0 2025-07-17T07:11:31.2877073Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 320 <- wrt source file 2025-07-17T07:11:31.2878367Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2025-07-17T07:11:31.2879619Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 357 <- wrt source file 2025-07-17T07:11:31.2883078Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0 2025-07-17T07:11:31.2884377Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 113 <- wrt source file 2025-07-17T07:11:31.2888138Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2025-07-17T07:11:31.2889696Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0, line 249 <- wrt source file 2025-07-17T07:11:31.2891000Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0 2025-07-17T07:11:31.2892256Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 123 <- wrt source file 2025-07-17T07:11:31.2893522Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2025-07-17T07:11:31.2894777Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 179 <- wrt source file 2025-07-17T07:11:31.9134975Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2025-07-17T07:11:31.9136325Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 406 <- wrt source file 2025-07-17T07:11:31.9137720Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2025-07-17T07:11:31.9139183Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 438 <- wrt source file 2025-07-17T07:11:31.9140453Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0 2025-07-17T07:11:31.9141836Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 459 <- wrt source file 2025-07-17T07:11:31.9143158Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2025-07-17T07:11:31.9144646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0, line 477 <- wrt source file 2025-07-17T07:11:31.9146224Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0 2025-07-17T07:11:31.9147739Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 492 <- wrt source file 2025-07-17T07:11:31.9149296Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0 2025-07-17T07:11:31.9150685Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 507 <- wrt source file 2025-07-17T07:11:31.9152137Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0 2025-07-17T07:11:31.9153577Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py::_compile_kernel:0, line 1760 <- wrt source file 2025-07-17T07:11:31.9154803Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py::_compile_kernel:0 2025-07-17T07:11:31.9155961Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0, line 357 <- wrt source file 2025-07-17T07:11:31.9157112Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::save:0 2025-07-17T07:11:31.9158228Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0, line 427 <- wrt source file 2025-07-17T07:11:31.9159357Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::load:0 2025-07-17T07:11:31.9160557Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0, line 575 <- wrt source file 2025-07-17T07:11:31.9161846Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/__init__.py::register_dataclass:0 2025-07-17T07:11:31.9163150Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 197 <- wrt source file 2025-07-17T07:11:31.9164502Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2025-07-17T07:11:31.9165814Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 261 <- wrt source file 2025-07-17T07:11:31.9167126Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0, line 561 <- wrt source file 2025-07-17T07:11:31.9379551Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::as_sparse_gradcheck:0 2025-07-17T07:11:31.9380859Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 344 <- wrt source file 2025-07-17T07:11:31.9382731Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2025-07-17T07:11:31.9384691Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 322 <- wrt source file 2025-07-17T07:11:31.9386466Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2025-07-17T07:11:31.9387904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0, line 1345 <- wrt source file 2025-07-17T07:11:31.9389342Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0 2025-07-17T07:11:31.9391008Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0, line 157 <- wrt source file 2025-07-17T07:11:31.9392619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0 2025-07-17T07:11:31.9394040Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 747 <- wrt source file 2025-07-17T07:11:31.9755573Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2025-07-17T07:11:31.9757024Z * 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 2025-07-17T07:11:31.9758597Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2025-07-17T07:11:31.9760016Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 233 <- wrt source file 2025-07-17T07:11:31.9812413Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2025-07-17T07:11:31.9813669Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 475 <- wrt source file 2025-07-17T07:11:31.9886307Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2025-07-17T07:11:31.9887585Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2025-07-17T07:11:32.0390567Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2025-07-17T07:11:32.0448271Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2025-07-17T07:11:32.0450828Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2025-07-17T07:11:32.0453341Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2025-07-17T07:11:32.0465921Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2025-07-17T07:11:32.0468539Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2025-07-17T07:11:32.0471276Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2025-07-17T07:11:32.0474046Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file 2025-07-17T07:11:32.0630192Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2025-07-17T07:11:32.0632800Z * 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 2025-07-17T07:11:32.0635530Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0 2025-07-17T07:11:32.0638069Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2025-07-17T07:11:32.0640653Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2025-07-17T07:11:32.0643444Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0, line 1066 <- wrt source file 2025-07-17T07:11:32.0646569Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0 2025-07-17T07:11:32.0649508Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 133 <- wrt source file 2025-07-17T07:11:32.0652370Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2025-07-17T07:11:32.0655404Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 269 <- wrt source file 2025-07-17T07:11:32.0658565Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0 2025-07-17T07:11:32.0661198Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 107 <- wrt source file 2025-07-17T07:11:32.0663563Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0 2025-07-17T07:11:32.0666090Z * 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 2025-07-17T07:11:32.0668842Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0 2025-07-17T07:11:32.0671355Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0, line 148 <- wrt source file 2025-07-17T07:11:32.0673854Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0 2025-07-17T07:11:32.0676145Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 163 <- wrt source file 2025-07-17T07:11:32.0678522Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0 2025-07-17T07:11:32.0680965Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0, line 431 <- wrt source file 2025-07-17T07:11:32.0683578Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0 2025-07-17T07:11:32.0686329Z * 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 1457 <- wrt source file 2025-07-17T07:11:32.0689352Z * 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 2025-07-17T07:11:32.0692241Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0, line 3289 <- wrt source file 2025-07-17T07:11:32.0695056Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0 2025-07-17T07:11:32.0697638Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0, line 98 <- wrt source file 2025-07-17T07:11:32.1192750Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0 2025-07-17T07:11:32.1195520Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 237 <- wrt source file 2025-07-17T07:11:32.1277952Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2025-07-17T07:11:32.1280753Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 306 <- wrt source file 2025-07-17T07:11:32.1283559Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2025-07-17T07:11:32.1286350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 540 <- wrt source file 2025-07-17T07:11:32.1440482Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2025-07-17T07:11:32.1442024Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 708 <- wrt source file 2025-07-17T07:11:32.1604253Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2025-07-17T07:11:32.1605998Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0, line 794 <- wrt source file 2025-07-17T07:11:32.1607486Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0 2025-07-17T07:11:32.1608897Z * 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 2025-07-17T07:11:32.1610342Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2025-07-17T07:11:32.1611720Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 175 <- wrt source file 2025-07-17T07:11:32.1680626Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2025-07-17T07:11:32.1681955Z * 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 2025-07-17T07:11:32.1686515Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0 2025-07-17T07:11:32.1687757Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 457 <- wrt source file 2025-07-17T07:11:32.1688987Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0 2025-07-17T07:11:32.1690205Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 171 <- wrt source file 2025-07-17T07:11:32.1722719Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2025-07-17T07:11:32.1724013Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 1008 <- wrt source file 2025-07-17T07:11:32.1771158Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2025-07-17T07:11:32.1772744Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1073 <- wrt source file 2025-07-17T07:11:32.1774226Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2025-07-17T07:11:32.1775622Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1294 <- wrt source file 2025-07-17T07:11:32.1789904Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2025-07-17T07:11:32.1791276Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1360 <- wrt source file 2025-07-17T07:11:32.1793705Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2025-07-17T07:11:32.1795191Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1423 <- wrt source file 2025-07-17T07:11:32.1798040Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2025-07-17T07:11:32.1799397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1468 <- wrt source file 2025-07-17T07:11:32.1800660Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2025-07-17T07:11:32.1801894Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1578 <- wrt source file 2025-07-17T07:11:32.1803448Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2025-07-17T07:11:32.1804946Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 95 <- wrt source file 2025-07-17T07:11:32.1806454Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0 2025-07-17T07:11:32.1807892Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file 2025-07-17T07:11:32.1809236Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2025-07-17T07:11:32.1810596Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 30 <- wrt source file 2025-07-17T07:11:32.1812109Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2025-07-17T07:11:32.1813698Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 24 <- wrt source file 2025-07-17T07:11:32.1815471Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2025-07-17T07:11:32.1817086Z * 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 2025-07-17T07:11:32.1818679Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2025-07-17T07:11:32.1820267Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 67 <- wrt source file 2025-07-17T07:11:32.1821950Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2025-07-17T07:11:32.1823546Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 142 <- wrt source file 2025-07-17T07:11:32.1825142Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2025-07-17T07:11:32.1826583Z * 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 2025-07-17T07:11:32.1832136Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2025-07-17T07:11:32.1833575Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 413 <- wrt source file 2025-07-17T07:11:32.1863245Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2025-07-17T07:11:32.1865718Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 211 <- wrt source file 2025-07-17T07:11:32.1867478Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2025-07-17T07:11:32.1868767Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 283 <- wrt source file 2025-07-17T07:11:32.1870070Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2025-07-17T07:11:32.1871350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 359 <- wrt source file 2025-07-17T07:11:32.1872939Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2025-07-17T07:11:32.1874261Z * 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 2025-07-17T07:11:32.1875627Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2025-07-17T07:11:32.1877245Z * 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 2025-07-17T07:11:32.1878913Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2025-07-17T07:11:32.1880564Z * 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 2025-07-17T07:11:32.1882398Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2025-07-17T07:11:32.1884258Z * 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 2025-07-17T07:11:32.1885786Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2025-07-17T07:11:32.1887193Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 209 <- wrt source file 2025-07-17T07:11:32.1888738Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2025-07-17T07:11:32.1890363Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 296 <- wrt source file 2025-07-17T07:11:32.1892164Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2025-07-17T07:11:32.1893750Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 378 <- wrt source file 2025-07-17T07:11:32.1895396Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2025-07-17T07:11:32.1896912Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 460 <- wrt source file 2025-07-17T07:11:32.1899581Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2025-07-17T07:11:32.1902667Z * 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 2025-07-17T07:11:32.1905551Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2025-07-17T07:11:32.1908321Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 515 <- wrt source file 2025-07-17T07:11:32.1911080Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2025-07-17T07:11:32.1913753Z * 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 2025-07-17T07:11:32.1916494Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2025-07-17T07:11:32.1919241Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1206 <- wrt source file 2025-07-17T07:11:32.1922072Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2025-07-17T07:11:32.1924870Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1273 <- wrt source file 2025-07-17T07:11:32.1927736Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2025-07-17T07:11:32.1930516Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1326 <- wrt source file 2025-07-17T07:11:32.1933353Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2025-07-17T07:11:32.1936172Z * 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 2025-07-17T07:11:32.1938934Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2025-07-17T07:11:32.1941541Z * 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 2025-07-17T07:11:32.1944153Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2025-07-17T07:11:32.1946704Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 635 <- wrt source file 2025-07-17T07:11:32.1949362Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2025-07-17T07:11:32.1952052Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 892 <- wrt source file 2025-07-17T07:11:32.1954903Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2025-07-17T07:11:32.1957702Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1014 <- wrt source file 2025-07-17T07:11:32.1960556Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2025-07-17T07:11:32.1963331Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1140 <- wrt source file 2025-07-17T07:11:32.1966335Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2025-07-17T07:11:32.1969154Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 111 <- wrt source file 2025-07-17T07:11:32.1972200Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2025-07-17T07:11:32.1975081Z * 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 2025-07-17T07:11:32.1978140Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2025-07-17T07:11:32.1981183Z * 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 2025-07-17T07:11:32.1984363Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2025-07-17T07:11:32.1987414Z * 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 2025-07-17T07:11:32.1990482Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2025-07-17T07:11:32.1993284Z * 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 2025-07-17T07:11:32.1995937Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2025-07-17T07:11:32.1999043Z * 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 2025-07-17T07:11:32.2002630Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2025-07-17T07:11:32.2005741Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 24 <- wrt source file 2025-07-17T07:11:32.2008635Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2025-07-17T07:11:32.2011513Z * 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 2025-07-17T07:11:32.2014632Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2025-07-17T07:11:32.2017523Z * 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 2025-07-17T07:11:32.2020231Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2025-07-17T07:11:32.2023001Z * 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 2025-07-17T07:11:32.2025923Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2025-07-17T07:11:32.2028897Z * 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 2025-07-17T07:11:32.2031979Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2025-07-17T07:11:32.2034926Z * 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 2025-07-17T07:11:32.2037894Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2025-07-17T07:11:32.2040891Z * 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 2025-07-17T07:11:32.2044027Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2025-07-17T07:11:32.2046806Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 110 <- wrt source file 2025-07-17T07:11:32.2049416Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0 2025-07-17T07:11:32.2052080Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 132 <- wrt source file 2025-07-17T07:11:32.2054873Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2025-07-17T07:11:32.2057574Z * 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 2025-07-17T07:11:32.2060169Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2025-07-17T07:11:32.2062748Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 288 <- wrt source file 2025-07-17T07:11:32.2065422Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2025-07-17T07:11:32.2068069Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 427 <- wrt source file 2025-07-17T07:11:32.2070810Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2025-07-17T07:11:32.2073587Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 608 <- wrt source file 2025-07-17T07:11:32.2076258Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2025-07-17T07:11:32.2079080Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 668 <- wrt source file 2025-07-17T07:11:32.2082015Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2025-07-17T07:11:32.2085034Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 720 <- wrt source file 2025-07-17T07:11:32.2088223Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2025-07-17T07:11:32.2091134Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 51 <- wrt source file 2025-07-17T07:11:32.2093959Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2025-07-17T07:11:32.2096879Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 130 <- wrt source file 2025-07-17T07:11:32.2099735Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2025-07-17T07:11:32.2102492Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 228 <- wrt source file 2025-07-17T07:11:32.2105240Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2025-07-17T07:11:32.2107892Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 149 <- wrt source file 2025-07-17T07:11:32.2110572Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2025-07-17T07:11:32.2113188Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 521 <- wrt source file 2025-07-17T07:11:32.2115892Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2025-07-17T07:11:32.2118560Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 543 <- wrt source file 2025-07-17T07:11:32.2121318Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2025-07-17T07:11:32.2124002Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 557 <- wrt source file 2025-07-17T07:11:32.2126726Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2025-07-17T07:11:32.2129375Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 579 <- wrt source file 2025-07-17T07:11:32.2132033Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2025-07-17T07:11:32.2134620Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 706 <- wrt source file 2025-07-17T07:11:32.2137326Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2025-07-17T07:11:32.2140248Z * 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 2025-07-17T07:11:32.2143437Z * 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 2025-07-17T07:11:32.2146567Z * 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 2025-07-17T07:11:32.2149765Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0 2025-07-17T07:11:32.2152872Z * 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 2025-07-17T07:11:32.2156062Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 28 <- wrt source file 2025-07-17T07:11:32.2174334Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2025-07-17T07:11:32.2176844Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 82 <- wrt source file 2025-07-17T07:11:32.2179258Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0 2025-07-17T07:11:32.2181674Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 151 <- wrt source file 2025-07-17T07:11:32.2184153Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2025-07-17T07:11:32.2186581Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 187 <- wrt source file 2025-07-17T07:11:32.2189034Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0 2025-07-17T07:11:32.2191645Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 71 <- wrt source file 2025-07-17T07:11:32.2194495Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2025-07-17T07:11:32.2197297Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 115 <- wrt source file 2025-07-17T07:11:32.2200112Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2025-07-17T07:11:32.2202904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 167 <- wrt source file 2025-07-17T07:11:32.2205625Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2025-07-17T07:11:32.2208433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 214 <- wrt source file 2025-07-17T07:11:32.2211424Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2025-07-17T07:11:32.2214340Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 243 <- wrt source file 2025-07-17T07:11:32.2217336Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2025-07-17T07:11:32.2220033Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 485 <- wrt source file 2025-07-17T07:11:32.2222518Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0 2025-07-17T07:11:32.2224847Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file 2025-07-17T07:11:32.2227190Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0 2025-07-17T07:11:32.2229483Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file 2025-07-17T07:11:32.2231822Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0 2025-07-17T07:11:32.2234180Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file 2025-07-17T07:11:32.2236619Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0 2025-07-17T07:11:32.2238990Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 894 <- wrt source file 2025-07-17T07:11:32.2241409Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0 2025-07-17T07:11:32.2243743Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1010 <- wrt source file 2025-07-17T07:11:32.2246079Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0 2025-07-17T07:11:32.2248389Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1109 <- wrt source file 2025-07-17T07:11:32.2250753Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0 2025-07-17T07:11:32.2253035Z * 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 2025-07-17T07:11:32.2255436Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0 2025-07-17T07:11:32.2257796Z * 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 2025-07-17T07:11:32.2260244Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2025-07-17T07:11:32.2262692Z * 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 2025-07-17T07:11:32.2265284Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2025-07-17T07:11:32.2313816Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0, line 182 <- wrt source file 2025-07-17T07:11:32.2316205Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0 2025-07-17T07:11:32.2318535Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0, line 880 <- wrt source file 2025-07-17T07:11:32.2320899Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0 2025-07-17T07:11:32.2323238Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 953 <- wrt source file 2025-07-17T07:11:32.2325707Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0 2025-07-17T07:11:32.2328053Z * 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 2025-07-17T07:11:32.2330421Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0 2025-07-17T07:11:32.2332777Z * 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 2025-07-17T07:11:32.2335252Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0 2025-07-17T07:11:32.2337495Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0, line 85 <- wrt source file 2025-07-17T07:11:32.2339684Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0 2025-07-17T07:11:32.2342021Z * DOCTEST : 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 173 <- wrt source file 2025-07-17T07:11:32.2359179Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2025-07-17T07:11:32.2361614Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2025-07-17T07:11:32.2363893Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0 2025-07-17T07:11:32.2366294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 424 <- wrt source file 2025-07-17T07:11:32.2368879Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2025-07-17T07:11:32.2371552Z * DOCTEST : 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: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0, line 2676 <- wrt source file 2025-07-17T07:11:32.2391516Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0 2025-07-17T07:11:32.2394285Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3143 <- wrt source file 2025-07-17T07:11:32.2397135Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2025-07-17T07:11:32.2399904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3372 <- wrt source file 2025-07-17T07:11:32.2402872Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 2025-07-17T07:11:32.2405652Z * DOCTEST : 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* DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0, line 47 <- wrt source file 2025-07-17T07:11:32.2475383Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/autograd/__init__.py::context:0 2025-07-17T07:11:32.2478193Z * 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 2025-07-17T07:11:32.2481293Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/checkpoint_activation.py::checkpoint:0 2025-07-17T07:11:32.2484128Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0, line 66 <- wrt source file 2025-07-17T07:11:32.2486857Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/contract.py::contract:0 2025-07-17T07:11:32.2489577Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0, line 190 <- wrt source file 2025-07-17T07:11:32.2492359Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_composable/replicate.py::replicate:0 2025-07-17T07:11:32.2495398Z * 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 2025-07-17T07:11:32.2498747Z * 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 2025-07-17T07:11:32.2501931Z * 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 2025-07-17T07:11:32.2505088Z * SKIPPED: 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file 2025-07-17T07:11:32.2522442Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/join.py::Join:0 2025-07-17T07:11:32.2525297Z * 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 2025-07-17T07:11:32.2528754Z * 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 2025-07-17T07:11:32.2531943Z * 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 2025-07-17T07:11:32.2535224Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py::noop_hook:0 2025-07-17T07:11:32.2538409Z * DOCTEST : 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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 1613 <- wrt source file 2025-07-17T07:11:32.2696263Z * 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 2025-07-17T07:11:32.2700095Z * 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 1698 <- wrt source file 2025-07-17T07:11:32.2703940Z * 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 2025-07-17T07:11:32.2707296Z * 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 2025-07-17T07:11:32.2710356Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0 2025-07-17T07:11:32.2713230Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 224 <- wrt source file 2025-07-17T07:11:32.2715826Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0 2025-07-17T07:11:32.2718449Z * 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 2025-07-17T07:11:32.2721189Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0 2025-07-17T07:11:32.2724235Z * DOCTEST : 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2025-07-17T07:11:32.2824136Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/loss.py::loss_parallel:0 2025-07-17T07:11:32.2827009Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0, line 64 <- wrt source file 2025-07-17T07:11:32.2829975Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::ColwiseParallel:0 2025-07-17T07:11:32.2832894Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0, line 198 <- wrt source file 2025-07-17T07:11:32.2835870Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::RowwiseParallel:0 2025-07-17T07:11:32.2838860Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0, line 350 <- wrt source file 2025-07-17T07:11:32.2841890Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0 2025-07-17T07:11:32.2844592Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 30 <- wrt source file 2025-07-17T07:11:32.2847174Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2025-07-17T07:11:32.2849567Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0, line 21 <- wrt source file 2025-07-17T07:11:32.2851882Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0 2025-07-17T07:11:32.2854301Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0, line 31 <- wrt source file 2025-07-17T07:11:32.2856992Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0 2025-07-17T07:11:32.2859545Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0, line 42 <- wrt source file 2025-07-17T07:11:32.2862227Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0 2025-07-17T07:11:32.2864697Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file 2025-07-17T07:11:32.2867112Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0 2025-07-17T07:11:32.2869428Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0, line 18 <- wrt source file 2025-07-17T07:11:32.2871921Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0 2025-07-17T07:11:32.2874400Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0, line 166 <- wrt source file 2025-07-17T07:11:32.2877091Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0 2025-07-17T07:11:32.2879824Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 187 <- wrt source file 2025-07-17T07:11:32.2882670Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0 2025-07-17T07:11:32.2885586Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 35 <- wrt source file 2025-07-17T07:11:32.2888661Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0 2025-07-17T07:11:32.2891409Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 42 <- wrt source file 2025-07-17T07:11:32.2893975Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0 2025-07-17T07:11:32.2896604Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0, line 20 <- wrt source file 2025-07-17T07:11:32.2899268Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0 2025-07-17T07:11:32.2902072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file 2025-07-17T07:11:32.2904929Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0 2025-07-17T07:11:32.2907458Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0, line 24 <- wrt source file 2025-07-17T07:11:32.2909798Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0 2025-07-17T07:11:32.2912436Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0, line 26 <- wrt source file 2025-07-17T07:11:32.2915422Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0 2025-07-17T07:11:32.2918192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0, line 36 <- wrt source file 2025-07-17T07:11:32.2920872Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0 2025-07-17T07:11:32.2923292Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 23 <- wrt source file 2025-07-17T07:11:32.2925714Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0 2025-07-17T07:11:32.2928188Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 24 <- wrt source file 2025-07-17T07:11:32.2930802Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2025-07-17T07:11:32.2933363Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 24 <- wrt source file 2025-07-17T07:11:32.2936075Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2025-07-17T07:11:32.2938687Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0, line 27 <- wrt source file 2025-07-17T07:11:32.2941372Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0 2025-07-17T07:11:32.2943985Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 24 <- wrt source file 2025-07-17T07:11:32.2946726Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2025-07-17T07:11:32.2949390Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 30 <- wrt source file 2025-07-17T07:11:32.2952074Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2025-07-17T07:11:32.2954580Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0, line 20 <- wrt source file 2025-07-17T07:11:32.2957050Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0 2025-07-17T07:11:32.2959567Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 43 <- wrt source file 2025-07-17T07:11:32.2962251Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0 2025-07-17T07:11:32.2964862Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 23 <- wrt source file 2025-07-17T07:11:32.2967416Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0 2025-07-17T07:11:32.2970067Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 28 <- wrt source file 2025-07-17T07:11:32.2973023Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0 2025-07-17T07:11:32.2975779Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 38 <- wrt source file 2025-07-17T07:11:32.2978526Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0 2025-07-17T07:11:32.2981428Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 103 <- wrt source file 2025-07-17T07:11:32.2984482Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0 2025-07-17T07:11:32.2987143Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0, line 22 <- wrt source file 2025-07-17T07:11:32.2989565Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0 2025-07-17T07:11:32.2992214Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 34 <- wrt source file 2025-07-17T07:11:32.2995178Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2025-07-17T07:11:32.2997819Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0, line 20 <- wrt source file 2025-07-17T07:11:32.3000245Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0 2025-07-17T07:11:32.3002621Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0, line 25 <- wrt source file 2025-07-17T07:11:32.3005092Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0 2025-07-17T07:11:32.3007539Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0, line 22 <- wrt source file 2025-07-17T07:11:32.3028428Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0 2025-07-17T07:11:32.3031038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1065 <- wrt source file 2025-07-17T07:11:32.3033745Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0 2025-07-17T07:11:32.3036436Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1177 <- wrt source file 2025-07-17T07:11:32.3039191Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0 2025-07-17T07:11:32.3042107Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1253 <- wrt source file 2025-07-17T07:11:32.3045428Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0 2025-07-17T07:11:32.3048176Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0, line 21 <- wrt source file 2025-07-17T07:11:32.3050660Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0 2025-07-17T07:11:32.3053101Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/utils.py::clamp_probs:0, line 114 <- wrt source file 2025-07-17T07:11:32.3055670Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/utils.py::clamp_probs:0 2025-07-17T07:11:32.3058145Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/von_mises.py::VonMises:0, line 117 <- wrt source file 2025-07-17T07:11:32.3060728Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/von_mises.py::VonMises:0 2025-07-17T07:11:32.3063243Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/weibull.py::Weibull:0, line 22 <- wrt source file 2025-07-17T07:11:32.3065701Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/weibull.py::Weibull:0 2025-07-17T07:11:32.3068109Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0, line 39 <- wrt source file 2025-07-17T07:11:32.3070572Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0 2025-07-17T07:11:32.3073076Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::Dim:0, line 103 <- wrt source file 2025-07-17T07:11:32.3075459Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::Dim:0 2025-07-17T07:11:32.3077962Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 715 <- wrt source file 2025-07-17T07:11:32.3080647Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0 2025-07-17T07:11:32.3083282Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1, line 731 <- wrt source file 2025-07-17T07:11:32.3085962Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1 2025-07-17T07:11:32.3088579Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::AdditionalInputs:0, line 815 <- wrt source file 2025-07-17T07:11:32.3091259Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::AdditionalInputs:0 2025-07-17T07:11:32.3093633Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0, line 102 <- wrt source file 2025-07-17T07:11:32.3095896Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0 2025-07-17T07:11:32.3098257Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1762 <- wrt source file 2025-07-17T07:11:32.3100804Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0 2025-07-17T07:11:32.3103266Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1856 <- wrt source file 2025-07-17T07:11:32.3105802Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0 2025-07-17T07:11:32.3108182Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0, line 48 <- wrt source file 2025-07-17T07:11:32.3110572Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0 2025-07-17T07:11:32.3112936Z * DOCTEST : 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65 <- wrt source file 2025-07-17T07:11:32.3130016Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_consistent:0 2025-07-17T07:11:32.3132381Z * 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 2025-07-17T07:11:32.3134796Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_more_precise:0 2025-07-17T07:11:32.3137555Z * 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 2025-07-17T07:11:32.3140550Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0 2025-07-17T07:11:32.3143361Z * 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 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file 2025-07-17T07:11:32.3363941Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2025-07-17T07:11:32.3366379Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0, line 89 <- wrt source file 2025-07-17T07:11:32.3369016Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0 2025-07-17T07:11:32.3372202Z * 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 2025-07-17T07:11:32.3375849Z * 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 2025-07-17T07:11:32.3378937Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 464 <- wrt source file 2025-07-17T07:11:32.3413809Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0 2025-07-17T07:11:32.3416699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 583 <- wrt source file 2025-07-17T07:11:32.3541253Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2025-07-17T07:11:32.3554085Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2178 <- wrt source file 2025-07-17T07:11:32.3562330Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0 2025-07-17T07:11:32.3564689Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0, line 2482 <- wrt source file 2025-07-17T07:11:32.3570852Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0 2025-07-17T07:11:32.3573444Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0, line 2622 <- wrt source file 2025-07-17T07:11:32.3580901Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0 2025-07-17T07:11:32.3583226Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3055 <- wrt source file 2025-07-17T07:11:32.3595593Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0 2025-07-17T07:11:32.3597836Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3125 <- wrt source file 2025-07-17T07:11:32.3602161Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0 2025-07-17T07:11:32.3604578Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3434 <- wrt source file 2025-07-17T07:11:32.3611235Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0 2025-07-17T07:11:32.3613684Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3499 <- wrt source file 2025-07-17T07:11:32.3618099Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2025-07-17T07:11:32.3620752Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3569 <- wrt source file 2025-07-17T07:11:32.3624219Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2025-07-17T07:11:32.3634595Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0, line 5267 <- wrt source file 2025-07-17T07:11:32.3636806Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0 2025-07-17T07:11:32.3638987Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2025-07-17T07:11:32.3641831Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0 2025-07-17T07:11:32.3644037Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2025-07-17T07:11:32.3646884Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0 2025-07-17T07:11:32.3649071Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2025-07-17T07:11:32.3654743Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0 2025-07-17T07:11:32.3657153Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2025-07-17T07:11:32.3659489Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0 2025-07-17T07:11:32.3661723Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2025-07-17T07:11:32.3690712Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0 2025-07-17T07:11:32.3693760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2025-07-17T07:11:32.3710785Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0 2025-07-17T07:11:32.3713301Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 230 <- wrt source file 2025-07-17T07:11:32.3715694Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0 2025-07-17T07:11:32.3717787Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 257 <- wrt source file 2025-07-17T07:11:32.3719910Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0 2025-07-17T07:11:32.3722057Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 292 <- wrt source file 2025-07-17T07:11:32.3724285Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0 2025-07-17T07:11:32.3726580Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 306 <- wrt source file 2025-07-17T07:11:32.3728858Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0 2025-07-17T07:11:32.3730927Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 323 <- wrt source file 2025-07-17T07:11:32.3732983Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0 2025-07-17T07:11:32.3735033Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 336 <- wrt source file 2025-07-17T07:11:32.3737236Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0 2025-07-17T07:11:32.3739266Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 352 <- wrt source file 2025-07-17T07:11:32.3741328Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0 2025-07-17T07:11:32.3743361Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 374 <- wrt source file 2025-07-17T07:11:32.3745457Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0 2025-07-17T07:11:32.3747611Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 460 <- wrt source file 2025-07-17T07:11:32.3749881Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0 2025-07-17T07:11:32.3752100Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 492 <- wrt source file 2025-07-17T07:11:32.3754356Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0 2025-07-17T07:11:32.3756587Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 543 <- wrt source file 2025-07-17T07:11:32.3758880Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0 2025-07-17T07:11:32.3761110Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 608 <- wrt source file 2025-07-17T07:11:32.3763393Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0 2025-07-17T07:11:32.3765601Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 647 <- wrt source file 2025-07-17T07:11:32.3767809Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0 2025-07-17T07:11:32.3769959Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 700 <- wrt source file 2025-07-17T07:11:32.3772248Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0 2025-07-17T07:11:32.3774527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 119 <- wrt source file 2025-07-17T07:11:32.3777119Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2025-07-17T07:11:32.3779529Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 95 <- wrt source file 2025-07-17T07:11:32.3781920Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0 2025-07-17T07:11:32.3784415Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 72 <- wrt source file 2025-07-17T07:11:32.3787114Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0 2025-07-17T07:11:32.3789511Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 114 <- wrt source file 2025-07-17T07:11:32.3791896Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0 2025-07-17T07:11:32.3794249Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 173 <- wrt source file 2025-07-17T07:11:32.3796651Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0 2025-07-17T07:11:32.3799049Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 229 <- wrt source file 2025-07-17T07:11:32.3801524Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2025-07-17T07:11:32.3803919Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 294 <- wrt source file 2025-07-17T07:11:32.3806324Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0 2025-07-17T07:11:32.3808699Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 322 <- wrt source file 2025-07-17T07:11:32.3811131Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2025-07-17T07:11:32.3813590Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 354 <- wrt source file 2025-07-17T07:11:32.3816189Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2025-07-17T07:11:32.3818604Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 387 <- wrt source file 2025-07-17T07:11:32.3820994Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0 2025-07-17T07:11:32.3823326Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 420 <- wrt source file 2025-07-17T07:11:32.3825701Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0 2025-07-17T07:11:32.3828038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 459 <- wrt source file 2025-07-17T07:11:32.3830477Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0 2025-07-17T07:11:32.3832858Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 504 <- wrt source file 2025-07-17T07:11:32.3835354Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0 2025-07-17T07:11:32.3837728Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 547 <- wrt source file 2025-07-17T07:11:32.3840102Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0 2025-07-17T07:11:32.3842427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 589 <- wrt source file 2025-07-17T07:11:32.3844858Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0 2025-07-17T07:11:32.3847257Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 642 <- wrt source file 2025-07-17T07:11:32.3849654Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0 2025-07-17T07:11:32.3851978Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 682 <- wrt source file 2025-07-17T07:11:32.3854357Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0 2025-07-17T07:11:32.3856739Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 724 <- wrt source file 2025-07-17T07:11:32.3859123Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0 2025-07-17T07:11:32.3861535Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 767 <- wrt source file 2025-07-17T07:11:32.3864014Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2025-07-17T07:11:32.3866469Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 816 <- wrt source file 2025-07-17T07:11:32.3868950Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2025-07-17T07:11:32.3871395Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 852 <- wrt source file 2025-07-17T07:11:32.3874071Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2025-07-17T07:11:32.3876523Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 885 <- wrt source file 2025-07-17T07:11:32.3878991Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0 2025-07-17T07:11:32.3881729Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 928 <- wrt source file 2025-07-17T07:11:32.3884677Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0 2025-07-17T07:11:32.3887682Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1036 <- wrt source file 2025-07-17T07:11:32.3890540Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2025-07-17T07:11:32.3893037Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1501 <- wrt source file 2025-07-17T07:11:32.3895545Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0 2025-07-17T07:11:32.3897953Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1543 <- wrt source file 2025-07-17T07:11:32.3900422Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0 2025-07-17T07:11:32.3902887Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1566 <- wrt source file 2025-07-17T07:11:32.3905489Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2025-07-17T07:11:32.3907998Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1601 <- wrt source file 2025-07-17T07:11:32.3910460Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0 2025-07-17T07:11:32.3912875Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1659 <- wrt source file 2025-07-17T07:11:32.3915329Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0 2025-07-17T07:11:32.3917758Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1700 <- wrt source file 2025-07-17T07:11:32.3920277Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2025-07-17T07:11:32.3922756Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1736 <- wrt source file 2025-07-17T07:11:32.3925315Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2025-07-17T07:11:32.3927791Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 330 <- wrt source file 2025-07-17T07:11:32.3930292Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2025-07-17T07:11:32.3932757Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 441 <- wrt source file 2025-07-17T07:11:32.4073662Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2025-07-17T07:11:32.4076601Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 552 <- wrt source file 2025-07-17T07:11:32.6331595Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2025-07-17T07:11:32.6424676Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2025-07-17T07:11:32.6445005Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2025-07-17T07:11:32.6447603Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 81 <- wrt source file 2025-07-17T07:11:32.6450139Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0 2025-07-17T07:11:32.6453021Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.append:0, line 254 <- wrt source file 2025-07-17T07:11:32.6455825Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.append:0 2025-07-17T07:11:32.6458441Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.insert:0, line 277 <- wrt source file 2025-07-17T07:11:32.6466430Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.insert:0 2025-07-17T07:11:32.6469084Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.extend:0, line 308 <- wrt source file 2025-07-17T07:11:32.6474869Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.extend:0 2025-07-17T07:11:32.6477567Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 337 <- wrt source file 2025-07-17T07:11:32.6480068Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0 2025-07-17T07:11:32.6482537Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 517 <- wrt source file 2025-07-17T07:11:32.6485057Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0 2025-07-17T07:11:32.6487572Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 647 <- wrt source file 2025-07-17T07:11:32.6490153Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0 2025-07-17T07:11:32.6492715Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 799 <- wrt source file 2025-07-17T07:11:32.6495384Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0 2025-07-17T07:11:32.6497968Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2025-07-17T07:11:32.6500596Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2025-07-17T07:11:32.6503165Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 78 <- wrt source file 2025-07-17T07:11:32.6505803Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2025-07-17T07:11:32.6508260Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2025-07-17T07:11:32.6510648Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0 2025-07-17T07:11:32.6512995Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 105 <- wrt source file 2025-07-17T07:11:32.6515432Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2025-07-17T07:11:32.6517810Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 157 <- wrt source file 2025-07-17T07:11:32.6527844Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2025-07-17T07:11:32.6530741Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 202 <- wrt source file 2025-07-17T07:11:32.6598367Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2025-07-17T07:11:32.6601208Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 245 <- wrt source file 2025-07-17T07:11:32.6603874Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2025-07-17T07:11:32.6606449Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 294 <- wrt source file 2025-07-17T07:11:32.6672965Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2025-07-17T07:11:32.6676156Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2025-07-17T07:11:32.6679121Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0 2025-07-17T07:11:32.6681368Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 224 <- wrt source file 2025-07-17T07:11:32.6685552Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0 2025-07-17T07:11:32.6687786Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 389 <- wrt source file 2025-07-17T07:11:32.6701095Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0 2025-07-17T07:11:32.6703563Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2025-07-17T07:11:32.6713999Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2025-07-17T07:11:32.6716666Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2025-07-17T07:11:32.6881016Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2025-07-17T07:11:32.6884096Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2025-07-17T07:11:32.9127564Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2025-07-17T07:11:32.9220859Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file 2025-07-17T07:11:32.9223623Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2025-07-17T07:11:32.9226045Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2025-07-17T07:11:32.9230722Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0 2025-07-17T07:11:32.9233041Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 80 <- wrt source file 2025-07-17T07:11:32.9239769Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0 2025-07-17T07:11:32.9242364Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 179 <- wrt source file 2025-07-17T07:11:32.9261210Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0 2025-07-17T07:11:32.9263509Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2025-07-17T07:11:32.9269569Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0 2025-07-17T07:11:32.9271962Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 212 <- wrt source file 2025-07-17T07:11:32.9297619Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2025-07-17T07:11:32.9300265Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 323 <- wrt source file 2025-07-17T07:11:32.9305470Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2025-07-17T07:11:32.9307927Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 409 <- wrt source file 2025-07-17T07:11:32.9320139Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2025-07-17T07:11:32.9322527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 523 <- wrt source file 2025-07-17T07:11:32.9328167Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2025-07-17T07:11:32.9330433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 602 <- wrt source file 2025-07-17T07:11:32.9334722Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0 2025-07-17T07:11:32.9337071Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 685 <- wrt source file 2025-07-17T07:11:32.9341965Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0 2025-07-17T07:11:32.9344372Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 757 <- wrt source file 2025-07-17T07:11:32.9353775Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2025-07-17T07:11:32.9356294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1, line 805 <- wrt source file 2025-07-17T07:11:32.9359672Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1 2025-07-17T07:11:32.9362242Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 951 <- wrt source file 2025-07-17T07:11:32.9370033Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2025-07-17T07:11:32.9372726Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1277 <- wrt source file 2025-07-17T07:11:32.9378894Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2025-07-17T07:11:32.9381443Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1419 <- wrt source file 2025-07-17T07:11:32.9388913Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2025-07-17T07:11:32.9391486Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1485 <- wrt source file 2025-07-17T07:11:32.9396520Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2025-07-17T07:11:32.9399034Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1565 <- wrt source file 2025-07-17T07:11:32.9404884Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2025-07-17T07:11:32.9416639Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1666 <- wrt source file 2025-07-17T07:11:32.9419333Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2025-07-17T07:11:32.9422064Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 551 <- wrt source file 2025-07-17T07:11:32.9424752Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2025-07-17T07:11:32.9427316Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1037 <- wrt source file 2025-07-17T07:11:32.9432265Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0 2025-07-17T07:11:32.9434658Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0, line 1288 <- wrt source file 2025-07-17T07:11:32.9439121Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0 2025-07-17T07:11:32.9441609Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2224 <- wrt source file 2025-07-17T07:11:32.9444201Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2025-07-17T07:11:32.9446760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2665 <- wrt source file 2025-07-17T07:11:32.9449376Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0 2025-07-17T07:11:32.9452001Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2693 <- wrt source file 2025-07-17T07:11:32.9454742Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2025-07-17T07:11:32.9457409Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2720 <- wrt source file 2025-07-17T07:11:32.9459939Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0 2025-07-17T07:11:32.9462493Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2747 <- wrt source file 2025-07-17T07:11:32.9465137Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2025-07-17T07:11:32.9467758Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2778 <- wrt source file 2025-07-17T07:11:32.9470496Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0 2025-07-17T07:11:32.9473164Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2802 <- wrt source file 2025-07-17T07:11:32.9475694Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0 2025-07-17T07:11:32.9478297Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2840 <- wrt source file 2025-07-17T07:11:32.9480958Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2025-07-17T07:11:32.9483682Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2025-07-17T07:11:32.9490463Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2025-07-17T07:11:32.9493611Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 151 <- wrt source file 2025-07-17T07:11:32.9499612Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2025-07-17T07:11:32.9502576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 262 <- wrt source file 2025-07-17T07:11:32.9507652Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2025-07-17T07:11:32.9510592Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 355 <- wrt source file 2025-07-17T07:11:32.9513354Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2025-07-17T07:11:32.9515842Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 70 <- wrt source file 2025-07-17T07:11:32.9519490Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2025-07-17T07:11:32.9521995Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 122 <- wrt source file 2025-07-17T07:11:32.9540101Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2025-07-17T07:11:32.9542611Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 187 <- wrt source file 2025-07-17T07:11:33.4967464Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2025-07-17T07:11:33.5158677Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 241 <- wrt source file 2025-07-17T07:11:33.5168352Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2025-07-17T07:11:33.5170544Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 294 <- wrt source file 2025-07-17T07:11:33.5176898Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2025-07-17T07:11:33.5179131Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 350 <- wrt source file 2025-07-17T07:11:33.5200518Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2025-07-17T07:11:33.5202943Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 395 <- wrt source file 2025-07-17T07:11:33.5208982Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2025-07-17T07:11:33.5211206Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 439 <- wrt source file 2025-07-17T07:11:33.5216848Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2025-07-17T07:11:33.5219081Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 497 <- wrt source file 2025-07-17T07:11:33.5223695Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2025-07-17T07:11:33.5226016Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 556 <- wrt source file 2025-07-17T07:11:33.5230761Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2025-07-17T07:11:33.5233036Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 600 <- wrt source file 2025-07-17T07:11:33.5238812Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2025-07-17T07:11:33.5241482Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 658 <- wrt source file 2025-07-17T07:11:33.9674331Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2025-07-17T07:11:33.9865245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 692 <- wrt source file 2025-07-17T07:11:33.9875467Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2025-07-17T07:11:33.9877879Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 747 <- wrt source file 2025-07-17T07:11:33.9881685Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2025-07-17T07:11:33.9884099Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 806 <- wrt source file 2025-07-17T07:11:33.9903427Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2025-07-17T07:11:33.9905973Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2025-07-17T07:11:33.9908673Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2025-07-17T07:11:33.9911322Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 93 <- wrt source file 2025-07-17T07:11:33.9914517Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2025-07-17T07:11:33.9917041Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 129 <- wrt source file 2025-07-17T07:11:33.9920842Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2025-07-17T07:11:33.9923250Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 206 <- wrt source file 2025-07-17T07:11:33.9961854Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2025-07-17T07:11:33.9964264Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 289 <- wrt source file 2025-07-17T07:11:34.1788790Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2025-07-17T07:11:34.1825082Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 363 <- wrt source file 2025-07-17T07:11:34.1835507Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2025-07-17T07:11:34.1838143Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 545 <- wrt source file 2025-07-17T07:11:34.2507329Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2025-07-17T07:11:34.2509785Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 636 <- wrt source file 2025-07-17T07:11:34.2516449Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2025-07-17T07:11:34.2518849Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 731 <- wrt source file 2025-07-17T07:11:34.2551807Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2025-07-17T07:11:34.2554201Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 847 <- wrt source file 2025-07-17T07:11:34.4065326Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2025-07-17T07:11:34.4097531Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 937 <- wrt source file 2025-07-17T07:11:34.4139025Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2025-07-17T07:11:34.4141677Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1024 <- wrt source file 2025-07-17T07:11:34.4725583Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2025-07-17T07:11:34.4728143Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1143 <- wrt source file 2025-07-17T07:11:34.4733308Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2025-07-17T07:11:34.4736151Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1194 <- wrt source file 2025-07-17T07:11:34.4778282Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2025-07-17T07:11:34.4781406Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1253 <- wrt source file 2025-07-17T07:11:34.6573396Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2025-07-17T07:11:34.6604606Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1308 <- wrt source file 2025-07-17T07:11:34.6610596Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2025-07-17T07:11:34.6613220Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1342 <- wrt source file 2025-07-17T07:11:34.6619919Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2025-07-17T07:11:34.6622535Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1385 <- wrt source file 2025-07-17T07:11:34.6672576Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2025-07-17T07:11:34.6675393Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1432 <- wrt source file 2025-07-17T07:11:34.6678062Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2025-07-17T07:11:34.6680669Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1463 <- wrt source file 2025-07-17T07:11:34.6684813Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2025-07-17T07:11:34.6687415Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1502 <- wrt source file 2025-07-17T07:11:34.6702114Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2025-07-17T07:11:34.6704474Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 595 <- wrt source file 2025-07-17T07:11:34.6714059Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0 2025-07-17T07:11:34.6716245Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 950 <- wrt source file 2025-07-17T07:11:34.7059047Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0 2025-07-17T07:11:34.7061292Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1285 <- wrt source file 2025-07-17T07:11:34.7075511Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0 2025-07-17T07:11:34.7077775Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1534 <- wrt source file 2025-07-17T07:11:34.7087196Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2025-07-17T07:11:34.7089489Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1656 <- wrt source file 2025-07-17T07:11:34.7097612Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2025-07-17T07:11:34.7099893Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1770 <- wrt source file 2025-07-17T07:11:34.7111326Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2025-07-17T07:11:34.7113771Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 71 <- wrt source file 2025-07-17T07:11:34.7124918Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0 2025-07-17T07:11:34.7127469Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 243 <- wrt source file 2025-07-17T07:11:34.7130432Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2025-07-17T07:11:34.7133222Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 521 <- wrt source file 2025-07-17T07:11:34.7136578Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2025-07-17T07:11:34.7139435Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2025-07-17T07:11:35.4209441Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0 2025-07-17T07:11:35.4224328Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 336 <- wrt source file 2025-07-17T07:11:35.5165742Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2025-07-17T07:11:35.5169592Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 562 <- wrt source file 2025-07-17T07:11:35.7167985Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2025-07-17T07:11:35.7175418Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 686 <- wrt source file 2025-07-17T07:11:35.7433259Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2025-07-17T07:11:35.7474869Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 995 <- wrt source file 2025-07-17T07:11:35.7945202Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2025-07-17T07:11:35.7948137Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2025-07-17T07:11:35.7968861Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2025-07-17T07:11:35.7971495Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 223 <- wrt source file 2025-07-17T07:11:35.7981319Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2025-07-17T07:11:35.7984088Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 273 <- wrt source file 2025-07-17T07:11:35.7989024Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2025-07-17T07:11:35.7991719Z * 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 2025-07-17T07:11:35.7994688Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2025-07-17T07:11:35.7997478Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 642 <- wrt source file 2025-07-17T07:11:35.8000415Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2025-07-17T07:11:35.8003404Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1446 <- wrt source file 2025-07-17T07:11:35.8006508Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2025-07-17T07:11:35.8009705Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1999 <- wrt source file 2025-07-17T07:11:35.8013294Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2025-07-17T07:11:35.8016679Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 2009 <- wrt source file 2025-07-17T07:11:35.8020019Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2025-07-17T07:11:35.8023402Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2044 <- wrt source file 2025-07-17T07:11:35.8026878Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2025-07-17T07:11:35.8029931Z * 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 2025-07-17T07:11:35.8032755Z * 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 2025-07-17T07:11:35.8035269Z * 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 2025-07-17T07:11:35.8037578Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0 2025-07-17T07:11:35.8040035Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2025-07-17T07:11:35.8042713Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2025-07-17T07:11:35.8045324Z * 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 2025-07-17T07:11:35.8048003Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2025-07-17T07:11:35.8050624Z * 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 2025-07-17T07:11:35.8053323Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2025-07-17T07:11:35.8055823Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 849 <- wrt source file 2025-07-17T07:11:35.8058150Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0 2025-07-17T07:11:35.8060620Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 885 <- wrt source file 2025-07-17T07:11:35.8063166Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2025-07-17T07:11:35.8065626Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 928 <- wrt source file 2025-07-17T07:11:35.8068091Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2025-07-17T07:11:35.8070527Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_structured:0, line 968 <- wrt source file 2025-07-17T07:11:35.8073240Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_structured:0 2025-07-17T07:11:35.8075710Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1197 <- wrt source file 2025-07-17T07:11:35.8077986Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0 2025-07-17T07:11:35.8080246Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1225 <- wrt source file 2025-07-17T07:11:35.8082562Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0 2025-07-17T07:11:35.8084855Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 431 <- wrt source file 2025-07-17T07:11:35.8087183Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2025-07-17T07:11:35.8089497Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 492 <- wrt source file 2025-07-17T07:11:35.8091885Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2025-07-17T07:11:35.8094205Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 548 <- wrt source file 2025-07-17T07:11:35.8096654Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2025-07-17T07:11:35.8098990Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 576 <- wrt source file 2025-07-17T07:11:35.8101399Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2025-07-17T07:11:35.8103861Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 314 <- wrt source file 2025-07-17T07:11:35.8106441Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2025-07-17T07:11:35.8109063Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 346 <- wrt source file 2025-07-17T07:11:35.8111802Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2025-07-17T07:11:35.8114397Z * 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 2025-07-17T07:11:35.8116966Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0 2025-07-17T07:11:35.8119528Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 134 <- wrt source file 2025-07-17T07:11:35.8122045Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2025-07-17T07:11:35.8124570Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 156 <- wrt source file 2025-07-17T07:11:35.8127207Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2025-07-17T07:11:35.8129878Z * 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 2025-07-17T07:11:35.8132667Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2025-07-17T07:11:35.8135995Z * 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 2025-07-17T07:11:35.8139512Z * 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 2025-07-17T07:11:35.8142371Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 280 <- wrt source file 2025-07-17T07:11:35.8144763Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2025-07-17T07:11:35.8147213Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 388 <- wrt source file 2025-07-17T07:11:35.8149808Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2025-07-17T07:11:35.8152212Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 491 <- wrt source file 2025-07-17T07:11:35.8154547Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2025-07-17T07:11:35.8156915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 547 <- wrt source file 2025-07-17T07:11:35.8159374Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2025-07-17T07:11:35.8161758Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 608 <- wrt source file 2025-07-17T07:11:35.8164205Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2025-07-17T07:11:35.8166567Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 683 <- wrt source file 2025-07-17T07:11:35.8168981Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2025-07-17T07:11:35.8171396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0, line 773 <- wrt source file 2025-07-17T07:11:35.8174039Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0 2025-07-17T07:11:35.8176580Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 971 <- wrt source file 2025-07-17T07:11:35.8179080Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2025-07-17T07:11:35.8181673Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0, line 1062 <- wrt source file 2025-07-17T07:11:35.8184297Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0 2025-07-17T07:11:35.8186872Z * DOCTEST : 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: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0, line 337 <- wrt source file 2025-07-17T07:11:35.8206180Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0 2025-07-17T07:11:35.8208505Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file 2025-07-17T07:11:35.8210946Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0 2025-07-17T07:11:35.8213706Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 295 <- wrt source file 2025-07-17T07:11:35.8216872Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0 2025-07-17T07:11:35.8219540Z * DOCTEST : 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<- wrt source file 2025-07-17T07:11:35.8284556Z * 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 2025-07-17T07:11:35.8287926Z * 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 2025-07-17T07:11:35.8291386Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2025-07-17T07:11:35.8294215Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0, line 277 <- wrt source file 2025-07-17T07:11:35.8296689Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0 2025-07-17T07:11:35.8299066Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 320 <- wrt source file 2025-07-17T07:11:35.8301514Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2025-07-17T07:11:35.8303920Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 357 <- wrt source file 2025-07-17T07:11:35.8306494Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2025-07-17T07:11:35.8308847Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 387 <- wrt source file 2025-07-17T07:11:35.8311215Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2025-07-17T07:11:35.8313552Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 422 <- wrt source file 2025-07-17T07:11:35.8315962Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2025-07-17T07:11:35.8318359Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 457 <- wrt source file 2025-07-17T07:11:35.8320886Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2025-07-17T07:11:35.8323333Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 494 <- wrt source file 2025-07-17T07:11:35.8325676Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2025-07-17T07:11:35.8328063Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 910 <- wrt source file 2025-07-17T07:11:35.8330578Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2025-07-17T07:11:35.8333052Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 307 <- wrt source file 2025-07-17T07:11:35.8335609Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0 2025-07-17T07:11:35.8338054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0, line 423 <- wrt source file 2025-07-17T07:11:35.8340500Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0 2025-07-17T07:11:35.8342853Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_is_leaf:0, line 1030 <- wrt source file 2025-07-17T07:11:35.8345194Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_is_leaf:0 2025-07-17T07:11:35.8347455Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 1349 <- wrt source file 2025-07-17T07:11:35.8349715Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0 2025-07-17T07:11:35.8352306Z * 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 2025-07-17T07:11:35.8355277Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2025-07-17T07:11:35.8358335Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 375 <- wrt source file 2025-07-17T07:11:35.8361591Z * SKIPPED: 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SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2025-07-17T07:11:35.8380876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 80 <- wrt source file 2025-07-17T07:11:35.8383284Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0 2025-07-17T07:11:35.8385904Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 718 <- wrt source file 2025-07-17T07:11:36.0590229Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2025-07-17T07:11:36.0591652Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file 2025-07-17T07:11:36.0593246Z * SKIPPED: 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2025-07-17T07:11:36.0602382Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0, line 238 <- wrt source file 2025-07-17T07:11:36.0603848Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::WeightedRandomSampler:0 2025-07-17T07:11:36.0605304Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0, line 301 <- wrt source file 2025-07-17T07:11:36.0606677Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py::BatchSampler:0 2025-07-17T07:11:36.0607965Z * 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 2025-07-17T07:11:36.0609333Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2025-07-17T07:11:36.0610909Z * 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 2025-07-17T07:11:36.0612224Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0 2025-07-17T07:11:36.0613515Z * 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 2025-07-17T07:11:36.0615031Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2025-07-17T07:11:36.0616627Z * 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 2025-07-17T07:11:36.0618250Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::IterDataPipe:0 2025-07-17T07:11:36.0619876Z * 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 2025-07-17T07:11:36.0621377Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2025-07-17T07:11:36.0622842Z * 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 2025-07-17T07:11:36.0624412Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2025-07-17T07:11:36.0625967Z * 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 2025-07-17T07:11:36.0627557Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2025-07-17T07:11:36.0629138Z * 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 2025-07-17T07:11:36.0630798Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2025-07-17T07:11:36.0632389Z * 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 2025-07-17T07:11:36.0642976Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2025-07-17T07:11:36.0644664Z * 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 2025-07-17T07:11:36.0646548Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0 2025-07-17T07:11:36.0648289Z * 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 2025-07-17T07:11:36.0650035Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::_ChildDataPipe:0 2025-07-17T07:11:36.0651621Z * 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 2025-07-17T07:11:36.0653411Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::DemultiplexerIterDataPipe:0 2025-07-17T07:11:36.0655397Z * 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 2025-07-17T07:11:36.0657244Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::MultiplexerIterDataPipe:0 2025-07-17T07:11:36.0659073Z * 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 2025-07-17T07:11:36.0660897Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ZipperIterDataPipe:0 2025-07-17T07:11:36.0662563Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0, line 30 <- wrt source file 2025-07-17T07:11:36.0664269Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/filelister.py::FileListerIterDataPipe:0 2025-07-17T07:11:36.0666082Z * 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 2025-07-17T07:11:36.0667735Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py::FileOpenerIterDataPipe:0 2025-07-17T07:11:36.0669612Z * 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 2025-07-17T07:11:36.0671618Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::BatcherIterDataPipe:0 2025-07-17T07:11:36.0673774Z * 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 2025-07-17T07:11:36.0675697Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::UnBatcherIterDataPipe:0 2025-07-17T07:11:36.0677597Z * 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 2025-07-17T07:11:36.0679522Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py::GrouperIterDataPipe:0 2025-07-17T07:11:36.0681430Z * 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 2025-07-17T07:11:36.0683185Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/selecting.py::FilterIterDataPipe:0 2025-07-17T07:11:36.0684818Z * 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 2025-07-17T07:11:36.0686575Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2025-07-17T07:11:36.0688317Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 29 <- wrt source file 2025-07-17T07:11:36.0690009Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2025-07-17T07:11:36.0691806Z * 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 2025-07-17T07:11:36.0694326Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2025-07-17T07:11:36.0697064Z * 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 2025-07-17T07:11:36.0699875Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2025-07-17T07:11:36.0702609Z * 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 2025-07-17T07:11:36.0704310Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2025-07-17T07:11:36.0706600Z * 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 2025-07-17T07:11:36.0709096Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2025-07-17T07:11:36.0711820Z * 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 2025-07-17T07:11:36.0714526Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2025-07-17T07:11:36.0716121Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 29 <- wrt source file 2025-07-17T07:11:36.0718396Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2025-07-17T07:11:36.0720896Z * 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 2025-07-17T07:11:36.0723374Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2025-07-17T07:11:36.0725762Z * 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 2025-07-17T07:11:36.0728269Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2025-07-17T07:11:36.0730080Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 439 <- wrt source file 2025-07-17T07:11:36.0731923Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2025-07-17T07:11:36.0734187Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 535 <- wrt source file 2025-07-17T07:11:36.0736240Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2025-07-17T07:11:36.0761800Z * 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 2025-07-17T07:11:36.0764324Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2025-07-17T07:11:36.0766520Z * 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 2025-07-17T07:11:36.0768927Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2025-07-17T07:11:36.0771401Z * 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 2025-07-17T07:11:36.0773777Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2025-07-17T07:11:36.0775970Z * 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 2025-07-17T07:11:36.0778283Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2025-07-17T07:11:36.0780772Z * 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 2025-07-17T07:11:36.0783251Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2025-07-17T07:11:36.0785605Z * 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 2025-07-17T07:11:36.0788160Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2025-07-17T07:11:36.0790567Z * 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 2025-07-17T07:11:36.0792974Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2025-07-17T07:11:36.0795157Z * 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 2025-07-17T07:11:36.0797547Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2025-07-17T07:11:36.0799562Z * 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 2025-07-17T07:11:36.0801132Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2025-07-17T07:11:36.0802582Z * 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 2025-07-17T07:11:36.0804283Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2025-07-17T07:11:36.0805891Z * 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 2025-07-17T07:11:36.0807541Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2025-07-17T07:11:36.0809221Z * 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 2025-07-17T07:11:36.0810955Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2025-07-17T07:11:36.0812555Z * 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 2025-07-17T07:11:36.0814400Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2025-07-17T07:11:36.0816149Z * 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 2025-07-17T07:11:36.0817824Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2025-07-17T07:11:36.0819405Z * 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 2025-07-17T07:11:36.0820956Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2025-07-17T07:11:36.0822577Z * 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 2025-07-17T07:11:36.0824030Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2025-07-17T07:11:36.0824778Z ============ 2025-07-17T07:11:36.0825037Z Finished doctests 2025-07-17T07:11:36.0825258Z 342 / 719 passed 2025-07-17T07:11:36.0825494Z  2025-07-17T07:11:36.0825797Z === Found 136 parse-time warnings === 2025-07-17T07:11:36.0826219Z --- Parse Warning: 1 / 136 --- 2025-07-17T07:11:36.0827320Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1493. 2025-07-17T07:11:36.0828568Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.0829038Z 2025-07-17T07:11:36.0829257Z dim_order(ambiguity_check=False) -> tuple 2025-07-17T07:11:36.0829563Z 2025-07-17T07:11:36.0829912Z Returns the uniquely determined tuple of int describing the dim order or 2025-07-17T07:11:36.0830379Z physical layout of :attr:`self`. 2025-07-17T07:11:36.0830657Z 2025-07-17T07:11:36.0831019Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-07-17T07:11:36.0831558Z starting from the outermost to the innermost dimension. 2025-07-17T07:11:36.0831908Z 2025-07-17T07:11:36.0832205Z Note that the dim order may not always be uniquely determined. 2025-07-17T07:11:36.0832909Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-07-17T07:11:36.0833815Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-07-17T07:11:36.0834597Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-07-17T07:11:36.0835309Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-07-17T07:11:36.0835879Z Otherwise, it will raise TypeError. 2025-07-17T07:11:36.0836173Z 2025-07-17T07:11:36.0836368Z Args: 2025-07-17T07:11:36.0836808Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-07-17T07:11:36.0837325Z 2025-07-17T07:11:36.0837538Z Examples:: 2025-07-17T07:11:36.0837755Z 2025-07-17T07:11:36.0837973Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-07-17T07:11:36.0838290Z (0, 1, 2, 3) 2025-07-17T07:11:36.0838597Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-07-17T07:11:36.0838958Z (0, 2, 1, 3) 2025-07-17T07:11:36.0839308Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-07-17T07:11:36.0839769Z (0, 2, 3, 1) 2025-07-17T07:11:36.0840041Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-07-17T07:11:36.0840349Z (0, 1, 2, 3) 2025-07-17T07:11:36.0840595Z >>> try: 2025-07-17T07:11:36.0840918Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-07-17T07:11:36.0841321Z ... except RuntimeError as e: 2025-07-17T07:11:36.0841629Z ... print(e) 2025-07-17T07:11:36.0842114Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-07-17T07:11:36.0842656Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-07-17T07:11:36.0843102Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-07-17T07:11:36.0843574Z ... ) # It can be mapped to contiguous format 2025-07-17T07:11:36.0843908Z (0, 1, 2, 3) 2025-07-17T07:11:36.0844157Z >>> try: 2025-07-17T07:11:36.0844484Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-07-17T07:11:36.0844895Z ... except TypeError as e: 2025-07-17T07:11:36.0845258Z ... print(e) 2025-07-17T07:11:36.0845797Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-07-17T07:11:36.0846242Z 2025-07-17T07:11:36.0846435Z .. warning:: 2025-07-17T07:11:36.0846773Z The dim_order tensor API is experimental and subject to change. 2025-07-17T07:11:36.0847172Z 2025-07-17T07:11:36.0847528Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.0847984Z 2025-07-17T07:11:36.0848199Z warnings.warn(msg) 2025-07-17T07:11:36.0848565Z 2025-07-17T07:11:36.0848885Z --- Parse Warning: 2 / 136 --- 2025-07-17T07:11:36.0849989Z /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=446. 2025-07-17T07:11:36.0851219Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.0851844Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-07-17T07:11:36.0852294Z 2025-07-17T07:11:36.0852589Z This is helpful when you want to visualize data over some 2025-07-17T07:11:36.0853046Z range of inputs. See below for a plotting example. 2025-07-17T07:11:36.0853388Z 2025-07-17T07:11:36.0853667Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-07-17T07:11:36.0854144Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-07-17T07:11:36.0854634Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-07-17T07:11:36.0855110Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-07-17T07:11:36.0855647Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-07-17T07:11:36.0856069Z to the result shape. 2025-07-17T07:11:36.0856334Z 2025-07-17T07:11:36.0856545Z .. note:: 2025-07-17T07:11:36.0856866Z 0D inputs are treated equivalently to 1D inputs of a 2025-07-17T07:11:36.0857235Z single element. 2025-07-17T07:11:36.0857508Z 2025-07-17T07:11:36.0857715Z .. warning:: 2025-07-17T07:11:36.0858051Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-07-17T07:11:36.0858523Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-07-17T07:11:36.0858888Z 2025-07-17T07:11:36.0859145Z In the future `torch.meshgrid` will transition to 2025-07-17T07:11:36.0859538Z `indexing='xy'` as the default. 2025-07-17T07:11:36.0859850Z 2025-07-17T07:11:36.0860147Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-07-17T07:11:36.0860631Z this issue with the goal of migrating to NumPy's behavior. 2025-07-17T07:11:36.0861079Z 2025-07-17T07:11:36.0861293Z .. seealso:: 2025-07-17T07:11:36.0861528Z 2025-07-17T07:11:36.0861828Z :func:`torch.cartesian_prod` has the same effect but it 2025-07-17T07:11:36.0862263Z collects the data in a tensor of vectors. 2025-07-17T07:11:36.0862586Z 2025-07-17T07:11:36.0862792Z Args: 2025-07-17T07:11:36.0863202Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-07-17T07:11:36.0863754Z treated as tensors of size :math:`(1,)` automatically 2025-07-17T07:11:36.0864125Z 2025-07-17T07:11:36.0864422Z indexing: (str, optional): the indexing mode, either "xy" 2025-07-17T07:11:36.0864896Z or "ij", defaults to "ij". See warning for future changes. 2025-07-17T07:11:36.0865260Z 2025-07-17T07:11:36.0865563Z If "xy" is selected, the first dimension corresponds 2025-07-17T07:11:36.0866011Z to the cardinality of the second input and the second 2025-07-17T07:11:36.0866459Z dimension corresponds to the cardinality of the first 2025-07-17T07:11:36.0866921Z input. 2025-07-17T07:11:36.0867176Z 2025-07-17T07:11:36.0867438Z If "ij" is selected, the dimensions are in the same 2025-07-17T07:11:36.0867850Z order as the cardinality of the inputs. 2025-07-17T07:11:36.0868186Z 2025-07-17T07:11:36.0868378Z Returns: 2025-07-17T07:11:36.0868700Z seq (sequence of Tensors): If the input has :math:`N` 2025-07-17T07:11:36.0869143Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-07-17T07:11:36.0869601Z output will also have :math:`N` tensors, where each tensor 2025-07-17T07:11:36.0870019Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-07-17T07:11:36.0870339Z 2025-07-17T07:11:36.0870550Z Example:: 2025-07-17T07:11:36.0870774Z 2025-07-17T07:11:36.0871007Z >>> x = torch.tensor([1, 2, 3]) 2025-07-17T07:11:36.0871350Z >>> y = torch.tensor([4, 5, 6]) 2025-07-17T07:11:36.0871648Z 2025-07-17T07:11:36.0872141Z Observe the element-wise pairings across the grid, (1, 4), 2025-07-17T07:11:36.0872597Z (1, 5), ..., (3, 6). This is the same thing as the 2025-07-17T07:11:36.0872955Z cartesian product. 2025-07-17T07:11:36.0873312Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-07-17T07:11:36.0873690Z >>> grid_x 2025-07-17T07:11:36.0873957Z tensor([[1, 1, 1], 2025-07-17T07:11:36.0874229Z [2, 2, 2], 2025-07-17T07:11:36.0874508Z [3, 3, 3]]) 2025-07-17T07:11:36.0874792Z >>> grid_y 2025-07-17T07:11:36.0875043Z tensor([[4, 5, 6], 2025-07-17T07:11:36.0875324Z [4, 5, 6], 2025-07-17T07:11:36.0875606Z [4, 5, 6]]) 2025-07-17T07:11:36.0875869Z 2025-07-17T07:11:36.0876159Z This correspondence can be seen when these grids are 2025-07-17T07:11:36.0876550Z stacked properly. 2025-07-17T07:11:36.0876935Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-07-17T07:11:36.0877398Z ... torch.cartesian_prod(x, y)) 2025-07-17T07:11:36.0877734Z True 2025-07-17T07:11:36.0877967Z 2025-07-17T07:11:36.0878248Z `torch.meshgrid` is commonly used to produce a grid for 2025-07-17T07:11:36.0878640Z plotting. 2025-07-17T07:11:36.0878941Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-07-17T07:11:36.0879310Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-07-17T07:11:36.0879688Z >>> import matplotlib.pyplot as plt 2025-07-17T07:11:36.0880142Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-07-17T07:11:36.0880498Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-07-17T07:11:36.0880888Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-07-17T07:11:36.0881276Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-07-17T07:11:36.0881648Z >>> ax = plt.axes(projection='3d') 2025-07-17T07:11:36.0882027Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-07-17T07:11:36.0882395Z >>> plt.show() 2025-07-17T07:11:36.0882660Z 2025-07-17T07:11:36.0882890Z .. image:: ../_static/img/meshgrid.png 2025-07-17T07:11:36.0883218Z :width: 512 2025-07-17T07:11:36.0883471Z 2025-07-17T07:11:36.0883660Z 2025-07-17T07:11:36.0884042Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.0884494Z 2025-07-17T07:11:36.0884739Z warnings.warn(msg) 2025-07-17T07:11:36.0884996Z 2025-07-17T07:11:36.0885338Z --- Parse Warning: 3 / 136 --- 2025-07-17T07:11:36.0886517Z /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=842. 2025-07-17T07:11:36.0887755Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.0888525Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-07-17T07:11:36.0889118Z 2025-07-17T07:11:36.0889375Z Returns the unique elements of the input tensor. 2025-07-17T07:11:36.0889730Z 2025-07-17T07:11:36.0890153Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-07-17T07:11:36.0890786Z this function also eliminates non-consecutive duplicate values. 2025-07-17T07:11:36.0891198Z 2025-07-17T07:11:36.0891547Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-07-17T07:11:36.0892199Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-07-17T07:11:36.0892891Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-07-17T07:11:36.0893478Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-07-17T07:11:36.0893862Z 2025-07-17T07:11:36.0894066Z Args: 2025-07-17T07:11:36.0894306Z input (Tensor): the input tensor 2025-07-17T07:11:36.0894748Z sorted (bool): Whether to sort the unique elements in ascending order 2025-07-17T07:11:36.0895272Z before returning as output. 2025-07-17T07:11:36.0895707Z return_inverse (bool): Whether to also return the indices for where 2025-07-17T07:11:36.0896280Z elements in the original input ended up in the returned unique list. 2025-07-17T07:11:36.0896863Z return_counts (bool): Whether to also return the counts for each unique 2025-07-17T07:11:36.0897314Z element. 2025-07-17T07:11:36.0897685Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-07-17T07:11:36.0898232Z unique of the flattened input is returned. Otherwise, each of the 2025-07-17T07:11:36.0898773Z tensors indexed by the given dimension is treated as one of the 2025-07-17T07:11:36.0899303Z elements to apply the unique operation upon. See examples for more 2025-07-17T07:11:36.0899752Z details. Default: ``None`` 2025-07-17T07:11:36.0900053Z 2025-07-17T07:11:36.0900254Z Returns: 2025-07-17T07:11:36.0900676Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-07-17T07:11:36.0901172Z 2025-07-17T07:11:36.0901491Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-07-17T07:11:36.0901989Z - **inverse_indices** (*Tensor*): (optional) if 2025-07-17T07:11:36.0902441Z :attr:`return_inverse` is True, there will be an additional 2025-07-17T07:11:36.0902965Z returned tensor (same shape as input) representing the indices 2025-07-17T07:11:36.0903499Z for where elements in the original input map to in the output; 2025-07-17T07:11:36.0903997Z otherwise, this function will only return a single tensor. 2025-07-17T07:11:36.0904429Z - **counts** (*Tensor*): (optional) if 2025-07-17T07:11:36.0904844Z :attr:`return_counts` is True, there will be an additional 2025-07-17T07:11:36.0905323Z returned tensor (same shape as output or output.size(dim), 2025-07-17T07:11:36.0905825Z if dim was specified) representing the number of occurrences 2025-07-17T07:11:36.0906296Z for each unique value or tensor. 2025-07-17T07:11:36.0906606Z 2025-07-17T07:11:36.0906817Z Example:: 2025-07-17T07:11:36.0907045Z 2025-07-17T07:11:36.0907432Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-07-17T07:11:36.0907852Z >>> output 2025-07-17T07:11:36.0908103Z tensor([1, 2, 3]) 2025-07-17T07:11:36.0908370Z 2025-07-17T07:11:36.0908608Z >>> output, inverse_indices = torch.unique( 2025-07-17T07:11:36.0909098Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-07-17T07:11:36.0909556Z >>> output 2025-07-17T07:11:36.0909796Z tensor([1, 2, 3]) 2025-07-17T07:11:36.0910072Z >>> inverse_indices 2025-07-17T07:11:36.0910356Z tensor([0, 2, 1, 2]) 2025-07-17T07:11:36.0910614Z 2025-07-17T07:11:36.0910864Z >>> output, inverse_indices = torch.unique( 2025-07-17T07:11:36.0911356Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-07-17T07:11:36.0911811Z >>> output 2025-07-17T07:11:36.0912048Z tensor([1, 2, 3]) 2025-07-17T07:11:36.0912328Z >>> inverse_indices 2025-07-17T07:11:36.0912607Z tensor([[0, 2], 2025-07-17T07:11:36.0912854Z [1, 2]]) 2025-07-17T07:11:36.0913107Z 2025-07-17T07:11:36.0913322Z >>> a = torch.tensor([ 2025-07-17T07:11:36.0913593Z ... [ 2025-07-17T07:11:36.0913837Z ... [1, 1, 0, 0], 2025-07-17T07:11:36.0914129Z ... [1, 1, 0, 0], 2025-07-17T07:11:36.0914401Z ... [0, 0, 1, 1], 2025-07-17T07:11:36.0914679Z ... ], 2025-07-17T07:11:36.0914913Z ... [ 2025-07-17T07:11:36.0915141Z ... [0, 0, 1, 1], 2025-07-17T07:11:36.0915426Z ... [0, 0, 1, 1], 2025-07-17T07:11:36.0915711Z ... [1, 1, 1, 1], 2025-07-17T07:11:36.0915979Z ... ], 2025-07-17T07:11:36.0916216Z ... [ 2025-07-17T07:11:36.0916462Z ... [1, 1, 0, 0], 2025-07-17T07:11:36.0916752Z ... [1, 1, 0, 0], 2025-07-17T07:11:36.0917030Z ... [0, 0, 1, 1], 2025-07-17T07:11:36.0917313Z ... ], 2025-07-17T07:11:36.0917554Z ... ]) 2025-07-17T07:11:36.0917766Z 2025-07-17T07:11:36.0918102Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-07-17T07:11:36.0918653Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-07-17T07:11:36.0919113Z >>> # each other, so one of them will be removed. 2025-07-17T07:11:36.0919476Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-07-17T07:11:36.0919785Z tensor(True) 2025-07-17T07:11:36.0920078Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-07-17T07:11:36.0920407Z >>> a_unique_dim0 2025-07-17T07:11:36.0920731Z tensor([[[0, 0, 1, 1], 2025-07-17T07:11:36.0921018Z [0, 0, 1, 1], 2025-07-17T07:11:36.0921291Z [1, 1, 1, 1]], 2025-07-17T07:11:36.0921584Z [[1, 1, 0, 0], 2025-07-17T07:11:36.0921867Z [1, 1, 0, 0], 2025-07-17T07:11:36.0922135Z [0, 0, 1, 1]]]) 2025-07-17T07:11:36.0922420Z 2025-07-17T07:11:36.0922754Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-07-17T07:11:36.0923181Z >>> # `a_unique_dim0`: 2025-07-17T07:11:36.0923499Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-07-17T07:11:36.0923829Z tensor(True) 2025-07-17T07:11:36.0924101Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-07-17T07:11:36.0924431Z tensor(True) 2025-07-17T07:11:36.0924672Z 2025-07-17T07:11:36.0924995Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-07-17T07:11:36.0925532Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-07-17T07:11:36.0925948Z >>> # them will be removed. 2025-07-17T07:11:36.0926339Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-07-17T07:11:36.0926641Z tensor(True) 2025-07-17T07:11:36.0926914Z >>> torch.unique(a, dim=1) 2025-07-17T07:11:36.0927228Z tensor([[[0, 0, 1, 1], 2025-07-17T07:11:36.0927500Z [1, 1, 0, 0]], 2025-07-17T07:11:36.0927789Z [[1, 1, 1, 1], 2025-07-17T07:11:36.0928076Z [0, 0, 1, 1]], 2025-07-17T07:11:36.0928363Z [[0, 0, 1, 1], 2025-07-17T07:11:36.0928631Z [1, 1, 0, 0]]]) 2025-07-17T07:11:36.0928906Z 2025-07-17T07:11:36.0929231Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-07-17T07:11:36.0929723Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-07-17T07:11:36.0930187Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-07-17T07:11:36.0930602Z >>> # sub-tensors will be removed. 2025-07-17T07:11:36.0930926Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-07-17T07:11:36.0931237Z tensor(True) 2025-07-17T07:11:36.0931507Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-07-17T07:11:36.0931803Z tensor(True) 2025-07-17T07:11:36.0932076Z >>> torch.unique(a, dim=2) 2025-07-17T07:11:36.0932382Z tensor([[[0, 1], 2025-07-17T07:11:36.0932649Z [0, 1], 2025-07-17T07:11:36.0932902Z [1, 0]], 2025-07-17T07:11:36.0933173Z [[1, 0], 2025-07-17T07:11:36.0933435Z [1, 0], 2025-07-17T07:11:36.0933685Z [1, 1]], 2025-07-17T07:11:36.0933948Z [[0, 1], 2025-07-17T07:11:36.0934205Z [0, 1], 2025-07-17T07:11:36.0934456Z [1, 0]]]) 2025-07-17T07:11:36.0934716Z 2025-07-17T07:11:36.0935093Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.0935619Z 2025-07-17T07:11:36.0935829Z warnings.warn(msg) 2025-07-17T07:11:36.0936070Z 2025-07-17T07:11:36.0938021Z --- Parse Warning: 4 / 136 --- 2025-07-17T07:11:36.0939050Z /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. 2025-07-17T07:11:36.0940199Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.0940659Z 2025-07-17T07:11:36.0940918Z Load a model from a github repo or a local directory. 2025-07-17T07:11:36.0941264Z 2025-07-17T07:11:36.0941602Z Note: Loading a model is the typical use case, but this can also be used to 2025-07-17T07:11:36.0942222Z for loading other objects such as tokenizers, loss functions, etc. 2025-07-17T07:11:36.0942618Z 2025-07-17T07:11:36.0942886Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-07-17T07:11:36.0943348Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-07-17T07:11:36.0943723Z ref (a tag or a branch). 2025-07-17T07:11:36.0943986Z 2025-07-17T07:11:36.0944264Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-07-17T07:11:36.0944643Z path to a local directory. 2025-07-17T07:11:36.0944911Z 2025-07-17T07:11:36.0945102Z Args: 2025-07-17T07:11:36.0945341Z repo_or_dir (str): If ``source`` is 'github', 2025-07-17T07:11:36.0945871Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-07-17T07:11:36.0946586Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-07-17T07:11:36.0947307Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-07-17T07:11:36.0947883Z If ``source`` is 'local' then it should be a path to a local directory. 2025-07-17T07:11:36.0948456Z model (str): the name of a callable (entrypoint) defined in the 2025-07-17T07:11:36.0948872Z repo/dir's ``hubconf.py``. 2025-07-17T07:11:36.0949265Z *args (optional): the corresponding args for callable ``model``. 2025-07-17T07:11:36.0949754Z source (str, optional): 'github' or 'local'. Specifies how 2025-07-17T07:11:36.0950217Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-07-17T07:11:36.0950712Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-07-17T07:11:36.0951264Z This parameter was introduced in v1.12 and helps ensuring that users 2025-07-17T07:11:36.0951737Z only run code from repos that they trust. 2025-07-17T07:11:36.0952063Z 2025-07-17T07:11:36.0952359Z - If ``False``, a prompt will ask the user whether the repo should 2025-07-17T07:11:36.0952757Z be trusted. 2025-07-17T07:11:36.0953109Z - If ``True``, the repo will be added to the trusted list and loaded 2025-07-17T07:11:36.0953541Z without requiring explicit confirmation. 2025-07-17T07:11:36.0953951Z - If ``"check"``, the repo will be checked against the list of 2025-07-17T07:11:36.0954437Z trusted repos in the cache. If it is not present in that list, the 2025-07-17T07:11:36.0954958Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-07-17T07:11:36.0955444Z - If ``None``: this will raise a warning, inviting the user to set 2025-07-17T07:11:36.0955926Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-07-17T07:11:36.0956437Z is only present for backward compatibility and will be removed in 2025-07-17T07:11:36.0956838Z v2.0. 2025-07-17T07:11:36.0957051Z 2025-07-17T07:11:36.0957369Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-07-17T07:11:36.0957911Z force_reload (bool, optional): whether to force a fresh download of 2025-07-17T07:11:36.0958425Z the github repo unconditionally. Does not have any effect if 2025-07-17T07:11:36.0958858Z ``source = 'local'``. Default is ``False``. 2025-07-17T07:11:36.0959296Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-07-17T07:11:36.0959817Z local caches. Note that the message about first download cannot be 2025-07-17T07:11:36.0960306Z muted. Does not have any effect if ``source = 'local'``. 2025-07-17T07:11:36.0960683Z Default is ``True``. 2025-07-17T07:11:36.0961158Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-07-17T07:11:36.0961847Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-07-17T07:11:36.0962549Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-07-17T07:11:36.0963128Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-07-17T07:11:36.0963627Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-07-17T07:11:36.0964046Z 2025-07-17T07:11:36.0964236Z Returns: 2025-07-17T07:11:36.0964535Z The output of the ``model`` callable when called with the given 2025-07-17T07:11:36.0964938Z ``*args`` and ``**kwargs``. 2025-07-17T07:11:36.0965211Z 2025-07-17T07:11:36.0965396Z Example: 2025-07-17T07:11:36.0965638Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T07:11:36.0965970Z >>> # from a github repo 2025-07-17T07:11:36.0966255Z >>> repo = "pytorch/vision" 2025-07-17T07:11:36.0966539Z >>> model = torch.hub.load( 2025-07-17T07:11:36.0966945Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-07-17T07:11:36.0967320Z ... ) 2025-07-17T07:11:36.0967527Z >>> # from a local directory 2025-07-17T07:11:36.0967900Z >>> path = "/some/local/path/pytorch/vision" 2025-07-17T07:11:36.0968231Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.0968631Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-07-17T07:11:36.0969065Z 2025-07-17T07:11:36.0969419Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.0969859Z 2025-07-17T07:11:36.0970046Z warnings.warn(msg) 2025-07-17T07:11:36.0970281Z 2025-07-17T07:11:36.0970580Z --- Parse Warning: 5 / 136 --- 2025-07-17T07:11:36.0971612Z /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. 2025-07-17T07:11:36.0972963Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.0973414Z 2025-07-17T07:11:36.0973684Z Load a model from a local directory with a ``hubconf.py``. 2025-07-17T07:11:36.0974040Z 2025-07-17T07:11:36.0974216Z Args: 2025-07-17T07:11:36.0974510Z hubconf_dir (str): path to a local directory that contains a 2025-07-17T07:11:36.0974889Z ``hubconf.py``. 2025-07-17T07:11:36.0975309Z model (str): name of an entrypoint defined in the directory's 2025-07-17T07:11:36.0975694Z ``hubconf.py``. 2025-07-17T07:11:36.0976046Z *args (optional): the corresponding args for callable ``model``. 2025-07-17T07:11:36.0976568Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-07-17T07:11:36.0976985Z 2025-07-17T07:11:36.0977159Z Returns: 2025-07-17T07:11:36.0977445Z a single model with corresponding pretrained weights. 2025-07-17T07:11:36.0977795Z 2025-07-17T07:11:36.0977973Z Example: 2025-07-17T07:11:36.0978211Z >>> # xdoctest: +SKIP("stub local path") 2025-07-17T07:11:36.0978560Z >>> path = "/some/local/path/pytorch/vision" 2025-07-17T07:11:36.0978885Z >>> model = _load_local( 2025-07-17T07:11:36.0979139Z ... path, 2025-07-17T07:11:36.0979362Z ... "resnet50", 2025-07-17T07:11:36.0979658Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-07-17T07:11:36.0979972Z ... ) 2025-07-17T07:11:36.0980165Z 2025-07-17T07:11:36.0980514Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.0980942Z 2025-07-17T07:11:36.0981133Z warnings.warn(msg) 2025-07-17T07:11:36.0981364Z 2025-07-17T07:11:36.0981653Z --- Parse Warning: 6 / 136 --- 2025-07-17T07:11:36.0982739Z /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. 2025-07-17T07:11:36.0984039Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.0984568Z Download object at the given URL to a local path. 2025-07-17T07:11:36.0984890Z 2025-07-17T07:11:36.0985073Z Args: 2025-07-17T07:11:36.0985314Z url (str): URL of the object to download 2025-07-17T07:11:36.0985770Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-07-17T07:11:36.0986445Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-07-17T07:11:36.0986983Z Default: None 2025-07-17T07:11:36.0987400Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-07-17T07:11:36.0987845Z Default: True 2025-07-17T07:11:36.0988085Z 2025-07-17T07:11:36.0988307Z Example: 2025-07-17T07:11:36.0988565Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T07:11:36.0988922Z >>> # xdoctest: +REQUIRES(POSIX) 2025-07-17T07:11:36.0989268Z >>> torch.hub.download_url_to_file( 2025-07-17T07:11:36.0989797Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-07-17T07:11:36.0990254Z ... "/tmp/temporary_file", 2025-07-17T07:11:36.0990560Z ... ) 2025-07-17T07:11:36.0990772Z 2025-07-17T07:11:36.0990972Z 2025-07-17T07:11:36.0991347Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.0991801Z 2025-07-17T07:11:36.0992003Z warnings.warn(msg) 2025-07-17T07:11:36.0992258Z 2025-07-17T07:11:36.0992561Z --- Parse Warning: 7 / 136 --- 2025-07-17T07:11:36.0993672Z /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. 2025-07-17T07:11:36.0995008Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.0995561Z Loads the Torch serialized object at the given URL. 2025-07-17T07:11:36.0995919Z 2025-07-17T07:11:36.0996212Z If downloaded file is a zip file, it will be automatically 2025-07-17T07:11:36.0996610Z decompressed. 2025-07-17T07:11:36.0996855Z 2025-07-17T07:11:36.0997179Z If the object is already present in `model_dir`, it's deserialized and 2025-07-17T07:11:36.0997613Z returned. 2025-07-17T07:11:36.0997970Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-07-17T07:11:36.0998505Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-07-17T07:11:36.0998894Z 2025-07-17T07:11:36.0999094Z Args: 2025-07-17T07:11:36.0999358Z url (str): URL of the object to download 2025-07-17T07:11:36.0999790Z model_dir (str, optional): directory in which to save the object 2025-07-17T07:11:36.1000471Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-07-17T07:11:36.1001208Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-07-17T07:11:36.1001680Z Default: True 2025-07-17T07:11:36.1002190Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-07-17T07:11:36.1002865Z ``filename-.ext`` where ```` is the first eight or more 2025-07-17T07:11:36.1003442Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-07-17T07:11:36.1003974Z ensure unique names and to verify the contents of the file. 2025-07-17T07:11:36.1004384Z Default: False 2025-07-17T07:11:36.1004909Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-07-17T07:11:36.1005730Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-07-17T07:11:36.1006435Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-07-17T07:11:36.1006902Z 2025-07-17T07:11:36.1007106Z Example: 2025-07-17T07:11:36.1007377Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-07-17T07:11:36.1007785Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-07-17T07:11:36.1008278Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-07-17T07:11:36.1008692Z ... ) 2025-07-17T07:11:36.1008913Z 2025-07-17T07:11:36.1009115Z 2025-07-17T07:11:36.1009479Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1009968Z 2025-07-17T07:11:36.1010184Z warnings.warn(msg) 2025-07-17T07:11:36.1010443Z 2025-07-17T07:11:36.1010741Z --- Parse Warning: 8 / 136 --- 2025-07-17T07:11:36.1011900Z /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=374. 2025-07-17T07:11:36.1013134Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:36.1013743Z Registers the function implementation as the fallback for the given key. 2025-07-17T07:11:36.1014193Z 2025-07-17T07:11:36.1014526Z This function only works for a library with global namespace ("_"). 2025-07-17T07:11:36.1014953Z 2025-07-17T07:11:36.1015140Z Args: 2025-07-17T07:11:36.1015647Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-07-17T07:11:36.1016185Z to register a fallthrough. 2025-07-17T07:11:36.1016739Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-07-17T07:11:36.1017368Z the dispatch key that the library was created with. 2025-07-17T07:11:36.1018047Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-07-17T07:11:36.1018880Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-07-17T07:11:36.1019408Z 2025-07-17T07:11:36.1019630Z Example:: 2025-07-17T07:11:36.1019872Z 2025-07-17T07:11:36.1020096Z >>> my_lib = Library("_", "IMPL") 2025-07-17T07:11:36.1020470Z >>> def fallback_kernel(op, *args, **kwargs): 2025-07-17T07:11:36.1020858Z >>> # Handle all autocast ops generically 2025-07-17T07:11:36.1021193Z >>> # ... 2025-07-17T07:11:36.1021523Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-07-17T07:11:36.1021877Z 2025-07-17T07:11:36.1022650Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-07-17T07:11:36.1023479Z 2025-07-17T07:11:36.1023730Z my_lib.fallback(fallback_kernel, "Autocast") 2025-07-17T07:11:36.1024066Z ^ 2025-07-17T07:11:36.1024272Z warnings.warn(msg) 2025-07-17T07:11:36.1024527Z 2025-07-17T07:11:36.1024836Z --- Parse Warning: 9 / 136 --- 2025-07-17T07:11:36.1025907Z /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=933. 2025-07-17T07:11:36.1027249Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:36.1027922Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-07-17T07:11:36.1028360Z 2025-07-17T07:11:36.1028644Z Also sometimes known as a "meta kernel", "abstract impl". 2025-07-17T07:11:36.1029029Z 2025-07-17T07:11:36.1029392Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-07-17T07:11:36.1029977Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-07-17T07:11:36.1030564Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-07-17T07:11:36.1031070Z what the properties of the output Tensors are. 2025-07-17T07:11:36.1031408Z 2025-07-17T07:11:36.1031754Z The FakeTensor implementation has the same signature as the operator. 2025-07-17T07:11:36.1032327Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-07-17T07:11:36.1032883Z implementation, assume that all Tensor inputs to the operator are 2025-07-17T07:11:36.1033455Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-07-17T07:11:36.1033988Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-07-17T07:11:36.1034607Z The FakeTensor implementation must consist of only PyTorch operations 2025-07-17T07:11:36.1035151Z (and may not directly access the storage or data of any input or 2025-07-17T07:11:36.1035570Z intermediate Tensors). 2025-07-17T07:11:36.1035840Z 2025-07-17T07:11:36.1036115Z This API may be used as a decorator (see examples). 2025-07-17T07:11:36.1036458Z 2025-07-17T07:11:36.1036714Z For a detailed guide on custom ops, please see 2025-07-17T07:11:36.1037209Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-07-17T07:11:36.1037632Z 2025-07-17T07:11:36.1037833Z Args: 2025-07-17T07:11:36.1038193Z op_name: Operator name (along with the overload) or OpOverload object. 2025-07-17T07:11:36.1038657Z func: Fake tensor implementation. 2025-07-17T07:11:36.1039090Z lib (Optional[Library]): Library to register the fake tensor to. 2025-07-17T07:11:36.1039601Z allow_override: Flag controlling if we want to override an 2025-07-17T07:11:36.1040086Z existing registered fake impl. This is by default off, 2025-07-17T07:11:36.1040559Z and will error you're trying to register a fake impl to 2025-07-17T07:11:36.1041038Z an operator that already has a fake impl. This also only 2025-07-17T07:11:36.1041507Z applies if the custom operator was not created via 2025-07-17T07:11:36.1041973Z torch.library.custom_op, as overriding and existing fake 2025-07-17T07:11:36.1042408Z impl is already allowed. 2025-07-17T07:11:36.1042722Z 2025-07-17T07:11:36.1042924Z Examples: 2025-07-17T07:11:36.1043155Z >>> import torch 2025-07-17T07:11:36.1043439Z >>> import numpy as np 2025-07-17T07:11:36.1043749Z >>> from torch import Tensor 2025-07-17T07:11:36.1044038Z >>> 2025-07-17T07:11:36.1044369Z >>> # Example 1: an operator without data-dependent output shape 2025-07-17T07:11:36.1044904Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-07-17T07:11:36.1045450Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-07-17T07:11:36.1045978Z >>> raise NotImplementedError("Implementation goes here") 2025-07-17T07:11:36.1046364Z >>> 2025-07-17T07:11:36.1046672Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-07-17T07:11:36.1047049Z >>> def _(x, weight, bias): 2025-07-17T07:11:36.1047361Z >>> assert x.dim() == 2 2025-07-17T07:11:36.1047684Z >>> assert weight.dim() == 2 2025-07-17T07:11:36.1048004Z >>> assert bias.dim() == 1 2025-07-17T07:11:36.1048384Z >>> assert x.shape[1] == weight.shape[1] 2025-07-17T07:11:36.1048757Z >>> assert weight.shape[0] == bias.shape[0] 2025-07-17T07:11:36.1049124Z >>> assert x.device == weight.device 2025-07-17T07:11:36.1049448Z >>> 2025-07-17T07:11:36.1049702Z >>> return (x @ weight.t()) + bias 2025-07-17T07:11:36.1050004Z >>> 2025-07-17T07:11:36.1050316Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-07-17T07:11:36.1050712Z >>> x = torch.randn(2, 3) 2025-07-17T07:11:36.1051034Z >>> w = torch.randn(3, 3) 2025-07-17T07:11:36.1051339Z >>> b = torch.randn(3) 2025-07-17T07:11:36.1051684Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-07-17T07:11:36.1052029Z >>> 2025-07-17T07:11:36.1052262Z >>> assert y.shape == (2, 3) 2025-07-17T07:11:36.1052594Z >>> 2025-07-17T07:11:36.1052909Z >>> # Example 2: an operator with data-dependent output shape 2025-07-17T07:11:36.1053411Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-07-17T07:11:36.1053938Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-07-17T07:11:36.1054300Z >>> x_np = x.numpy(force=True) 2025-07-17T07:11:36.1054665Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-07-17T07:11:36.1055046Z >>> return torch.tensor(res, device=x.device) 2025-07-17T07:11:36.1055458Z >>> 2025-07-17T07:11:36.1055781Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-07-17T07:11:36.1056157Z >>> def _(x): 2025-07-17T07:11:36.1056482Z >>> # Number of nonzero-elements is data-dependent. 2025-07-17T07:11:36.1056915Z >>> # Since we cannot peek at the data in an fake impl, 2025-07-17T07:11:36.1057358Z >>> # we use the ctx object to construct a new symint that 2025-07-17T07:11:36.1057764Z >>> # represents the data-dependent size. 2025-07-17T07:11:36.1058135Z >>> ctx = torch.library.get_ctx() 2025-07-17T07:11:36.1058498Z >>> nnz = ctx.new_dynamic_size() 2025-07-17T07:11:36.1058830Z >>> shape = [nnz, x.dim()] 2025-07-17T07:11:36.1059201Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-07-17T07:11:36.1059575Z >>> return result 2025-07-17T07:11:36.1059843Z >>> 2025-07-17T07:11:36.1060169Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-07-17T07:11:36.1060562Z >>> 2025-07-17T07:11:36.1060809Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-07-17T07:11:36.1061287Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-07-17T07:11:36.1061773Z >>> trace.print_readable() 2025-07-17T07:11:36.1062078Z >>> 2025-07-17T07:11:36.1062432Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-07-17T07:11:36.1062864Z 2025-07-17T07:11:36.1063067Z 2025-07-17T07:11:36.1063721Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-07-17T07:11:36.1064454Z 2025-07-17T07:11:36.1064654Z _._ = None 2025-07-17T07:11:36.1064857Z ^ 2025-07-17T07:11:36.1065074Z warnings.warn(msg) 2025-07-17T07:11:36.1065328Z 2025-07-17T07:11:36.1065647Z --- Parse Warning: 10 / 136 --- 2025-07-17T07:11:36.1066773Z /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=1068. 2025-07-17T07:11:36.1068027Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1068567Z Register a backward formula for this custom op. 2025-07-17T07:11:36.1068938Z 2025-07-17T07:11:36.1069259Z In order for an operator to work with autograd, you need to register 2025-07-17T07:11:36.1069690Z a backward formula: 2025-07-17T07:11:36.1070069Z 1. You must tell us how to compute gradients during the backward pass 2025-07-17T07:11:36.1070528Z by providing us a "backward" function. 2025-07-17T07:11:36.1070981Z 2. If you need any values from the forward to compute gradients, you can 2025-07-17T07:11:36.1071469Z use `setup_context` to save values for backward. 2025-07-17T07:11:36.1071954Z 2025-07-17T07:11:36.1072303Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-07-17T07:11:36.1072852Z - ``grads`` is one or more gradients. The number of gradients matches 2025-07-17T07:11:36.1073288Z the number of outputs of the operator. 2025-07-17T07:11:36.1073805Z The ``ctx`` object is `the same ctx object `_ used by 2025-07-17T07:11:36.1074397Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-07-17T07:11:36.1074927Z same as :meth:`torch.autograd.Function.backward`. 2025-07-17T07:11:36.1075342Z 2025-07-17T07:11:36.1075677Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-07-17T07:11:36.1076254Z Please save quantities needed for backward onto the ``ctx`` object via 2025-07-17T07:11:36.1076835Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-07-17T07:11:36.1077408Z or assigning them as attributes of ``ctx``. If your custom op has 2025-07-17T07:11:36.1077948Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-07-17T07:11:36.1078486Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-07-17T07:11:36.1078877Z 2025-07-17T07:11:36.1079213Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-07-17T07:11:36.1079789Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-07-17T07:11:36.1080366Z not depend on or mutate global state. If you need a non-traceable backward, 2025-07-17T07:11:36.1080951Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-07-17T07:11:36.1081385Z 2025-07-17T07:11:36.1081710Z If you need different autograd behavior on different devices, then we 2025-07-17T07:11:36.1082287Z recommend creating two different custom operators, one for each device 2025-07-17T07:11:36.1082868Z that needs different behavior, and switching between them at runtime. 2025-07-17T07:11:36.1083297Z 2025-07-17T07:11:36.1083487Z Examples: 2025-07-17T07:11:36.1083729Z >>> import torch 2025-07-17T07:11:36.1084012Z >>> import numpy as np 2025-07-17T07:11:36.1084309Z >>> from torch import Tensor 2025-07-17T07:11:36.1084610Z >>> 2025-07-17T07:11:36.1084959Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-07-17T07:11:36.1085396Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-07-17T07:11:36.1085742Z >>> x_np = x.cpu().numpy() 2025-07-17T07:11:36.1086059Z >>> y_np = np.sin(x_np) 2025-07-17T07:11:36.1086427Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-07-17T07:11:36.1086774Z >>> 2025-07-17T07:11:36.1087063Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-07-17T07:11:36.1087425Z >>> x, = inputs 2025-07-17T07:11:36.1087706Z >>> ctx.save_for_backward(x) 2025-07-17T07:11:36.1088007Z >>> 2025-07-17T07:11:36.1088248Z >>> def backward(ctx, grad): 2025-07-17T07:11:36.1088557Z >>> x, = ctx.saved_tensors 2025-07-17T07:11:36.1088871Z >>> return grad * x.cos() 2025-07-17T07:11:36.1089164Z >>> 2025-07-17T07:11:36.1089454Z >>> torch.library.register_autograd( 2025-07-17T07:11:36.1089879Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-07-17T07:11:36.1090261Z ... ) 2025-07-17T07:11:36.1090485Z >>> 2025-07-17T07:11:36.1090737Z >>> x = torch.randn(3, requires_grad=True) 2025-07-17T07:11:36.1091074Z >>> y = numpy_sin(x) 2025-07-17T07:11:36.1091436Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-07-17T07:11:36.1091843Z >>> assert torch.allclose(grad_x, x.cos()) 2025-07-17T07:11:36.1092165Z >>> 2025-07-17T07:11:36.1092419Z >>> # Example with a keyword-only arg 2025-07-17T07:11:36.1092847Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-07-17T07:11:36.1093322Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-07-17T07:11:36.1093703Z >>> x_np = x.cpu().numpy() 2025-07-17T07:11:36.1094052Z >>> y_np = x_np * val 2025-07-17T07:11:36.1094408Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-07-17T07:11:36.1094775Z >>> 2025-07-17T07:11:36.1095303Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-07-17T07:11:36.1095789Z >>> ctx.val = keyword_only_inputs["val"] 2025-07-17T07:11:36.1096123Z >>> 2025-07-17T07:11:36.1096371Z >>> def backward(ctx, grad): 2025-07-17T07:11:36.1096685Z >>> return grad * ctx.val 2025-07-17T07:11:36.1096990Z >>> 2025-07-17T07:11:36.1097257Z >>> torch.library.register_autograd( 2025-07-17T07:11:36.1097685Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-07-17T07:11:36.1098065Z ... ) 2025-07-17T07:11:36.1098294Z >>> 2025-07-17T07:11:36.1098561Z >>> x = torch.randn(3, requires_grad=True) 2025-07-17T07:11:36.1098899Z >>> y = numpy_mul(x, val=3.14) 2025-07-17T07:11:36.1099298Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-07-17T07:11:36.1099778Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-07-17T07:11:36.1100148Z 2025-07-17T07:11:36.1100356Z 2025-07-17T07:11:36.1100733Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1101180Z 2025-07-17T07:11:36.1101398Z warnings.warn(msg) 2025-07-17T07:11:36.1101650Z 2025-07-17T07:11:36.1101967Z --- Parse Warning: 11 / 136 --- 2025-07-17T07:11:36.1103053Z /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=1482. 2025-07-17T07:11:36.1104265Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1104880Z Given an operator and some sample arguments, tests if the operator is 2025-07-17T07:11:36.1105316Z registered correctly. 2025-07-17T07:11:36.1105588Z 2025-07-17T07:11:36.1105922Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-07-17T07:11:36.1106514Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-07-17T07:11:36.1107106Z and these APIs require that the functions you pass them satisfy certain 2025-07-17T07:11:36.1107699Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-07-17T07:11:36.1108204Z ``opcheck`` tests these metadata and properties. 2025-07-17T07:11:36.1108541Z 2025-07-17T07:11:36.1108783Z Concretely, we test the following: 2025-07-17T07:11:36.1109095Z 2025-07-17T07:11:36.1109383Z - test_schema: If the schema matches the implementation of 2025-07-17T07:11:36.1109909Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-07-17T07:11:36.1110512Z then we check the implementation mutates the Tensor. If the schema 2025-07-17T07:11:36.1111037Z specifies that we return a new Tensor, then we check that the 2025-07-17T07:11:36.1111567Z implementation returns a new Tensor (instead of an existing one or 2025-07-17T07:11:36.1112021Z a view of an existing one). 2025-07-17T07:11:36.1112439Z - test_autograd_registration: If the operator supports training 2025-07-17T07:11:36.1112955Z (autograd): we check that its autograd formula is registered via 2025-07-17T07:11:36.1113497Z torch.library.register_autograd or a manual registration to one 2025-07-17T07:11:36.1114039Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-07-17T07:11:36.1114513Z registrations may lead to undefined behavior. 2025-07-17T07:11:36.1114947Z - test_faketensor: If the operator has a FakeTensor kernel 2025-07-17T07:11:36.1115452Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-07-17T07:11:36.1115974Z but not sufficient) for the operator to work with PyTorch compilation 2025-07-17T07:11:36.1116575Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-07-17T07:11:36.1117112Z (also sometimes known as a meta kernel) was registered for the 2025-07-17T07:11:36.1117628Z operator and that it is correct. This test takes the result of 2025-07-17T07:11:36.1118142Z running the operator on real tensors and the result of running 2025-07-17T07:11:36.1118645Z the operator on FakeTensors and checks that they have the same 2025-07-17T07:11:36.1119123Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-07-17T07:11:36.1119602Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-07-17T07:11:36.1120100Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-07-17T07:11:36.1120618Z This checks that the outputs (and gradients, if applicable) are the 2025-07-17T07:11:36.1121105Z same under eager-mode PyTorch and torch.compile. 2025-07-17T07:11:36.1121570Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-07-17T07:11:36.1122038Z other things it tests are that the operator supports 2025-07-17T07:11:36.1122538Z functionalization and that the backward pass (if it exists) also 2025-07-17T07:11:36.1123021Z supports FakeTensor and functionalization. 2025-07-17T07:11:36.1123348Z 2025-07-17T07:11:36.1123664Z For best results, please call ``opcheck`` multiple times with a 2025-07-17T07:11:36.1124169Z representative set of inputs. If your operator supports 2025-07-17T07:11:36.1124704Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-07-17T07:11:36.1125282Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-07-17T07:11:36.1125796Z use ``opcheck`` with inputs on all supported devices. 2025-07-17T07:11:36.1126158Z 2025-07-17T07:11:36.1126345Z Args: 2025-07-17T07:11:36.1126673Z op: The operator. Must either be a function decorated with 2025-07-17T07:11:36.1127293Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-07-17T07:11:36.1127845Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-07-17T07:11:36.1128298Z args: The args to the operator 2025-07-17T07:11:36.1128642Z kwargs: The kwargs to the operator 2025-07-17T07:11:36.1129063Z test_utils: Tests that we should run. Default: all of them. 2025-07-17T07:11:36.1129499Z Example: ("test_schema", "test_faketensor") 2025-07-17T07:11:36.1129953Z raise_exception: If we should raise an exception on the first 2025-07-17T07:11:36.1130445Z error. If False, we will return a dict with information 2025-07-17T07:11:36.1130850Z on if each test passed or not. 2025-07-17T07:11:36.1131373Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-07-17T07:11:36.1131884Z If specified ``atol`` must also be specified. 2025-07-17T07:11:36.1132343Z If omitted, default values based on the ``dtype`` are selected 2025-07-17T07:11:36.1132821Z (see the table in :func:`torch.testing.assert_close`). 2025-07-17T07:11:36.1133352Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-07-17T07:11:36.1167862Z If specified ``rtol`` must also be specified. 2025-07-17T07:11:36.1168339Z If omitted, default values based on the ``dtype`` are selected 2025-07-17T07:11:36.1168820Z (see the table in :func:`torch.testing.assert_close`). 2025-07-17T07:11:36.1169179Z 2025-07-17T07:11:36.1169382Z .. warning:: 2025-07-17T07:11:36.1169609Z 2025-07-17T07:11:36.1170014Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-07-17T07:11:36.1170589Z opcheck tests if your usage of torch.library APIs is correct while 2025-07-17T07:11:36.1171189Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-07-17T07:11:36.1171730Z mathematically correct. Use both to test custom ops that support 2025-07-17T07:11:36.1172340Z gradient computation. 2025-07-17T07:11:36.1172609Z 2025-07-17T07:11:36.1172795Z Example: 2025-07-17T07:11:36.1173003Z 2025-07-17T07:11:36.1173246Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:36.1173699Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-07-17T07:11:36.1174157Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-07-17T07:11:36.1174518Z >>> x_np = x.numpy(force=True) 2025-07-17T07:11:36.1174827Z >>> z_np = x_np * y 2025-07-17T07:11:36.1175155Z >>> return torch.from_numpy(z_np).to(x.device) 2025-07-17T07:11:36.1175556Z >>> 2025-07-17T07:11:36.1175789Z >>> @numpy_mul.register_fake 2025-07-17T07:11:36.1176081Z >>> def _(x, y): 2025-07-17T07:11:36.1176371Z >>> return torch.empty_like(x) 2025-07-17T07:11:36.1176667Z >>> 2025-07-17T07:11:36.1176921Z >>> def setup_context(ctx, inputs, output): 2025-07-17T07:11:36.1177246Z >>> y, = inputs 2025-07-17T07:11:36.1177509Z >>> ctx.y = y 2025-07-17T07:11:36.1177756Z >>> 2025-07-17T07:11:36.1177982Z >>> def backward(ctx, grad): 2025-07-17T07:11:36.1178294Z >>> return grad * ctx.y, None 2025-07-17T07:11:36.1178587Z >>> 2025-07-17T07:11:36.1178928Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-07-17T07:11:36.1179345Z >>> 2025-07-17T07:11:36.1179568Z >>> sample_inputs = [ 2025-07-17T07:11:36.1179855Z >>> (torch.randn(3), 3.14), 2025-07-17T07:11:36.1180191Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-07-17T07:11:36.1180576Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-07-17T07:11:36.1181027Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-07-17T07:11:36.1181410Z >>> ] 2025-07-17T07:11:36.1181619Z >>> 2025-07-17T07:11:36.1181857Z >>> for args in sample_inputs: 2025-07-17T07:11:36.1182196Z >>> torch.library.opcheck(numpy_mul, args) 2025-07-17T07:11:36.1182519Z 2025-07-17T07:11:36.1182700Z 2025-07-17T07:11:36.1183079Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1183522Z 2025-07-17T07:11:36.1183723Z warnings.warn(msg) 2025-07-17T07:11:36.1183989Z 2025-07-17T07:11:36.1184430Z --- Parse Warning: 12 / 136 --- 2025-07-17T07:11:36.1190547Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1285. 2025-07-17T07:11:36.1212481Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1213215Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-07-17T07:11:36.1213731Z 2025-07-17T07:11:36.1214005Z Loads an object saved with :func:`torch.save` from a file. 2025-07-17T07:11:36.1214361Z 2025-07-17T07:11:36.1214689Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-07-17T07:11:36.1215349Z which underlie tensors, specially. They are first deserialized on the 2025-07-17T07:11:36.1215903Z CPU and are then moved to the device they were saved from. If this fails 2025-07-17T07:11:36.1216515Z (e.g. because the run time system doesn't have certain devices), an exception 2025-07-17T07:11:36.1217098Z is raised. However, storages can be dynamically remapped to an alternative 2025-07-17T07:11:36.1217674Z set of devices using the :attr:`map_location` argument. 2025-07-17T07:11:36.1218018Z 2025-07-17T07:11:36.1218355Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-07-17T07:11:36.1218925Z storage with two arguments: storage and location. The storage argument 2025-07-17T07:11:36.1219483Z will be the initial deserialization of the storage, residing on the CPU. 2025-07-17T07:11:36.1220030Z Each serialized storage has a location tag associated with it which 2025-07-17T07:11:36.1220563Z identifies the device it was saved from, and this tag is the second 2025-07-17T07:11:36.1221126Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-07-17T07:11:36.1221714Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-07-17T07:11:36.1222250Z :attr:`map_location` should return either ``None`` or a storage. If 2025-07-17T07:11:36.1222813Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-07-17T07:11:36.1223419Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-07-17T07:11:36.1224012Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-07-17T07:11:36.1224442Z 2025-07-17T07:11:36.1224772Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-07-17T07:11:36.1225346Z a device tag, it indicates the location where all tensors should be loaded. 2025-07-17T07:11:36.1225766Z 2025-07-17T07:11:36.1226125Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-07-17T07:11:36.1226701Z appearing in the file (keys), to ones that specify where to put the 2025-07-17T07:11:36.1227112Z storages (values). 2025-07-17T07:11:36.1227345Z 2025-07-17T07:11:36.1227660Z User extensions can register their own location tags and tagging and 2025-07-17T07:11:36.1228254Z deserialization methods using :func:`torch.serialization.register_package`. 2025-07-17T07:11:36.1228704Z 2025-07-17T07:11:36.1229045Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-07-17T07:11:36.1229478Z 2025-07-17T07:11:36.1229656Z Args: 2025-07-17T07:11:36.1230095Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-07-17T07:11:36.1230693Z or a string or os.PathLike object containing a file name 2025-07-17T07:11:36.1231302Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-07-17T07:11:36.1231823Z locations 2025-07-17T07:11:36.1232204Z pickle_module: module used for unpickling metadata and objects (has to 2025-07-17T07:11:36.1232739Z match the :attr:`pickle_module` used to serialize file) 2025-07-17T07:11:36.1233233Z weights_only: Indicates whether unpickler should be restricted to 2025-07-17T07:11:36.1233716Z loading only tensors, primitive types, dictionaries 2025-07-17T07:11:36.1234197Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-07-17T07:11:36.1234658Z See :ref:`weights-only` for more details. 2025-07-17T07:11:36.1235224Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-07-17T07:11:36.1236007Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-07-17T07:11:36.1236794Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-07-17T07:11:36.1237602Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-07-17T07:11:36.1238414Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-07-17T07:11:36.1239002Z will be lazily loaded when their data is accessed. 2025-07-17T07:11:36.1239495Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-07-17T07:11:36.1240072Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-07-17T07:11:36.1240502Z :attr:`errors=...`. 2025-07-17T07:11:36.1240759Z 2025-07-17T07:11:36.1240951Z .. warning:: 2025-07-17T07:11:36.1241303Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-07-17T07:11:36.1241816Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-07-17T07:11:36.1242392Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-07-17T07:11:36.1243018Z during unpickling. Never load data that could have come from an untrusted 2025-07-17T07:11:36.1243661Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-07-17T07:11:36.1244141Z 2025-07-17T07:11:36.1244324Z .. note:: 2025-07-17T07:11:36.1244710Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-07-17T07:11:36.1245339Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-07-17T07:11:36.1245984Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-07-17T07:11:36.1246449Z 2025-07-17T07:11:36.1246628Z .. note:: 2025-07-17T07:11:36.1247015Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-07-17T07:11:36.1247600Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-07-17T07:11:36.1248145Z when loading files saved by Python 2 in Python 3. If this default 2025-07-17T07:11:36.1248726Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-07-17T07:11:36.1249342Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-07-17T07:11:36.1249940Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-07-17T07:11:36.1250526Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-07-17T07:11:36.1250952Z 2025-07-17T07:11:36.1251134Z Example: 2025-07-17T07:11:36.1251407Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-07-17T07:11:36.1251792Z >>> torch.load("tensors.pt", weights_only=True) 2025-07-17T07:11:36.1252147Z # Load all tensors onto the CPU 2025-07-17T07:11:36.1252503Z >>> torch.load( 2025-07-17T07:11:36.1252762Z ... "tensors.pt", 2025-07-17T07:11:36.1253076Z ... map_location=torch.device("cpu"), 2025-07-17T07:11:36.1253405Z ... weights_only=True, 2025-07-17T07:11:36.1253687Z ... ) 2025-07-17T07:11:36.1253958Z # Load all tensors onto the CPU, using a function 2025-07-17T07:11:36.1254294Z >>> torch.load( 2025-07-17T07:11:36.1254553Z ... "tensors.pt", 2025-07-17T07:11:36.1254872Z ... map_location=lambda storage, loc: storage, 2025-07-17T07:11:36.1255280Z ... weights_only=True, 2025-07-17T07:11:36.1255565Z ... ) 2025-07-17T07:11:36.1255808Z # Load all tensors onto GPU 1 2025-07-17T07:11:36.1256103Z >>> torch.load( 2025-07-17T07:11:36.1256367Z ... "tensors.pt", 2025-07-17T07:11:36.1256715Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-07-17T07:11:36.1257125Z ... weights_only=True, 2025-07-17T07:11:36.1257432Z ... ) # type: ignore[attr-defined] 2025-07-17T07:11:36.1257772Z # Map tensors from GPU 1 to GPU 0 2025-07-17T07:11:36.1258130Z >>> torch.load( 2025-07-17T07:11:36.1258381Z ... "tensors.pt", 2025-07-17T07:11:36.1258691Z ... map_location={"cuda:1": "cuda:0"}, 2025-07-17T07:11:36.1259022Z ... weights_only=True, 2025-07-17T07:11:36.1259292Z ... ) 2025-07-17T07:11:36.1259545Z # Load tensor from io.BytesIO object 2025-07-17T07:11:36.1260025Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-07-17T07:11:36.1260506Z >>> with open("tensor.pt", "rb") as f: 2025-07-17T07:11:36.1260849Z ... buffer = io.BytesIO(f.read()) 2025-07-17T07:11:36.1261206Z >>> torch.load(buffer, weights_only=False) 2025-07-17T07:11:36.1261600Z # Load a module with 'ascii' encoding for unpickling 2025-07-17T07:11:36.1262104Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-07-17T07:11:36.1262683Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-07-17T07:11:36.1263077Z 2025-07-17T07:11:36.1263435Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1263528Z 2025-07-17T07:11:36.1263622Z warnings.warn(msg) 2025-07-17T07:11:36.1263697Z 2025-07-17T07:11:36.1263939Z --- Parse Warning: 13 / 136 --- 2025-07-17T07:11:36.1264955Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py line=1848. 2025-07-17T07:11:36.1265229Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1265450Z Computes the minimum storage size to hold the given tensor geometry. 2025-07-17T07:11:36.1265526Z 2025-07-17T07:11:36.1265621Z Example 2025-07-17T07:11:36.1265701Z ======= 2025-07-17T07:11:36.1265778Z 2025-07-17T07:11:36.1266022Z This is the size of a newly allocated tensor's storage, in units of elements 2025-07-17T07:11:36.1266100Z 2025-07-17T07:11:36.1266214Z >>> t = torch.empty((10, 20)) 2025-07-17T07:11:36.1266454Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-07-17T07:11:36.1266537Z 200 2025-07-17T07:11:36.1266622Z 2025-07-17T07:11:36.1266726Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:36.1266857Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-07-17T07:11:36.1266992Z >>> size = compute_required_storage_length( 2025-07-17T07:11:36.1267126Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-07-17T07:11:36.1267209Z ... ) 2025-07-17T07:11:36.1267350Z >>> size == t.storage().size() 2025-07-17T07:11:36.1267432Z True 2025-07-17T07:11:36.1267512Z 2025-07-17T07:11:36.1267714Z A valid tensor may have a larger storage size, but never smaller 2025-07-17T07:11:36.1267795Z 2025-07-17T07:11:36.1267911Z >>> slice = torch.empty(100)[20:40] 2025-07-17T07:11:36.1268011Z >>> slice.storage().size() 2025-07-17T07:11:36.1268091Z 100 2025-07-17T07:11:36.1268178Z 2025-07-17T07:11:36.1268290Z >>> compute_required_storage_length( 2025-07-17T07:11:36.1268454Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-07-17T07:11:36.1268547Z ... ) 2025-07-17T07:11:36.1268627Z 40 2025-07-17T07:11:36.1268706Z 2025-07-17T07:11:36.1268798Z 2025-07-17T07:11:36.1269050Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1269128Z 2025-07-17T07:11:36.1269233Z warnings.warn(msg) 2025-07-17T07:11:36.1269338Z 2025-07-17T07:11:36.1269532Z --- Parse Warning: 14 / 136 --- 2025-07-17T07:11:36.1270486Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=66. 2025-07-17T07:11:36.1270735Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:36.1270992Z Check if the current accelerator is available at runtime: it was build, all the 2025-07-17T07:11:36.1271200Z required drivers are available and at least one device is visible. 2025-07-17T07:11:36.1271346Z See :ref:`accelerator` for details. 2025-07-17T07:11:36.1271426Z 2025-07-17T07:11:36.1271504Z Returns: 2025-07-17T07:11:36.1271966Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-07-17T07:11:36.1272042Z 2025-07-17T07:11:36.1272286Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-07-17T07:11:36.1272555Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-07-17T07:11:36.1272801Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-07-17T07:11:36.1272944Z :ref:`multiprocessing-poison-fork-note`. 2025-07-17T07:11:36.1273031Z 2025-07-17T07:11:36.1273117Z Example:: 2025-07-17T07:11:36.1273191Z 2025-07-17T07:11:36.1273462Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T07:11:36.1273539Z 2025-07-17T07:11:36.1274115Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-07-17T07:11:36.1274191Z 2025-07-17T07:11:36.1274447Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T07:11:36.1274559Z ^ 2025-07-17T07:11:36.1274653Z warnings.warn(msg) 2025-07-17T07:11:36.1274732Z 2025-07-17T07:11:36.1274937Z --- Parse Warning: 15 / 136 --- 2025-07-17T07:11:36.1275840Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=212. 2025-07-17T07:11:36.1276098Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:36.1276300Z Wait for all kernels in all streams on the given device to complete. 2025-07-17T07:11:36.1276376Z 2025-07-17T07:11:36.1276466Z Args: 2025-07-17T07:11:36.1276784Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-07-17T07:11:36.1277010Z the current :ref:`accelerator` device type. If not given, 2025-07-17T07:11:36.1277275Z use :func:`torch.accelerator.current_device_index` by default. 2025-07-17T07:11:36.1277356Z 2025-07-17T07:11:36.1277681Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-07-17T07:11:36.1277760Z 2025-07-17T07:11:36.1277848Z Example:: 2025-07-17T07:11:36.1277937Z 2025-07-17T07:11:36.1278077Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:36.1278343Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T07:11:36.1278497Z >>> start_event = torch.Event(enable_timing=True) 2025-07-17T07:11:36.1278631Z >>> end_event = torch.Event(enable_timing=True) 2025-07-17T07:11:36.1278742Z >>> start_event.record() 2025-07-17T07:11:36.1278982Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-07-17T07:11:36.1279122Z >>> sum = torch.sum(tensor) 2025-07-17T07:11:36.1279230Z >>> end_event.record() 2025-07-17T07:11:36.1279354Z >>> torch.accelerator.synchronize() 2025-07-17T07:11:36.1279578Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-07-17T07:11:36.1279672Z 2025-07-17T07:11:36.1280233Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-07-17T07:11:36.1280322Z 2025-07-17T07:11:36.1280579Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-07-17T07:11:36.1280673Z ^ 2025-07-17T07:11:36.1280780Z warnings.warn(msg) 2025-07-17T07:11:36.1280856Z 2025-07-17T07:11:36.1281037Z --- Parse Warning: 16 / 136 --- 2025-07-17T07:11:36.1281888Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=434. 2025-07-17T07:11:36.1282140Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:36.1282260Z Retrieves the CUDA runtime API module. 2025-07-17T07:11:36.1282345Z 2025-07-17T07:11:36.1282421Z 2025-07-17T07:11:36.1282681Z This function initializes the CUDA runtime environment if it is not already 2025-07-17T07:11:36.1282906Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-07-17T07:11:36.1283126Z runtime API module provides access to various CUDA runtime functions. 2025-07-17T07:11:36.1283217Z 2025-07-17T07:11:36.1283294Z Args: 2025-07-17T07:11:36.1283377Z ``None`` 2025-07-17T07:11:36.1283463Z 2025-07-17T07:11:36.1283545Z Returns: 2025-07-17T07:11:36.1283684Z module: The CUDA runtime API module (_cudart). 2025-07-17T07:11:36.1283772Z 2025-07-17T07:11:36.1283851Z Raises: 2025-07-17T07:11:36.1284092Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-07-17T07:11:36.1284456Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-07-17T07:11:36.1284534Z 2025-07-17T07:11:36.1284675Z Example of CUDA operations with profiling: 2025-07-17T07:11:36.1284764Z >>> import torch 2025-07-17T07:11:36.1284897Z >>> from torch.cuda import cudart, check_error 2025-07-17T07:11:36.1284997Z >>> import os 2025-07-17T07:11:36.1285073Z >>> 2025-07-17T07:11:36.1285184Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-07-17T07:11:36.1285274Z >>> 2025-07-17T07:11:36.1285407Z >>> def perform_cuda_operations_with_streams(): 2025-07-17T07:11:36.1285519Z >>> stream = torch.cuda.Stream() 2025-07-17T07:11:36.1285654Z >>> with torch.cuda.stream(stream): 2025-07-17T07:11:36.1285804Z >>> x = torch.randn(100, 100, device='cuda') 2025-07-17T07:11:36.1285938Z >>> y = torch.randn(100, 100, device='cuda') 2025-07-17T07:11:36.1286047Z >>> z = torch.mul(x, y) 2025-07-17T07:11:36.1286131Z >>> return z 2025-07-17T07:11:36.1286223Z >>> 2025-07-17T07:11:36.1286336Z >>> torch.cuda.synchronize() 2025-07-17T07:11:36.1286464Z >>> print("====== Start nsys profiling ======") 2025-07-17T07:11:36.1286601Z >>> check_error(cudart().cudaProfilerStart()) 2025-07-17T07:11:36.1286739Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-07-17T07:11:36.1286894Z >>> result = perform_cuda_operations_with_streams() 2025-07-17T07:11:36.1287018Z >>> print("CUDA operations completed.") 2025-07-17T07:11:36.1287177Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-07-17T07:11:36.1287333Z >>> print("====== End nsys profiling ======") 2025-07-17T07:11:36.1287410Z 2025-07-17T07:11:36.1287606Z To run this example and save the profiling information, execute: 2025-07-17T07:11:36.1288034Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-07-17T07:11:36.1288113Z 2025-07-17T07:11:36.1288353Z This command profiles the CUDA operations in the provided script and saves 2025-07-17T07:11:36.1288548Z the profiling information to a file named `trace_name.prof`. 2025-07-17T07:11:36.1288780Z The `--profile-from-start off` option ensures that profiling starts only 2025-07-17T07:11:36.1288934Z after the `cudaProfilerStart` call in the script. 2025-07-17T07:11:36.1289156Z The `--csv` and `--print-summary` options format the profiling output as a 2025-07-17T07:11:36.1289285Z CSV file and print a summary, respectively. 2025-07-17T07:11:36.1289532Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-07-17T07:11:36.1289686Z overwrite of the output file if it already exists. 2025-07-17T07:11:36.1289768Z 2025-07-17T07:11:36.1290430Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2025-07-17T07:11:36.1290507Z 2025-07-17T07:11:36.1290861Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-07-17T07:11:36.1290936Z ^ 2025-07-17T07:11:36.1291029Z warnings.warn(msg) 2025-07-17T07:11:36.1291116Z 2025-07-17T07:11:36.1291296Z --- Parse Warning: 17 / 136 --- 2025-07-17T07:11:36.1292177Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2025-07-17T07:11:36.1292448Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1292523Z 2025-07-17T07:11:36.1292758Z Append the given callback function to this ``Future``, which will be run 2025-07-17T07:11:36.1292958Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-07-17T07:11:36.1293160Z the same ``Future``, but the order in which they will be executed cannot 2025-07-17T07:11:36.1293349Z be guaranteed (to enforce a certain order consider chaining: 2025-07-17T07:11:36.1293545Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-07-17T07:11:36.1293747Z is the reference to this ``Future``. The callback function can use the 2025-07-17T07:11:36.1293956Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-07-17T07:11:36.1294177Z already completed, the given callback will be run immediately inline. 2025-07-17T07:11:36.1294266Z 2025-07-17T07:11:36.1294484Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-07-17T07:11:36.1294696Z callback might be invoked while the async kernels that are populating 2025-07-17T07:11:36.1294929Z those tensors haven't yet finished executing on the device. However, the 2025-07-17T07:11:36.1295130Z callback will be invoked with some dedicated streams set as current 2025-07-17T07:11:36.1295423Z (fetched from a global pool) which will be synchronized with those 2025-07-17T07:11:36.1295661Z kernels. Hence any operation performed by the callback on these tensors 2025-07-17T07:11:36.1295864Z will be scheduled on the device after the kernels complete. In other 2025-07-17T07:11:36.1296070Z words, as long as the callback doesn't switch streams, it can safely 2025-07-17T07:11:36.1296294Z manipulate the result without any additional synchronization. This is 2025-07-17T07:11:36.1296449Z similar to the non-blocking behavior of :meth:`wait`. 2025-07-17T07:11:36.1296569Z 2025-07-17T07:11:36.1296779Z Similarly, if the callback returns a value that contains tensors that 2025-07-17T07:11:36.1296966Z reside on a GPU, it can do so even if the kernels that are producing 2025-07-17T07:11:36.1297238Z these tensors are still running on the device, as long as the callback 2025-07-17T07:11:36.1297439Z didn't change streams during its execution. If one wants to change 2025-07-17T07:11:36.1297657Z streams, one must be careful to re-synchronize them with the original 2025-07-17T07:11:36.1297870Z streams, that is, those that were current when the callback was invoked. 2025-07-17T07:11:36.1297944Z 2025-07-17T07:11:36.1298026Z Args: 2025-07-17T07:11:36.1298224Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-07-17T07:11:36.1298326Z the only argument. 2025-07-17T07:11:36.1298406Z 2025-07-17T07:11:36.1298483Z Returns: 2025-07-17T07:11:36.1298643Z A new ``Future`` object that holds the return value of the 2025-07-17T07:11:36.1298824Z ``callback`` and will be marked as completed when the given 2025-07-17T07:11:36.1298921Z ``callback`` finishes. 2025-07-17T07:11:36.1299006Z 2025-07-17T07:11:36.1299180Z .. note:: Note that if the callback function throws, either 2025-07-17T07:11:36.1299390Z through the original future being completed with an exception and 2025-07-17T07:11:36.1299588Z calling ``fut.wait()``, or through other code in the callback, the 2025-07-17T07:11:36.1299788Z future returned by ``then`` will be marked appropriately with the 2025-07-17T07:11:36.1299982Z encountered error. However, if this callback later completes 2025-07-17T07:11:36.1300199Z additional futures, those futures are not marked as completed with 2025-07-17T07:11:36.1300399Z an error and the user is responsible for handling completion/waiting 2025-07-17T07:11:36.1300520Z on those futures independently. 2025-07-17T07:11:36.1300600Z 2025-07-17T07:11:36.1300685Z Example:: 2025-07-17T07:11:36.1300772Z 2025-07-17T07:11:36.1300916Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-07-17T07:11:36.1301016Z >>> def callback(fut): 2025-07-17T07:11:36.1301162Z ... print(f"RPC return value is {fut.wait()}.") 2025-07-17T07:11:36.1301271Z >>> fut = torch.futures.Future() 2025-07-17T07:11:36.1301436Z >>> # The inserted callback will print the return value when 2025-07-17T07:11:36.1301562Z >>> # receiving the response from "worker1" 2025-07-17T07:11:36.1301664Z >>> cb_fut = fut.then(callback) 2025-07-17T07:11:36.1301769Z >>> chain_cb_fut = cb_fut.then( 2025-07-17T07:11:36.1301912Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-07-17T07:11:36.1301991Z ... ) 2025-07-17T07:11:36.1302092Z >>> fut.set_result(5) 2025-07-17T07:11:36.1302187Z RPC return value is 5. 2025-07-17T07:11:36.1302281Z Chained cb done. None 2025-07-17T07:11:36.1302381Z 2025-07-17T07:11:36.1302630Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1302703Z 2025-07-17T07:11:36.1302807Z warnings.warn(msg) 2025-07-17T07:11:36.1302880Z 2025-07-17T07:11:36.1303071Z --- Parse Warning: 18 / 136 --- 2025-07-17T07:11:36.1303976Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=211. 2025-07-17T07:11:36.1304229Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1304306Z 2025-07-17T07:11:36.1304506Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-07-17T07:11:36.1304718Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-07-17T07:11:36.1304866Z cannot be marked completed twice. 2025-07-17T07:11:36.1304947Z 2025-07-17T07:11:36.1305162Z If the result contains tensors that reside on GPUs, this method can be 2025-07-17T07:11:36.1305423Z called even if the asynchronous kernels that are populating those 2025-07-17T07:11:36.1305640Z tensors haven't yet completed running on the device, provided that the 2025-07-17T07:11:36.1305868Z streams on which those kernels were enqueued are set as the current ones 2025-07-17T07:11:36.1306072Z when this method is called. Put simply, it's safe to call this method 2025-07-17T07:11:36.1306284Z immediately after launching those kernels, without any additional 2025-07-17T07:11:36.1306517Z synchronization, as long as one doesn't change streams in between. This 2025-07-17T07:11:36.1306729Z method will record events on all the relevant current streams and will 2025-07-17T07:11:36.1306924Z use them to ensure proper scheduling for all the consumers of this 2025-07-17T07:11:36.1307017Z ``Future``. 2025-07-17T07:11:36.1307093Z 2025-07-17T07:11:36.1307183Z Args: 2025-07-17T07:11:36.1307339Z result (object): the result object of this ``Future``. 2025-07-17T07:11:36.1307414Z 2025-07-17T07:11:36.1307506Z Example:: 2025-07-17T07:11:36.1307583Z 2025-07-17T07:11:36.1307724Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-07-17T07:11:36.1307829Z >>> import threading 2025-07-17T07:11:36.1307914Z >>> import time 2025-07-17T07:11:36.1308024Z >>> def slow_set_future(fut, value): 2025-07-17T07:11:36.1308128Z ... time.sleep(0.5) 2025-07-17T07:11:36.1308225Z ... fut.set_result(value) 2025-07-17T07:11:36.1308331Z >>> fut = torch.futures.Future() 2025-07-17T07:11:36.1308436Z >>> t = threading.Thread( 2025-07-17T07:11:36.1308534Z ... target=slow_set_future, 2025-07-17T07:11:36.1308652Z ... args=(fut, torch.ones(2) * 3) 2025-07-17T07:11:36.1308728Z ... ) 2025-07-17T07:11:36.1308811Z >>> t.start() 2025-07-17T07:11:36.1308916Z >>> print(fut.wait()) 2025-07-17T07:11:36.1309001Z tensor([3., 3.]) 2025-07-17T07:11:36.1309080Z >>> t.join() 2025-07-17T07:11:36.1309169Z 2025-07-17T07:11:36.1309418Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1309494Z 2025-07-17T07:11:36.1309597Z warnings.warn(msg) 2025-07-17T07:11:36.1309673Z 2025-07-17T07:11:36.1309857Z --- Parse Warning: 19 / 136 --- 2025-07-17T07:11:36.1310741Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=145. 2025-07-17T07:11:36.1310995Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1311221Z Compiles compute shader from source and allows one to invoke kernels 2025-07-17T07:11:36.1311393Z defined there from the comfort of Python runtime 2025-07-17T07:11:36.1311478Z Example:: 2025-07-17T07:11:36.1311565Z 2025-07-17T07:11:36.1311696Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-07-17T07:11:36.1311818Z >>> lib = torch.mps.compile_shader( 2025-07-17T07:11:36.1312213Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-07-17T07:11:36.1312290Z ... ) 2025-07-17T07:11:36.1312410Z >>> x = torch.zeros(16, device="mps") 2025-07-17T07:11:36.1312502Z >>> lib.full(x, 3.14) 2025-07-17T07:11:36.1312579Z 2025-07-17T07:11:36.1312840Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1312916Z 2025-07-17T07:11:36.1313007Z warnings.warn(msg) 2025-07-17T07:11:36.1313093Z 2025-07-17T07:11:36.1313271Z --- Parse Warning: 20 / 136 --- 2025-07-17T07:11:36.1314133Z /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. 2025-07-17T07:11:36.1314449Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1314605Z Return the sum of each row of the given sparse tensor. 2025-07-17T07:11:36.1314690Z 2025-07-17T07:11:36.1314914Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-07-17T07:11:36.1315103Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-07-17T07:11:36.1315321Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-07-17T07:11:36.1315471Z returns a dense tensor instead of a sparse tensor. 2025-07-17T07:11:36.1315543Z 2025-07-17T07:11:36.1315813Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-07-17T07:11:36.1316008Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-07-17T07:11:36.1316094Z 2025-07-17T07:11:36.1316315Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-07-17T07:11:36.1316552Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-07-17T07:11:36.1316633Z 2025-07-17T07:11:36.1316711Z Args: 2025-07-17T07:11:36.1316829Z input (Tensor): the input sparse tensor 2025-07-17T07:11:36.1317108Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-07-17T07:11:36.1317197Z over all dims. 2025-07-17T07:11:36.1317447Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-07-17T07:11:36.1317564Z Default: dtype of :attr:`input`. 2025-07-17T07:11:36.1317637Z 2025-07-17T07:11:36.1317729Z Example:: 2025-07-17T07:11:36.1317804Z 2025-07-17T07:11:36.1317882Z >>> nnz = 3 2025-07-17T07:11:36.1317979Z >>> dims = [5, 5, 2, 3] 2025-07-17T07:11:36.1318134Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-07-17T07:11:36.1318322Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-07-17T07:11:36.1318455Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-07-17T07:11:36.1318557Z >>> size = torch.Size(dims) 2025-07-17T07:11:36.1318695Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:36.1318833Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-07-17T07:11:36.1318915Z >>> S 2025-07-17T07:11:36.1319039Z tensor(indices=tensor([[2, 0, 3], 2025-07-17T07:11:36.1319134Z [2, 4, 1]]), 2025-07-17T07:11:36.1319260Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-07-17T07:11:36.1319383Z [ 0.3411, 0.0918, -0.2312]], 2025-07-17T07:11:36.1319489Z 2025-07-17T07:11:36.1319597Z [[ 0.5348, 0.0634, -2.0494], 2025-07-17T07:11:36.1319720Z [-0.7125, -1.0646, 2.1844]], 2025-07-17T07:11:36.1319801Z 2025-07-17T07:11:36.1319907Z [[ 0.1276, 0.1874, -0.6334], 2025-07-17T07:11:36.1320032Z [-1.9682, -0.5340, 0.7483]]]), 2025-07-17T07:11:36.1320177Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-07-17T07:11:36.1320271Z 2025-07-17T07:11:36.1320463Z # when sum over only part of sparse_dims, return a sparse tensor 2025-07-17T07:11:36.1320578Z >>> torch.sparse.sum(S, [1, 3]) 2025-07-17T07:11:36.1320703Z tensor(indices=tensor([[0, 2, 3]]), 2025-07-17T07:11:36.1320817Z values=tensor([[-1.4512, 0.4073], 2025-07-17T07:11:36.1320944Z [-0.8901, 0.2017], 2025-07-17T07:11:36.1321064Z [-0.3183, -1.7539]]), 2025-07-17T07:11:36.1321201Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-07-17T07:11:36.1321296Z 2025-07-17T07:11:36.1321535Z # when sum over all sparse dim, return a dense tensor 2025-07-17T07:11:36.1321643Z # with summed dims squeezed 2025-07-17T07:11:36.1321767Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-07-17T07:11:36.1321866Z tensor([-2.6596, -1.1450]) 2025-07-17T07:11:36.1321947Z 2025-07-17T07:11:36.1322219Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1322303Z 2025-07-17T07:11:36.1322400Z warnings.warn(msg) 2025-07-17T07:11:36.1322492Z 2025-07-17T07:11:36.1322677Z --- Parse Warning: 21 / 136 --- 2025-07-17T07:11:36.1323529Z /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. 2025-07-17T07:11:36.1323800Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1323880Z 2025-07-17T07:11:36.1324112Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-07-17T07:11:36.1324309Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-07-17T07:11:36.1324525Z pushes the map into PyTorch operations called by ``func``, effectively 2025-07-17T07:11:36.1324645Z vectorizing those operations. 2025-07-17T07:11:36.1324723Z 2025-07-17T07:11:36.1324931Z vmap is useful for handling batch dimensions: one can write a function 2025-07-17T07:11:36.1325143Z ``func`` that runs on examples and then lift it to a function that can 2025-07-17T07:11:36.1325351Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-07-17T07:11:36.1325532Z compute batched gradients when composed with autograd. 2025-07-17T07:11:36.1325614Z 2025-07-17T07:11:36.1325700Z .. note:: 2025-07-17T07:11:36.1325893Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-07-17T07:11:36.1326025Z convenience. Use whichever one you'd like. 2025-07-17T07:11:36.1326104Z 2025-07-17T07:11:36.1326197Z Args: 2025-07-17T07:11:36.1326405Z func (function): A Python function that takes one or more arguments. 2025-07-17T07:11:36.1326528Z Must return one or more Tensors. 2025-07-17T07:11:36.1326726Z in_dims (int or nested structure): Specifies which dimension of the 2025-07-17T07:11:36.1326895Z inputs should be mapped over. ``in_dims`` should have a 2025-07-17T07:11:36.1327096Z structure like the inputs. If the ``in_dim`` for a particular 2025-07-17T07:11:36.1327279Z input is None, then that indicates there is no map dimension. 2025-07-17T07:11:36.1327367Z Default: 0. 2025-07-17T07:11:36.1327599Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-07-17T07:11:36.1327788Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-07-17T07:11:36.1327943Z it should have one element per output. Default: 0. 2025-07-17T07:11:36.1328139Z randomness (str): Specifies whether the randomness in this 2025-07-17T07:11:36.1328346Z vmap should be the same or different across batches. If 'different', 2025-07-17T07:11:36.1328555Z the randomness for each batch will be different. If 'same', the 2025-07-17T07:11:36.1328768Z randomness will be the same across batches. If 'error', any calls to 2025-07-17T07:11:36.1328973Z random functions will error. Default: 'error'. WARNING: this flag 2025-07-17T07:11:36.1329186Z only applies to random PyTorch operations and does not apply to 2025-07-17T07:11:36.1329319Z Python's random module or numpy randomness. 2025-07-17T07:11:36.1329580Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-07-17T07:11:36.1329801Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-07-17T07:11:36.1330110Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-07-17T07:11:36.1330389Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-07-17T07:11:36.1330470Z 2025-07-17T07:11:36.1330552Z Returns: 2025-07-17T07:11:36.1330752Z Returns a new "batched" function. It takes the same inputs as 2025-07-17T07:11:36.1330936Z ``func``, except each input has an extra dimension at the index 2025-07-17T07:11:36.1331125Z specified by ``in_dims``. It takes returns the same outputs as 2025-07-17T07:11:36.1331322Z ``func``, except each output has an extra dimension at the index 2025-07-17T07:11:36.1331428Z specified by ``out_dims``. 2025-07-17T07:11:36.1331523Z 2025-07-17T07:11:36.1331608Z .. warning: 2025-07-17T07:11:36.1331808Z :func:`vmap` works best with functional-style code. Please do not 2025-07-17T07:11:36.1332004Z perform any side-effects in ``func``, with the exception of 2025-07-17T07:11:36.1332240Z in-place PyTorch operations. Examples of side-effects include mutating 2025-07-17T07:11:36.1332463Z Python data structures and assigning values to variables not captured 2025-07-17T07:11:36.1332559Z in ``func``. 2025-07-17T07:11:36.1332636Z 2025-07-17T07:11:36.1332884Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-07-17T07:11:36.1333100Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-07-17T07:11:36.1333318Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-07-17T07:11:36.1333409Z 2025-07-17T07:11:36.1333510Z >>> torch.dot # [D], [D] -> [] 2025-07-17T07:11:36.1333714Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-07-17T07:11:36.1333853Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-07-17T07:11:36.1333949Z >>> batched_dot(x, y) 2025-07-17T07:11:36.1334028Z 2025-07-17T07:11:36.1334270Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-07-17T07:11:36.1334375Z model authoring experience. 2025-07-17T07:11:36.1334466Z 2025-07-17T07:11:36.1334575Z >>> batch_size, feature_size = 3, 5 2025-07-17T07:11:36.1334746Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-07-17T07:11:36.1334838Z >>> 2025-07-17T07:11:36.1334940Z >>> def model(feature_vec): 2025-07-17T07:11:36.1335069Z >>> # Very simple linear model with activation 2025-07-17T07:11:36.1335299Z >>> return feature_vec.dot(weights).relu() 2025-07-17T07:11:36.1335384Z >>> 2025-07-17T07:11:36.1335535Z >>> examples = torch.randn(batch_size, feature_size) 2025-07-17T07:11:36.1335703Z >>> result = torch.vmap(model)(examples) 2025-07-17T07:11:36.1335783Z 2025-07-17T07:11:36.1336031Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-07-17T07:11:36.1336284Z or impossible to batch. One example is higher-order gradient computation. 2025-07-17T07:11:36.1336516Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-07-17T07:11:36.1336754Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-07-17T07:11:36.1337001Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-07-17T07:11:36.1337236Z we can vectorize the whole computation, computing the Jacobian in a single 2025-07-17T07:11:36.1337353Z call to ``autograd.grad``. 2025-07-17T07:11:36.1337431Z 2025-07-17T07:11:36.1337514Z >>> # Setup 2025-07-17T07:11:36.1337607Z >>> N = 5 2025-07-17T07:11:36.1337701Z >>> f = lambda x: x**2 2025-07-17T07:11:36.1337863Z >>> x = torch.randn(N, requires_grad=True) 2025-07-17T07:11:36.1337946Z >>> y = f(x) 2025-07-17T07:11:36.1338039Z >>> I_N = torch.eye(N) 2025-07-17T07:11:36.1338132Z >>> 2025-07-17T07:11:36.1338276Z >>> # Sequential approach 2025-07-17T07:11:36.1338492Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-07-17T07:11:36.1338614Z >>> for v in I_N.unbind()] 2025-07-17T07:11:36.1338734Z >>> jacobian = torch.stack(jacobian_rows) 2025-07-17T07:11:36.1338813Z >>> 2025-07-17T07:11:36.1338941Z >>> # vectorized gradient computation 2025-07-17T07:11:36.1339033Z >>> def get_vjp(v): 2025-07-17T07:11:36.1339153Z >>> return torch.autograd.grad(y, x, v) 2025-07-17T07:11:36.1339281Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-07-17T07:11:36.1339360Z 2025-07-17T07:11:36.1339631Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-07-17T07:11:36.1339715Z 2025-07-17T07:11:36.1339815Z >>> torch.dot # [D], [D] -> [] 2025-07-17T07:11:36.1339931Z >>> batched_dot = torch.vmap( 2025-07-17T07:11:36.1340034Z ... torch.vmap(torch.dot) 2025-07-17T07:11:36.1340152Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-07-17T07:11:36.1340296Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-07-17T07:11:36.1340421Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-07-17T07:11:36.1340498Z 2025-07-17T07:11:36.1340750Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-07-17T07:11:36.1340900Z the dimension that each inputs are batched along as 2025-07-17T07:11:36.1340991Z 2025-07-17T07:11:36.1341091Z >>> torch.dot # [N], [N] -> [] 2025-07-17T07:11:36.1341310Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-07-17T07:11:36.1341444Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-07-17T07:11:36.1341539Z >>> batched_dot( 2025-07-17T07:11:36.1341621Z ... x, y 2025-07-17T07:11:36.1341819Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-07-17T07:11:36.1341897Z 2025-07-17T07:11:36.1342156Z If there are multiple inputs each of which is batched along different dimensions, 2025-07-17T07:11:36.1342365Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-07-17T07:11:36.1342442Z 2025-07-17T07:11:36.1342555Z >>> torch.dot # [D], [D] -> [] 2025-07-17T07:11:36.1342785Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-07-17T07:11:36.1342907Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-07-17T07:11:36.1343012Z >>> batched_dot( 2025-07-17T07:11:36.1343096Z ... x, y 2025-07-17T07:11:36.1343286Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-07-17T07:11:36.1343377Z 2025-07-17T07:11:36.1343638Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-07-17T07:11:36.1343747Z matching the shape of the input: 2025-07-17T07:11:36.1343837Z 2025-07-17T07:11:36.1343981Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-07-17T07:11:36.1344114Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-07-17T07:11:36.1344210Z >>> input = {"x": x, "y": y} 2025-07-17T07:11:36.1344385Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-07-17T07:11:36.1344496Z >>> batched_dot(input) 2025-07-17T07:11:36.1344575Z 2025-07-17T07:11:36.1344852Z By default, the output is batched along the first dimension. However, it can be batched 2025-07-17T07:11:36.1344988Z along any dimension by using ``out_dims`` 2025-07-17T07:11:36.1345068Z 2025-07-17T07:11:36.1345165Z >>> f = lambda x: x**2 2025-07-17T07:11:36.1345276Z >>> x = torch.randn(2, 5) 2025-07-17T07:11:36.1345424Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-07-17T07:11:36.1345540Z >>> batched_pow(x) # [5, 2] 2025-07-17T07:11:36.1345616Z 2025-07-17T07:11:36.1345955Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-07-17T07:11:36.1346058Z accept kwargs 2025-07-17T07:11:36.1346137Z 2025-07-17T07:11:36.1346236Z >>> x = torch.randn([2, 5]) 2025-07-17T07:11:36.1346343Z >>> def fn(x, scale=4.): 2025-07-17T07:11:36.1346436Z >>> return x * scale 2025-07-17T07:11:36.1346516Z >>> 2025-07-17T07:11:36.1346634Z >>> batched_pow = torch.vmap(fn) 2025-07-17T07:11:36.1346775Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-07-17T07:11:36.1346997Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-07-17T07:11:36.1347089Z 2025-07-17T07:11:36.1347176Z .. note:: 2025-07-17T07:11:36.1347408Z vmap does not provide general autobatching or handle variable-length 2025-07-17T07:11:36.1347514Z sequences out of the box. 2025-07-17T07:11:36.1347592Z 2025-07-17T07:11:36.1347852Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1347927Z 2025-07-17T07:11:36.1348019Z warnings.warn(msg) 2025-07-17T07:11:36.1348101Z 2025-07-17T07:11:36.1348304Z --- Parse Warning: 22 / 136 --- 2025-07-17T07:11:36.1349153Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=306. 2025-07-17T07:11:36.1349424Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1349658Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-07-17T07:11:36.1349864Z input(s) specified by ``argnums``. This operator can be nested to 2025-07-17T07:11:36.1349977Z compute higher-order gradients. 2025-07-17T07:11:36.1350054Z 2025-07-17T07:11:36.1350143Z Args: 2025-07-17T07:11:36.1350351Z func (Callable): A Python function that takes one or more arguments. 2025-07-17T07:11:36.1350613Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-07-17T07:11:36.1350891Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-07-17T07:11:36.1350987Z ``(output, aux)``. 2025-07-17T07:11:36.1351270Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-07-17T07:11:36.1351469Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-07-17T07:11:36.1351684Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-07-17T07:11:36.1351860Z auxiliary objects: ``(output, aux)``. Default: False. 2025-07-17T07:11:36.1351939Z 2025-07-17T07:11:36.1352049Z Returns: 2025-07-17T07:11:36.1352340Z Function to compute gradients with respect to its inputs. By default, the output of 2025-07-17T07:11:36.1352572Z the function is the gradient tensor(s) with respect to the first argument. 2025-07-17T07:11:36.1352852Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-07-17T07:11:36.1353100Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-07-17T07:11:36.1353238Z respect to each ``argnums`` value is returned. 2025-07-17T07:11:36.1353323Z 2025-07-17T07:11:36.1353422Z Example of using ``grad``: 2025-07-17T07:11:36.1353497Z 2025-07-17T07:11:36.1353602Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1353711Z >>> from torch.func import grad 2025-07-17T07:11:36.1353820Z >>> x = torch.randn([]) 2025-07-17T07:11:36.1353983Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-07-17T07:11:36.1354109Z >>> assert torch.allclose(cos_x, x.cos()) 2025-07-17T07:11:36.1354202Z >>> 2025-07-17T07:11:36.1354307Z >>> # Second-order gradients 2025-07-17T07:11:36.1354498Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-07-17T07:11:36.1354643Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-07-17T07:11:36.1354719Z 2025-07-17T07:11:36.1354964Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-07-17T07:11:36.1355050Z 2025-07-17T07:11:36.1355145Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1355260Z >>> from torch.func import grad, vmap 2025-07-17T07:11:36.1355384Z >>> batch_size, feature_size = 3, 5 2025-07-17T07:11:36.1355459Z >>> 2025-07-17T07:11:36.1355584Z >>> def model(weights, feature_vec): 2025-07-17T07:11:36.1355712Z >>> # Very simple linear model with activation 2025-07-17T07:11:36.1355822Z >>> assert feature_vec.dim() == 1 2025-07-17T07:11:36.1355953Z >>> return feature_vec.dot(weights).relu() 2025-07-17T07:11:36.1356030Z >>> 2025-07-17T07:11:36.1356162Z >>> def compute_loss(weights, example, target): 2025-07-17T07:11:36.1356280Z >>> y = model(weights, example) 2025-07-17T07:11:36.1356411Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-07-17T07:11:36.1356485Z >>> 2025-07-17T07:11:36.1356659Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-07-17T07:11:36.1356802Z >>> examples = torch.randn(batch_size, feature_size) 2025-07-17T07:11:36.1356922Z >>> targets = torch.randn(batch_size) 2025-07-17T07:11:36.1357045Z >>> inputs = (weights, examples, targets) 2025-07-17T07:11:36.1357271Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-07-17T07:11:36.1357373Z ... *inputs 2025-07-17T07:11:36.1357448Z ... ) 2025-07-17T07:11:36.1357519Z 2025-07-17T07:11:36.1357701Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-07-17T07:11:36.1357778Z 2025-07-17T07:11:36.1357873Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1357990Z >>> from torch.func import grad 2025-07-17T07:11:36.1358096Z >>> def my_loss_func(y, y_pred): 2025-07-17T07:11:36.1358224Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-07-17T07:11:36.1358336Z >>> loss = loss_per_sample.mean() 2025-07-17T07:11:36.1358456Z >>> return loss, (y_pred, loss_per_sample) 2025-07-17T07:11:36.1358539Z >>> 2025-07-17T07:11:36.1358685Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-07-17T07:11:36.1358783Z >>> y_true = torch.rand(4) 2025-07-17T07:11:36.1358918Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-07-17T07:11:36.1359049Z >>> out = fn(y_true, y_preds) 2025-07-17T07:11:36.1359296Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-07-17T07:11:36.1359370Z 2025-07-17T07:11:36.1359459Z .. note:: 2025-07-17T07:11:36.1359634Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-07-17T07:11:36.1359709Z 2025-07-17T07:11:36.1359851Z Case 1: Using ``torch.no_grad`` inside a function: 2025-07-17T07:11:36.1359932Z 2025-07-17T07:11:36.1360028Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1360113Z >>> def f(x): 2025-07-17T07:11:36.1360221Z >>> with torch.no_grad(): 2025-07-17T07:11:36.1360311Z >>> c = x ** 2 2025-07-17T07:11:36.1360404Z >>> return x - c 2025-07-17T07:11:36.1360486Z 2025-07-17T07:11:36.1360680Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-07-17T07:11:36.1360791Z 2025-07-17T07:11:36.1360971Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-07-17T07:11:36.1361047Z 2025-07-17T07:11:36.1361153Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1361304Z >>> with torch.no_grad(): 2025-07-17T07:11:36.1361397Z >>> grad(f)(x) 2025-07-17T07:11:36.1361483Z 2025-07-17T07:11:36.1361703Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-07-17T07:11:36.1361919Z outer one. This is because ``grad`` is a "function transform": its result 2025-07-17T07:11:36.1362145Z should not depend on the result of a context manager outside of ``f``. 2025-07-17T07:11:36.1362224Z 2025-07-17T07:11:36.1362311Z 2025-07-17T07:11:36.1362561Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1362639Z 2025-07-17T07:11:36.1362748Z warnings.warn(msg) 2025-07-17T07:11:36.1362824Z 2025-07-17T07:11:36.1363016Z --- Parse Warning: 23 / 136 --- 2025-07-17T07:11:36.1364008Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py line=396. 2025-07-17T07:11:36.1364254Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:36.1364439Z Register a FakeTensor implementation for this custom op. 2025-07-17T07:11:36.1364514Z 2025-07-17T07:11:36.1364759Z This is necessary to get the operator to work efficiently with torch.compile. 2025-07-17T07:11:36.1364850Z 2025-07-17T07:11:36.1365066Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-07-17T07:11:36.1365291Z specifies the behavior of this operator on Tensors that carry no data. 2025-07-17T07:11:36.1365450Z Given some input Tensors with certain properties 2025-07-17T07:11:36.1365708Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-07-17T07:11:36.1365808Z the output Tensors are. 2025-07-17T07:11:36.1365897Z 2025-07-17T07:11:36.1366096Z Please see :func:`torch.library.impl_abstract` for more details. 2025-07-17T07:11:36.1366184Z 2025-07-17T07:11:36.1366260Z Args: 2025-07-17T07:11:36.1366429Z fn (Callable): The function to register as the FakeTensor 2025-07-17T07:11:36.1366535Z implementation. 2025-07-17T07:11:36.1366608Z 2025-07-17T07:11:36.1366691Z Examples: 2025-07-17T07:11:36.1366795Z >>> import torch 2025-07-17T07:11:36.1366892Z >>> import numpy as np 2025-07-17T07:11:36.1366995Z >>> from torch import Tensor 2025-07-17T07:11:36.1367088Z >>> 2025-07-17T07:11:36.1367276Z >>> # Example 1: an operator without data-dependent output shape 2025-07-17T07:11:36.1367501Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-07-17T07:11:36.1367693Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-07-17T07:11:36.1367808Z >>> return (x @ weight.t()) + bias 2025-07-17T07:11:36.1367902Z >>> 2025-07-17T07:11:36.1368000Z >>> @linear.register_fake 2025-07-17T07:11:36.1368102Z >>> def _(x, weight, bias): 2025-07-17T07:11:36.1368213Z >>> assert x.dim() == 2 2025-07-17T07:11:36.1368322Z >>> assert weight.dim() == 2 2025-07-17T07:11:36.1368427Z >>> assert bias.dim() == 1 2025-07-17T07:11:36.1368563Z >>> assert x.shape[1] == weight.shape[1] 2025-07-17T07:11:36.1368691Z >>> assert weight.shape[0] == bias.shape[0] 2025-07-17T07:11:36.1368821Z >>> assert x.device == weight.device 2025-07-17T07:11:36.1368990Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-07-17T07:11:36.1369068Z >>> 2025-07-17T07:11:36.1369180Z >>> x = torch.randn(2, 2) 2025-07-17T07:11:36.1369334Z >>> weight = torch.randn(2, 2) 2025-07-17T07:11:36.1369435Z >>> bias = torch.randn(2) 2025-07-17T07:11:36.1369580Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T07:11:36.1369768Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-07-17T07:11:36.1369910Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T07:11:36.1370154Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-07-17T07:11:36.1370232Z >>> 2025-07-17T07:11:36.1370418Z >>> # Example 2: an operator with data-dependent output shape 2025-07-17T07:11:36.1370611Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-07-17T07:11:36.1370734Z >>> def nonzero(x: Tensor) -> Tensor: 2025-07-17T07:11:36.1370846Z >>> x_np = x.cpu().numpy() 2025-07-17T07:11:36.1370980Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-07-17T07:11:36.1371119Z >>> return torch.tensor(res, device=x.device) 2025-07-17T07:11:36.1371198Z >>> 2025-07-17T07:11:36.1371303Z >>> @nonzero.register_fake 2025-07-17T07:11:36.1371399Z >>> def _(x): 2025-07-17T07:11:36.1371545Z >>> # Number of nonzero-elements is data-dependent. 2025-07-17T07:11:36.1371708Z >>> # Since we cannot peek at the data in an abstract impl, 2025-07-17T07:11:36.1372021Z >>> # we use the ctx object to construct a new symint that 2025-07-17T07:11:36.1372147Z >>> # represents the data-dependent size. 2025-07-17T07:11:36.1372267Z >>> ctx = torch.library.get_ctx() 2025-07-17T07:11:36.1372390Z >>> nnz = ctx.new_dynamic_size() 2025-07-17T07:11:36.1372498Z >>> shape = [nnz, x.dim()] 2025-07-17T07:11:36.1372656Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-07-17T07:11:36.1372750Z >>> return result 2025-07-17T07:11:36.1372828Z >>> 2025-07-17T07:11:36.1372951Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-07-17T07:11:36.1373081Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T07:11:36.1373225Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-07-17T07:11:36.1373363Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-07-17T07:11:36.1373490Z >>> assert torch.allclose(out, x.nonzero()) 2025-07-17T07:11:36.1373565Z 2025-07-17T07:11:36.1373649Z 2025-07-17T07:11:36.1374178Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-07-17T07:11:36.1374319Z 2025-07-17T07:11:36.1374396Z _._ = None 2025-07-17T07:11:36.1374471Z ^ 2025-07-17T07:11:36.1374574Z warnings.warn(msg) 2025-07-17T07:11:36.1374651Z 2025-07-17T07:11:36.1374850Z --- Parse Warning: 24 / 136 --- 2025-07-17T07:11:36.1375923Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_profile.py line=94. 2025-07-17T07:11:36.1376188Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1376278Z 2025-07-17T07:11:36.1376502Z Registers a fake kernel based on the given operator profiles. This fake 2025-07-17T07:11:36.1376746Z kernel registration will override any existing fake kernel registrations. 2025-07-17T07:11:36.1376874Z 2025-07-17T07:11:36.1377084Z The input is a dictionary mapping operator names to a set of operator 2025-07-17T07:11:36.1377320Z profiles, which we will use to generate fake kernels. The operator profiles 2025-07-17T07:11:36.1377588Z are a record of the input and output tensor metadata. Based on this 2025-07-17T07:11:36.1377826Z information we will match a given input to the recorded profile, and return 2025-07-17T07:11:36.1378060Z an output with the same metadata as in the recorded profile. If a profile 2025-07-17T07:11:36.1378199Z doesn't exist then an exception will be thrown. 2025-07-17T07:11:36.1378277Z 2025-07-17T07:11:36.1378518Z The fake kernel generation is considered unsafe because it relies on the 2025-07-17T07:11:36.1378742Z rigid, pre-defined operator profiles that do not account for potential 2025-07-17T07:11:36.1378988Z variations in output behavior. Specifically, the generated kernels assume a 2025-07-17T07:11:36.1379255Z fixed relationship between input and output ranks. However, in reality, it's 2025-07-17T07:11:36.1379493Z possible that data-dependent operations may produce outputs of different 2025-07-17T07:11:36.1379725Z ranks even when given inputs of the same rank. The generated fake kernels 2025-07-17T07:11:36.1379936Z are inflexible and unable to accommodate these nuances, making them 2025-07-17T07:11:36.1380031Z potentially unsafe. 2025-07-17T07:11:36.1380118Z 2025-07-17T07:11:36.1380198Z Args: 2025-07-17T07:11:36.1380410Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-07-17T07:11:36.1380616Z name to a set of operator profiles from which we will generate fake 2025-07-17T07:11:36.1380699Z kernels. 2025-07-17T07:11:36.1380775Z 2025-07-17T07:11:36.1380866Z Examples: 2025-07-17T07:11:36.1380943Z 2025-07-17T07:11:36.1381108Z >>> # Example: Registering an op-profile from draft-export 2025-07-17T07:11:36.1381214Z >>> import torch 2025-07-17T07:11:36.1381379Z >>> from torch.export._draft_export import draft_export 2025-07-17T07:11:36.1381477Z >>> 2025-07-17T07:11:36.1381655Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-07-17T07:11:36.1381777Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-07-17T07:11:36.1381874Z >>> return x + y 2025-07-17T07:11:36.1381956Z >>> 2025-07-17T07:11:36.1382060Z >>> class M(torch.nn.Module): 2025-07-17T07:11:36.1382175Z >>> def forward(self, a, b): 2025-07-17T07:11:36.1382316Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-07-17T07:11:36.1382404Z >>> return res 2025-07-17T07:11:36.1382496Z >>> 2025-07-17T07:11:36.1382663Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-07-17T07:11:36.1382749Z >>> 2025-07-17T07:11:36.1383045Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-07-17T07:11:36.1383201Z >>> decomp = ep.run_decompositions() 2025-07-17T07:11:36.1383288Z 2025-07-17T07:11:36.1383364Z 2025-07-17T07:11:36.1383610Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1383699Z 2025-07-17T07:11:36.1383791Z warnings.warn(msg) 2025-07-17T07:11:36.1383867Z 2025-07-17T07:11:36.1384069Z --- Parse Warning: 25 / 136 --- 2025-07-17T07:11:36.1384927Z /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. 2025-07-17T07:11:36.1385191Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1385438Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-07-17T07:11:36.1385519Z 2025-07-17T07:11:36.1385756Z This is a more structured way of using triton kernels with PyTorch. 2025-07-17T07:11:36.1386008Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-07-17T07:11:36.1386304Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-07-17T07:11:36.1386408Z that is simpler; 2025-07-17T07:11:36.1386659Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-07-17T07:11:36.1386896Z want to create an operator that behaves like PyTorch built-in operators. 2025-07-17T07:11:36.1387104Z For example, you may use a ``torch.library`` wrapper API to define the 2025-07-17T07:11:36.1387317Z behavior of the triton kernel when passed a tensor subclass or under 2025-07-17T07:11:36.1387431Z a TorchDispatchMode. 2025-07-17T07:11:36.1387512Z 2025-07-17T07:11:36.1387761Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-07-17T07:11:36.1387875Z when the implementation 2025-07-17T07:11:36.1388090Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-07-17T07:11:36.1388269Z custom operators as opaque (:func:`torch.compile` and 2025-07-17T07:11:36.1388501Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-07-17T07:11:36.1388720Z makes the implementation visible to these subsystems, allowing them 2025-07-17T07:11:36.1388845Z to optimize the triton kernel(s). 2025-07-17T07:11:36.1388921Z 2025-07-17T07:11:36.1389109Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-07-17T07:11:36.1389341Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-07-17T07:11:36.1389529Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-07-17T07:11:36.1389609Z 2025-07-17T07:11:36.1389703Z Args: 2025-07-17T07:11:36.1389922Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-07-17T07:11:36.1390146Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-07-17T07:11:36.1390306Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-07-17T07:11:36.1390539Z To avoid name collisions, please use your project name as the namespace; 2025-07-17T07:11:36.1390746Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-07-17T07:11:36.1391013Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-07-17T07:11:36.1391252Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-07-17T07:11:36.1391510Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-07-17T07:11:36.1391690Z schema (None | str): A schema string for the operator. If None 2025-07-17T07:11:36.1391902Z (recommended) we'll infer a schema for the operator from its type 2025-07-17T07:11:36.1392129Z annotations. We recommend letting us infer a schema unless you 2025-07-17T07:11:36.1392240Z have a specific reason not to. 2025-07-17T07:11:36.1392401Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-07-17T07:11:36.1392476Z 2025-07-17T07:11:36.1392573Z Example:: 2025-07-17T07:11:36.1392650Z 2025-07-17T07:11:36.1392786Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:36.1392887Z >>> import torch 2025-07-17T07:11:36.1393033Z >>> from torch.library import triton_op, wrap_triton 2025-07-17T07:11:36.1393109Z >>> 2025-07-17T07:11:36.1393210Z >>> import triton 2025-07-17T07:11:36.1393324Z >>> from triton import language as tl 2025-07-17T07:11:36.1393399Z >>> 2025-07-17T07:11:36.1393493Z >>> @triton.jit 2025-07-17T07:11:36.1393581Z >>> def add_kernel( 2025-07-17T07:11:36.1393694Z >>> in_ptr0, 2025-07-17T07:11:36.1393789Z >>> in_ptr1, 2025-07-17T07:11:36.1393873Z >>> out_ptr, 2025-07-17T07:11:36.1393966Z >>> n_elements, 2025-07-17T07:11:36.1394126Z >>> BLOCK_SIZE: "tl.constexpr", 2025-07-17T07:11:36.1394204Z >>> ): 2025-07-17T07:11:36.1394322Z >>> pid = tl.program_id(axis=0) 2025-07-17T07:11:36.1394432Z >>> block_start = pid * BLOCK_SIZE 2025-07-17T07:11:36.1394576Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-07-17T07:11:36.1394689Z >>> mask = offsets < n_elements 2025-07-17T07:11:36.1394815Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-07-17T07:11:36.1394935Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-07-17T07:11:36.1395038Z >>> output = x + y 2025-07-17T07:11:36.1395176Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-07-17T07:11:36.1395267Z >>> 2025-07-17T07:11:36.1395393Z >>> @triton_op("mylib::add", mutates_args={}) 2025-07-17T07:11:36.1395568Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-07-17T07:11:36.1395691Z >>> output = torch.empty_like(x) 2025-07-17T07:11:36.1395800Z >>> n_elements = output.numel() 2025-07-17T07:11:36.1395876Z >>> 2025-07-17T07:11:36.1395975Z >>> def grid(meta): 2025-07-17T07:11:36.1396143Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-07-17T07:11:36.1396221Z >>> 2025-07-17T07:11:36.1396410Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-07-17T07:11:36.1396595Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-07-17T07:11:36.1396702Z >>> return output 2025-07-17T07:11:36.1396781Z >>> 2025-07-17T07:11:36.1396876Z >>> @torch.compile 2025-07-17T07:11:36.1396978Z >>> def f(x, y): 2025-07-17T07:11:36.1397074Z >>> return add(x, y) 2025-07-17T07:11:36.1397151Z >>> 2025-07-17T07:11:36.1397278Z >>> x = torch.randn(3, device="cuda") 2025-07-17T07:11:36.1397391Z >>> y = torch.randn(3, device="cuda") 2025-07-17T07:11:36.1397468Z >>> 2025-07-17T07:11:36.1397567Z >>> z = f(x, y) 2025-07-17T07:11:36.1397681Z >>> assert torch.allclose(z, x + y) 2025-07-17T07:11:36.1397769Z 2025-07-17T07:11:36.1397846Z 2025-07-17T07:11:36.1398096Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1398182Z 2025-07-17T07:11:36.1398277Z warnings.warn(msg) 2025-07-17T07:11:36.1398355Z 2025-07-17T07:11:36.1398548Z --- Parse Warning: 26 / 136 --- 2025-07-17T07:11:36.1399411Z /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. 2025-07-17T07:11:36.1399700Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1399898Z Allows capture of a triton kernel into a graph via make_fx or 2025-07-17T07:11:36.1400003Z non-strict ``torch.export``. 2025-07-17T07:11:36.1400085Z 2025-07-17T07:11:36.1400264Z These technologies perform Dispatcher-based tracing (via 2025-07-17T07:11:36.1400461Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-07-17T07:11:36.1400660Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-07-17T07:11:36.1400770Z can actually be traced into a graph. 2025-07-17T07:11:36.1400844Z 2025-07-17T07:11:36.1401053Z Please use this API together with :func:`torch.library.triton_op`. 2025-07-17T07:11:36.1401128Z 2025-07-17T07:11:36.1401240Z Examples: 2025-07-17T07:11:36.1401321Z 2025-07-17T07:11:36.1401412Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1401510Z >>> import torch 2025-07-17T07:11:36.1401596Z >>> import triton 2025-07-17T07:11:36.1401757Z >>> from triton import language as tl 2025-07-17T07:11:36.1401944Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-07-17T07:11:36.1402070Z >>> from torch.library import wrap_triton 2025-07-17T07:11:36.1402150Z >>> 2025-07-17T07:11:36.1402246Z >>> @triton.jit 2025-07-17T07:11:36.1402334Z >>> def add_kernel( 2025-07-17T07:11:36.1402418Z >>> in_ptr0, 2025-07-17T07:11:36.1402513Z >>> in_ptr1, 2025-07-17T07:11:36.1402596Z >>> out_ptr, 2025-07-17T07:11:36.1402682Z >>> n_elements, 2025-07-17T07:11:36.1402798Z >>> BLOCK_SIZE: "tl.constexpr", 2025-07-17T07:11:36.1402878Z >>> ): 2025-07-17T07:11:36.1403000Z >>> pid = tl.program_id(axis=0) 2025-07-17T07:11:36.1403111Z >>> block_start = pid * BLOCK_SIZE 2025-07-17T07:11:36.1403256Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-07-17T07:11:36.1403377Z >>> mask = offsets < n_elements 2025-07-17T07:11:36.1403504Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-07-17T07:11:36.1403630Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-07-17T07:11:36.1403736Z >>> output = x + y 2025-07-17T07:11:36.1403877Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-07-17T07:11:36.1403956Z >>> 2025-07-17T07:11:36.1404057Z >>> def add(x, y): 2025-07-17T07:11:36.1404173Z >>> output = torch.empty_like(x) 2025-07-17T07:11:36.1404291Z >>> n_elements = output.numel() 2025-07-17T07:11:36.1404370Z >>> 2025-07-17T07:11:36.1404470Z >>> def grid_fn(meta): 2025-07-17T07:11:36.1404650Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-07-17T07:11:36.1404728Z >>> 2025-07-17T07:11:36.1404922Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-07-17T07:11:36.1405024Z >>> return output 2025-07-17T07:11:36.1405100Z >>> 2025-07-17T07:11:36.1405209Z >>> x = torch.randn(3, device="cuda") 2025-07-17T07:11:36.1405324Z >>> y = torch.randn(3, device="cuda") 2025-07-17T07:11:36.1405420Z >>> gm = make_fx(add)(x, y) 2025-07-17T07:11:36.1405517Z >>> print(gm.code) 2025-07-17T07:11:36.1405620Z >>> # def forward(self, x_1, y_1): 2025-07-17T07:11:36.1405851Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-07-17T07:11:36.1406092Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-07-17T07:11:36.1406217Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-07-17T07:11:36.1406350Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-07-17T07:11:36.1406509Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-07-17T07:11:36.1406630Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-07-17T07:11:36.1406723Z >>> # }) 2025-07-17T07:11:36.1406821Z >>> # return empty_like 2025-07-17T07:11:36.1406895Z 2025-07-17T07:11:36.1406981Z 2025-07-17T07:11:36.1407236Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1407315Z 2025-07-17T07:11:36.1407417Z warnings.warn(msg) 2025-07-17T07:11:36.1407490Z 2025-07-17T07:11:36.1407671Z --- Parse Warning: 27 / 136 --- 2025-07-17T07:11:36.1408615Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=286. 2025-07-17T07:11:36.1408903Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1408986Z 2025-07-17T07:11:36.1409258Z Test if two objects are equal, and print an error message if test fails. 2025-07-17T07:11:36.1409332Z 2025-07-17T07:11:36.1409486Z The test is performed with ``actual == desired``. 2025-07-17T07:11:36.1409561Z 2025-07-17T07:11:36.1409640Z Parameters 2025-07-17T07:11:36.1409732Z ---------- 2025-07-17T07:11:36.1409821Z test_string : str 2025-07-17T07:11:36.1409939Z The message supplied to AssertionError. 2025-07-17T07:11:36.1410038Z actual : object 2025-07-17T07:11:36.1410186Z The object to test for equality against `desired`. 2025-07-17T07:11:36.1410269Z desired : object 2025-07-17T07:11:36.1410375Z The expected result. 2025-07-17T07:11:36.1410453Z 2025-07-17T07:11:36.1410542Z Examples 2025-07-17T07:11:36.1410624Z -------- 2025-07-17T07:11:36.1410734Z >>> np.testing.print_assert_equal( 2025-07-17T07:11:36.1410853Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-07-17T07:11:36.1410951Z ... ) # doctest: +SKIP 2025-07-17T07:11:36.1411059Z >>> np.testing.print_assert_equal( 2025-07-17T07:11:36.1411177Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-07-17T07:11:36.1411272Z ... ) # doctest: +SKIP 2025-07-17T07:11:36.1411379Z Traceback (most recent call last): 2025-07-17T07:11:36.1411468Z ... 2025-07-17T07:11:36.1411597Z AssertionError: Test XYZ of func xyz failed 2025-07-17T07:11:36.1411678Z ACTUAL: 2025-07-17T07:11:36.1411765Z [0, 1] 2025-07-17T07:11:36.1411852Z DESIRED: 2025-07-17T07:11:36.1411941Z [0, 2] 2025-07-17T07:11:36.1412019Z 2025-07-17T07:11:36.1412095Z 2025-07-17T07:11:36.1412360Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1412438Z 2025-07-17T07:11:36.1412535Z warnings.warn(msg) 2025-07-17T07:11:36.1412616Z 2025-07-17T07:11:36.1412796Z --- Parse Warning: 28 / 136 --- 2025-07-17T07:11:36.1413742Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=331. 2025-07-17T07:11:36.1414014Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1414091Z 2025-07-17T07:11:36.1414301Z Raises an AssertionError if two items are not equal up to desired 2025-07-17T07:11:36.1414386Z precision. 2025-07-17T07:11:36.1414462Z 2025-07-17T07:11:36.1414636Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T07:11:36.1414812Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T07:11:36.1414996Z instead of this function for more consistent floating point 2025-07-17T07:11:36.1415118Z comparisons. 2025-07-17T07:11:36.1415275Z 2025-07-17T07:11:36.1415488Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-07-17T07:11:36.1415575Z 2025-07-17T07:11:36.1415730Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-07-17T07:11:36.1415810Z 2025-07-17T07:11:36.1416034Z That is a looser test than originally documented, but agrees with what the 2025-07-17T07:11:36.1416261Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-07-17T07:11:36.1416496Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-07-17T07:11:36.1416610Z delegates to assert_array_almost_equal 2025-07-17T07:11:36.1416686Z 2025-07-17T07:11:36.1416775Z Parameters 2025-07-17T07:11:36.1416856Z ---------- 2025-07-17T07:11:36.1416943Z actual : array_like 2025-07-17T07:11:36.1417072Z The object to check. 2025-07-17T07:11:36.1417166Z desired : array_like 2025-07-17T07:11:36.1417259Z The expected object. 2025-07-17T07:11:36.1417358Z decimal : int, optional 2025-07-17T07:11:36.1417472Z Desired precision, default is 7. 2025-07-17T07:11:36.1417617Z err_msg : str, optional 2025-07-17T07:11:36.1417772Z The error message to be printed in case of failure. 2025-07-17T07:11:36.1417867Z verbose : bool, optional 2025-07-17T07:11:36.1418075Z If True, the conflicting values are appended to the error message. 2025-07-17T07:11:36.1418150Z 2025-07-17T07:11:36.1418230Z Raises 2025-07-17T07:11:36.1418318Z ------ 2025-07-17T07:11:36.1418405Z AssertionError 2025-07-17T07:11:36.1418598Z If actual and desired are not equal up to specified precision. 2025-07-17T07:11:36.1418683Z 2025-07-17T07:11:36.1418762Z See Also 2025-07-17T07:11:36.1418842Z -------- 2025-07-17T07:11:36.1419080Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T07:11:36.1419210Z relative and/or absolute precision. 2025-07-17T07:11:36.1419419Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T07:11:36.1419495Z 2025-07-17T07:11:36.1419577Z Examples 2025-07-17T07:11:36.1419665Z -------- 2025-07-17T07:11:36.1419825Z >>> from torch._numpy.testing import assert_almost_equal 2025-07-17T07:11:36.1419951Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-07-17T07:11:36.1420126Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-07-17T07:11:36.1420231Z Traceback (most recent call last): 2025-07-17T07:11:36.1420308Z ... 2025-07-17T07:11:36.1420401Z AssertionError: 2025-07-17T07:11:36.1420518Z Arrays are not almost equal to 10 decimals 2025-07-17T07:11:36.1420613Z ACTUAL: 2.3333333333333 2025-07-17T07:11:36.1420699Z DESIRED: 2.33333334 2025-07-17T07:11:36.1420772Z 2025-07-17T07:11:36.1420871Z >>> assert_almost_equal( 2025-07-17T07:11:36.1421072Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-07-17T07:11:36.1421149Z ... ) 2025-07-17T07:11:36.1421262Z Traceback (most recent call last): 2025-07-17T07:11:36.1421336Z ... 2025-07-17T07:11:36.1421426Z AssertionError: 2025-07-17T07:11:36.1421550Z Arrays are not almost equal to 9 decimals 2025-07-17T07:11:36.1421632Z 2025-07-17T07:11:36.1421734Z Mismatched elements: 1 / 2 (50%) 2025-07-17T07:11:36.1421865Z Max absolute difference: 6.666699636781459e-09 2025-07-17T07:11:36.1421990Z Max relative difference: 2.8571569790287484e-09 2025-07-17T07:11:36.1422132Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-07-17T07:11:36.1422262Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-07-17T07:11:36.1422339Z 2025-07-17T07:11:36.1422420Z 2025-07-17T07:11:36.1422667Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1422744Z 2025-07-17T07:11:36.1422867Z warnings.warn(msg) 2025-07-17T07:11:36.1422941Z 2025-07-17T07:11:36.1423134Z --- Parse Warning: 29 / 136 --- 2025-07-17T07:11:36.1424088Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=457. 2025-07-17T07:11:36.1424344Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1424431Z 2025-07-17T07:11:36.1424647Z Raises an AssertionError if two items are not equal up to significant 2025-07-17T07:11:36.1424725Z digits. 2025-07-17T07:11:36.1424810Z 2025-07-17T07:11:36.1424977Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T07:11:36.1425151Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T07:11:36.1425372Z instead of this function for more consistent floating point 2025-07-17T07:11:36.1425464Z comparisons. 2025-07-17T07:11:36.1425539Z 2025-07-17T07:11:36.1425727Z Given two numbers, check that they are approximately equal. 2025-07-17T07:11:36.1425987Z Approximately equal is defined as the number of significant digits 2025-07-17T07:11:36.1426079Z that agree. 2025-07-17T07:11:36.1426152Z 2025-07-17T07:11:36.1426234Z Parameters 2025-07-17T07:11:36.1426325Z ---------- 2025-07-17T07:11:36.1426409Z actual : scalar 2025-07-17T07:11:36.1426503Z The object to check. 2025-07-17T07:11:36.1426595Z desired : scalar 2025-07-17T07:11:36.1426684Z The expected object. 2025-07-17T07:11:36.1426782Z significant : int, optional 2025-07-17T07:11:36.1426900Z Desired precision, default is 7. 2025-07-17T07:11:36.1426989Z err_msg : str, optional 2025-07-17T07:11:36.1427140Z The error message to be printed in case of failure. 2025-07-17T07:11:36.1427249Z verbose : bool, optional 2025-07-17T07:11:36.1427450Z If True, the conflicting values are appended to the error message. 2025-07-17T07:11:36.1427536Z 2025-07-17T07:11:36.1427615Z Raises 2025-07-17T07:11:36.1427693Z ------ 2025-07-17T07:11:36.1427792Z AssertionError 2025-07-17T07:11:36.1427978Z If actual and desired are not equal up to specified precision. 2025-07-17T07:11:36.1428052Z 2025-07-17T07:11:36.1428139Z See Also 2025-07-17T07:11:36.1428217Z -------- 2025-07-17T07:11:36.1428450Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T07:11:36.1428582Z relative and/or absolute precision. 2025-07-17T07:11:36.1428783Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T07:11:36.1428855Z 2025-07-17T07:11:36.1428950Z Examples 2025-07-17T07:11:36.1429026Z -------- 2025-07-17T07:11:36.1429144Z >>> np.testing.assert_approx_equal( 2025-07-17T07:11:36.1429247Z ... 0.12345677777777e-20, 0.1234567e-20 2025-07-17T07:11:36.1429337Z ... ) # doctest: +SKIP 2025-07-17T07:11:36.1429456Z >>> np.testing.assert_approx_equal( 2025-07-17T07:11:36.1429542Z ... 0.12345670e-20, 2025-07-17T07:11:36.1429646Z ... 0.12345671e-20, # doctest: +SKIP 2025-07-17T07:11:36.1429745Z ... significant=8, 2025-07-17T07:11:36.1429823Z ... ) 2025-07-17T07:11:36.1429928Z >>> np.testing.assert_approx_equal( 2025-07-17T07:11:36.1430019Z ... 0.12345670e-20, 2025-07-17T07:11:36.1430120Z ... 0.12345672e-20, # doctest: +SKIP 2025-07-17T07:11:36.1430210Z ... significant=8, 2025-07-17T07:11:36.1430303Z ... ) 2025-07-17T07:11:36.1430409Z Traceback (most recent call last): 2025-07-17T07:11:36.1430496Z ... 2025-07-17T07:11:36.1430585Z AssertionError: 2025-07-17T07:11:36.1430709Z Items are not equal to 8 significant digits: 2025-07-17T07:11:36.1430805Z ACTUAL: 1.234567e-21 2025-07-17T07:11:36.1430895Z DESIRED: 1.2345672e-21 2025-07-17T07:11:36.1430994Z 2025-07-17T07:11:36.1431160Z the evaluated condition that raises the exception is 2025-07-17T07:11:36.1431234Z 2025-07-17T07:11:36.1431418Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-07-17T07:11:36.1431504Z True 2025-07-17T07:11:36.1431578Z 2025-07-17T07:11:36.1431650Z 2025-07-17T07:11:36.1431906Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1431982Z 2025-07-17T07:11:36.1432079Z warnings.warn(msg) 2025-07-17T07:11:36.1432155Z 2025-07-17T07:11:36.1432337Z --- Parse Warning: 30 / 136 --- 2025-07-17T07:11:36.1433267Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=744. 2025-07-17T07:11:36.1433526Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1433632Z 2025-07-17T07:11:36.1433838Z Raises an AssertionError if two array_like objects are not equal. 2025-07-17T07:11:36.1433914Z 2025-07-17T07:11:36.1434160Z Given two array_like objects, check that the shape is equal and all 2025-07-17T07:11:36.1434382Z elements of these objects are equal (but see the Notes for the special 2025-07-17T07:11:36.1434578Z handling of a scalar). An exception is raised at shape mismatch or 2025-07-17T07:11:36.1434799Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-07-17T07:11:36.1435010Z are compared like numbers, no assertion is raised if both objects have 2025-07-17T07:11:36.1435107Z NaNs in the same positions. 2025-07-17T07:11:36.1435184Z 2025-07-17T07:11:36.1435402Z The usual caution for verifying equality with floating point numbers is 2025-07-17T07:11:36.1435478Z advised. 2025-07-17T07:11:36.1435556Z 2025-07-17T07:11:36.1435639Z Parameters 2025-07-17T07:11:36.1435719Z ---------- 2025-07-17T07:11:36.1435806Z x : array_like 2025-07-17T07:11:36.1435905Z The actual object to check. 2025-07-17T07:11:36.1435993Z y : array_like 2025-07-17T07:11:36.1436099Z The desired, expected object. 2025-07-17T07:11:36.1436194Z err_msg : str, optional 2025-07-17T07:11:36.1436351Z The error message to be printed in case of failure. 2025-07-17T07:11:36.1436445Z verbose : bool, optional 2025-07-17T07:11:36.1436641Z If True, the conflicting values are appended to the error message. 2025-07-17T07:11:36.1436742Z strict : bool, optional 2025-07-17T07:11:36.1436934Z If True, raise an AssertionError when either the shape or the data 2025-07-17T07:11:36.1437105Z type of the array_like objects does not match. The special 2025-07-17T07:11:36.1437312Z handling for scalars mentioned in the Notes section is disabled. 2025-07-17T07:11:36.1437386Z 2025-07-17T07:11:36.1437471Z Raises 2025-07-17T07:11:36.1437549Z ------ 2025-07-17T07:11:36.1437634Z AssertionError 2025-07-17T07:11:36.1437768Z If actual and desired objects are not equal. 2025-07-17T07:11:36.1437842Z 2025-07-17T07:11:36.1437920Z See Also 2025-07-17T07:11:36.1438006Z -------- 2025-07-17T07:11:36.1438234Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T07:11:36.1438355Z relative and/or absolute precision. 2025-07-17T07:11:36.1438564Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T07:11:36.1438639Z 2025-07-17T07:11:36.1438715Z Notes 2025-07-17T07:11:36.1438799Z ----- 2025-07-17T07:11:36.1438976Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-07-17T07:11:36.1439197Z function checks that each element of the array_like object is equal to 2025-07-17T07:11:36.1439413Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-07-17T07:11:36.1439492Z 2025-07-17T07:11:36.1439610Z Examples 2025-07-17T07:11:36.1439688Z -------- 2025-07-17T07:11:36.1439818Z The first assert does not raise an exception: 2025-07-17T07:11:36.1439904Z 2025-07-17T07:11:36.1440016Z >>> np.testing.assert_array_equal( 2025-07-17T07:11:36.1440154Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-07-17T07:11:36.1440240Z ... ) 2025-07-17T07:11:36.1440313Z 2025-07-17T07:11:36.1440534Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-07-17T07:11:36.1440651Z functions for these cases instead: 2025-07-17T07:11:36.1440723Z 2025-07-17T07:11:36.1440840Z >>> np.testing.assert_allclose( 2025-07-17T07:11:36.1441031Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-07-17T07:11:36.1441106Z ... ) 2025-07-17T07:11:36.1441190Z 2025-07-17T07:11:36.1441388Z As mentioned in the Notes section, `assert_array_equal` has special 2025-07-17T07:11:36.1441651Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-07-17T07:11:36.1441736Z 2025-07-17T07:11:36.1441837Z >>> x = np.full((2, 5), fill_value=3) 2025-07-17T07:11:36.1442015Z >>> np.testing.assert_array_equal(x, 3) 2025-07-17T07:11:36.1442106Z 2025-07-17T07:11:36.1442319Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-07-17T07:11:36.1442411Z array: 2025-07-17T07:11:36.1442487Z 2025-07-17T07:11:36.1442633Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-07-17T07:11:36.1442755Z Traceback (most recent call last): 2025-07-17T07:11:36.1442834Z ... 2025-07-17T07:11:36.1442924Z AssertionError: 2025-07-17T07:11:36.1443026Z Arrays are not equal 2025-07-17T07:11:36.1443109Z 2025-07-17T07:11:36.1443206Z (shapes (2, 5), () mismatch) 2025-07-17T07:11:36.1443324Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-07-17T07:11:36.1443413Z [3, 3, 3, 3, 3]]) 2025-07-17T07:11:36.1443512Z y: torch.ndarray(3) 2025-07-17T07:11:36.1443607Z 2025-07-17T07:11:36.1443817Z The `strict` parameter also ensures that the array data types match: 2025-07-17T07:11:36.1443893Z 2025-07-17T07:11:36.1444001Z >>> x = np.array([2, 2, 2]) 2025-07-17T07:11:36.1444130Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-07-17T07:11:36.1444288Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-07-17T07:11:36.1444395Z Traceback (most recent call last): 2025-07-17T07:11:36.1444475Z ... 2025-07-17T07:11:36.1444577Z AssertionError: 2025-07-17T07:11:36.1444667Z Arrays are not equal 2025-07-17T07:11:36.1444748Z 2025-07-17T07:11:36.1444902Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-07-17T07:11:36.1445000Z x: torch.ndarray([2, 2, 2]) 2025-07-17T07:11:36.1445098Z y: torch.ndarray([2., 2., 2.]) 2025-07-17T07:11:36.1445185Z 2025-07-17T07:11:36.1445431Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1445509Z 2025-07-17T07:11:36.1445614Z warnings.warn(msg) 2025-07-17T07:11:36.1445689Z 2025-07-17T07:11:36.1445884Z --- Parse Warning: 31 / 136 --- 2025-07-17T07:11:36.1446852Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=851. 2025-07-17T07:11:36.1447117Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1447206Z 2025-07-17T07:11:36.1447415Z Raises an AssertionError if two objects are not equal up to desired 2025-07-17T07:11:36.1447500Z precision. 2025-07-17T07:11:36.1447588Z 2025-07-17T07:11:36.1447758Z .. note:: It is recommended to use one of `assert_allclose`, 2025-07-17T07:11:36.1447946Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-07-17T07:11:36.1448161Z instead of this function for more consistent floating point 2025-07-17T07:11:36.1448250Z comparisons. 2025-07-17T07:11:36.1448336Z 2025-07-17T07:11:36.1448572Z The test verifies identical shapes and that the elements of ``actual`` and 2025-07-17T07:11:36.1448665Z ``desired`` satisfy. 2025-07-17T07:11:36.1448751Z 2025-07-17T07:11:36.1448876Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-07-17T07:11:36.1448949Z 2025-07-17T07:11:36.1449182Z That is a looser test than originally documented, but agrees with what the 2025-07-17T07:11:36.1449415Z actual implementation did up to rounding vagaries. An exception is raised 2025-07-17T07:11:36.1449656Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-07-17T07:11:36.1449872Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-07-17T07:11:36.1449989Z objects have NaNs in the same positions. 2025-07-17T07:11:36.1450105Z 2025-07-17T07:11:36.1450191Z Parameters 2025-07-17T07:11:36.1450271Z ---------- 2025-07-17T07:11:36.1450362Z x : array_like 2025-07-17T07:11:36.1450464Z The actual object to check. 2025-07-17T07:11:36.1450617Z y : array_like 2025-07-17T07:11:36.1450738Z The desired, expected object. 2025-07-17T07:11:36.1450829Z decimal : int, optional 2025-07-17T07:11:36.1450936Z Desired precision, default is 6. 2025-07-17T07:11:36.1451035Z err_msg : str, optional 2025-07-17T07:11:36.1451184Z The error message to be printed in case of failure. 2025-07-17T07:11:36.1451292Z verbose : bool, optional 2025-07-17T07:11:36.1451492Z If True, the conflicting values are appended to the error message. 2025-07-17T07:11:36.1451567Z 2025-07-17T07:11:36.1451654Z Raises 2025-07-17T07:11:36.1451732Z ------ 2025-07-17T07:11:36.1451813Z AssertionError 2025-07-17T07:11:36.1452014Z If actual and desired are not equal up to specified precision. 2025-07-17T07:11:36.1452094Z 2025-07-17T07:11:36.1452173Z See Also 2025-07-17T07:11:36.1452265Z -------- 2025-07-17T07:11:36.1452496Z assert_allclose: Compare two array_like objects for equality with desired 2025-07-17T07:11:36.1452628Z relative and/or absolute precision. 2025-07-17T07:11:36.1452833Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-07-17T07:11:36.1452909Z 2025-07-17T07:11:36.1452998Z Examples 2025-07-17T07:11:36.1453076Z -------- 2025-07-17T07:11:36.1453203Z the first assert does not raise an exception 2025-07-17T07:11:36.1453282Z 2025-07-17T07:11:36.1453531Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-07-17T07:11:36.1453605Z 2025-07-17T07:11:36.1453734Z >>> np.testing.assert_array_almost_equal( 2025-07-17T07:11:36.1453882Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-07-17T07:11:36.1453961Z ... ) 2025-07-17T07:11:36.1454082Z Traceback (most recent call last): 2025-07-17T07:11:36.1454160Z ... 2025-07-17T07:11:36.1454254Z AssertionError: 2025-07-17T07:11:36.1454367Z Arrays are not almost equal to 5 decimals 2025-07-17T07:11:36.1454650Z 2025-07-17T07:11:36.1454806Z Mismatched elements: 1 / 3 (33.3%) 2025-07-17T07:11:36.1454966Z Max absolute difference: 5.999999999994898e-05 2025-07-17T07:11:36.1455270Z Max relative difference: 2.5713661239633743e-05 2025-07-17T07:11:36.1455468Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-07-17T07:11:36.1455680Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-07-17T07:11:36.1455840Z 2025-07-17T07:11:36.1456009Z >>> np.testing.assert_array_almost_equal( 2025-07-17T07:11:36.1456225Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-07-17T07:11:36.1456340Z ... ) 2025-07-17T07:11:36.1456483Z Traceback (most recent call last): 2025-07-17T07:11:36.1456621Z ... 2025-07-17T07:11:36.1456816Z AssertionError: 2025-07-17T07:11:36.1456983Z Arrays are not almost equal to 5 decimals 2025-07-17T07:11:36.1457149Z 2025-07-17T07:11:36.1457288Z x and y nan location mismatch: 2025-07-17T07:11:36.1457525Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-07-17T07:11:36.1457691Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-07-17T07:11:36.1457850Z 2025-07-17T07:11:36.1458026Z 2025-07-17T07:11:36.1458310Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1458420Z 2025-07-17T07:11:36.1458617Z warnings.warn(msg) 2025-07-17T07:11:36.1458707Z 2025-07-17T07:11:36.1458982Z --- Parse Warning: 32 / 136 --- 2025-07-17T07:11:36.1460041Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1848. 2025-07-17T07:11:36.1460367Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1460701Z Context manager that resets warning registry for catching warnings 2025-07-17T07:11:36.1460811Z 2025-07-17T07:11:36.1461075Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-07-17T07:11:36.1461429Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-07-17T07:11:36.1461696Z it impossible to retrigger the warning in this module, whatever you put in 2025-07-17T07:11:36.1462009Z the warnings filters. This context manager accepts a sequence of `modules` 2025-07-17T07:11:36.1462178Z as a keyword argument to its constructor and: 2025-07-17T07:11:36.1462296Z 2025-07-17T07:11:36.1462605Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-07-17T07:11:36.1462743Z on entry; 2025-07-17T07:11:36.1462962Z * resets ``__warningregistry__`` to its previous state on exit. 2025-07-17T07:11:36.1463112Z 2025-07-17T07:11:36.1463368Z This makes it possible to trigger any warning afresh inside the context 2025-07-17T07:11:36.1463613Z manager without disturbing the state of warnings outside. 2025-07-17T07:11:36.1463753Z 2025-07-17T07:11:36.1464036Z For compatibility with Python 3.0, please consider all arguments to be 2025-07-17T07:11:36.1464217Z keyword-only. 2025-07-17T07:11:36.1464326Z 2025-07-17T07:11:36.1464452Z Parameters 2025-07-17T07:11:36.1464591Z ---------- 2025-07-17T07:11:36.1464749Z record : bool, optional 2025-07-17T07:11:36.1464982Z Specifies whether warnings should be captured by a custom 2025-07-17T07:11:36.1465289Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-07-17T07:11:36.1465527Z returned by the context manager. Otherwise None is returned by the 2025-07-17T07:11:36.1465840Z context manager. The objects appended to the list are arguments whose 2025-07-17T07:11:36.1466022Z attributes mirror the arguments to ``showwarning()``. 2025-07-17T07:11:36.1466196Z modules : sequence, optional 2025-07-17T07:11:36.1466505Z Sequence of modules for which to reset warnings registry on entry and 2025-07-17T07:11:36.1466728Z restore on exit. To work correctly, all 'ignore' filters should 2025-07-17T07:11:36.1466920Z filter by one of these modules. 2025-07-17T07:11:36.1467034Z 2025-07-17T07:11:36.1467129Z Examples 2025-07-17T07:11:36.1467340Z -------- 2025-07-17T07:11:36.1467465Z >>> import warnings 2025-07-17T07:11:36.1467680Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-07-17T07:11:36.1467881Z ... modules=[np.core.fromnumeric] 2025-07-17T07:11:36.1467993Z ... ): 2025-07-17T07:11:36.1468129Z ... warnings.simplefilter("always") 2025-07-17T07:11:36.1468507Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-07-17T07:11:36.1468733Z ... # do something that raises a warning but ignore those in 2025-07-17T07:11:36.1468920Z ... # np.core.fromnumeric 2025-07-17T07:11:36.1469032Z 2025-07-17T07:11:36.1469314Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1469481Z 2025-07-17T07:11:36.1469624Z warnings.warn(msg) 2025-07-17T07:11:36.1469741Z 2025-07-17T07:11:36.1470010Z --- Parse Warning: 33 / 136 --- 2025-07-17T07:11:36.1470974Z /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. 2025-07-17T07:11:36.1471291Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1471604Z Applies a 1D convolution over a quantized input signal composed of 2025-07-17T07:11:36.1478909Z several quantized input planes. 2025-07-17T07:11:36.1479039Z 2025-07-17T07:11:36.1479439Z For details on input arguments, parameters, and implementation see 2025-07-17T07:11:36.1479553Z :class:`~torch.nn.Conv1d`. 2025-07-17T07:11:36.1479629Z 2025-07-17T07:11:36.1479718Z .. note:: 2025-07-17T07:11:36.1479922Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-07-17T07:11:36.1480000Z 2025-07-17T07:11:36.1480076Z .. note:: 2025-07-17T07:11:36.1480259Z Only `torch.quint8` is supported for the input data type. 2025-07-17T07:11:36.1480332Z 2025-07-17T07:11:36.1480409Z 2025-07-17T07:11:36.1480498Z Attributes: 2025-07-17T07:11:36.1480706Z weight (Tensor): packed tensor derived from the learnable weight 2025-07-17T07:11:36.1480804Z parameter. 2025-07-17T07:11:36.1480951Z scale (Tensor): scalar for the output scale 2025-07-17T07:11:36.1481108Z zero_point (Tensor): scalar for the output zero point 2025-07-17T07:11:36.1481192Z 2025-07-17T07:11:36.1481341Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-07-17T07:11:36.1481418Z 2025-07-17T07:11:36.1481505Z Examples:: 2025-07-17T07:11:36.1481580Z 2025-07-17T07:11:36.1481720Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-07-17T07:11:36.1481849Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-07-17T07:11:36.1481957Z >>> input = torch.randn(20, 16, 100) 2025-07-17T07:11:36.1482060Z >>> # quantize input to quint8 2025-07-17T07:11:36.1482156Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1482366Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-07-17T07:11:36.1482492Z ... dtype=torch.quint8) 2025-07-17T07:11:36.1482588Z >>> output = m(q_input) 2025-07-17T07:11:36.1482660Z 2025-07-17T07:11:36.1482737Z 2025-07-17T07:11:36.1482988Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1483059Z 2025-07-17T07:11:36.1483153Z warnings.warn(msg) 2025-07-17T07:11:36.1483225Z 2025-07-17T07:11:36.1483453Z --- Parse Warning: 34 / 136 --- 2025-07-17T07:11:36.1484373Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=12. 2025-07-17T07:11:36.1484628Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1484754Z A quantized long short-term memory (LSTM). 2025-07-17T07:11:36.1484824Z 2025-07-17T07:11:36.1485100Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-07-17T07:11:36.1485240Z 2025-07-17T07:11:36.1485321Z Attributes: 2025-07-17T07:11:36.1485437Z layers : instances of the `_LSTMLayer` 2025-07-17T07:11:36.1485518Z 2025-07-17T07:11:36.1485596Z .. note:: 2025-07-17T07:11:36.1485803Z To access the weights and biases, you need to access them per layer. 2025-07-17T07:11:36.1485975Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-07-17T07:11:36.1486048Z 2025-07-17T07:11:36.1486139Z Examples:: 2025-07-17T07:11:36.1486231Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1486330Z >>> custom_module_config = { 2025-07-17T07:11:36.1486465Z ... 'float_to_observed_custom_module_class': { 2025-07-17T07:11:36.1486584Z ... nn.LSTM: nn.quantizable.LSTM, 2025-07-17T07:11:36.1486661Z ... }, 2025-07-17T07:11:36.1486848Z ... 'observed_to_quantized_custom_module_class': { 2025-07-17T07:11:36.1486986Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-07-17T07:11:36.1487064Z ... } 2025-07-17T07:11:36.1487144Z ... } 2025-07-17T07:11:36.1487408Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-07-17T07:11:36.1487625Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-07-17T07:11:36.1487700Z 2025-07-17T07:11:36.1487946Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1488025Z 2025-07-17T07:11:36.1488114Z warnings.warn(msg) 2025-07-17T07:11:36.1488187Z 2025-07-17T07:11:36.1488380Z --- Parse Warning: 35 / 136 --- 2025-07-17T07:11:36.1489577Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-07-17T07:11:36.1489844Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1489917Z 2025-07-17T07:11:36.1490172Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-07-17T07:11:36.1490401Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-07-17T07:11:36.1490643Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-07-17T07:11:36.1490731Z input by the user. 2025-07-17T07:11:36.1490954Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-07-17T07:11:36.1491102Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-07-17T07:11:36.1491174Z 2025-07-17T07:11:36.1491257Z Note:: 2025-07-17T07:11:36.1491575Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-07-17T07:11:36.1491658Z 2025-07-17T07:11:36.1491732Z Args: 2025-07-17T07:11:36.1491822Z model (nn.Module): 2025-07-17T07:11:36.1492046Z The model whose layers will be sparsified. The layers that needs to be 2025-07-17T07:11:36.1492286Z sparsified should be added separately using the register_layer() function 2025-07-17T07:11:36.1492400Z aggregate_fn (Optional, Callable): 2025-07-17T07:11:36.1492659Z default aggregate_fn that is used if not specified while registering the layer. 2025-07-17T07:11:36.1492818Z specifies how inputs should be aggregated over time. 2025-07-17T07:11:36.1493108Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-07-17T07:11:36.1493189Z Example 2025-07-17T07:11:36.1493361Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-07-17T07:11:36.1493487Z reduce_fn (Optional, Callable): 2025-07-17T07:11:36.1493727Z default reduce_fn that is used if not specified while registering the layer. 2025-07-17T07:11:36.1494007Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-07-17T07:11:36.1494126Z calling agg_fn() on all inputs. 2025-07-17T07:11:36.1494211Z Example 2025-07-17T07:11:36.1494405Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-07-17T07:11:36.1494511Z mask_fn (Optional, Callable): 2025-07-17T07:11:36.1494814Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-07-17T07:11:36.1495070Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-07-17T07:11:36.1495260Z register_layer(). 2025-07-17T07:11:36.1495607Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-07-17T07:11:36.1495737Z arguments. 2025-07-17T07:11:36.1495843Z features (Optional, list): 2025-07-17T07:11:36.1495980Z default selected features to sparsify. 2025-07-17T07:11:36.1496290Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-07-17T07:11:36.1496382Z For example, 2025-07-17T07:11:36.1496634Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-07-17T07:11:36.1496740Z feature_dim (Optional, int): 2025-07-17T07:11:36.1497012Z default dimension of input features. Again, features along this dim will be chosen 2025-07-17T07:11:36.1497119Z for sparsification. 2025-07-17T07:11:36.1497215Z sparse_config (Dict): 2025-07-17T07:11:36.1497435Z Default configuration for the mask_fn. This config will be passed 2025-07-17T07:11:36.1497534Z with the mask_fn() 2025-07-17T07:11:36.1497606Z 2025-07-17T07:11:36.1497693Z Example: 2025-07-17T07:11:36.1497782Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1497876Z >>> model = SomeModel() 2025-07-17T07:11:36.1498119Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-07-17T07:11:36.1498219Z >>> # Initialize aggregate_fn 2025-07-17T07:11:36.1498306Z >>> def agg_fn(x, y): 2025-07-17T07:11:36.1498397Z >>> return x + y 2025-07-17T07:11:36.1498473Z >>> 2025-07-17T07:11:36.1498569Z >>> # Initialize reduce_fn 2025-07-17T07:11:36.1498663Z >>> def reduce_fn(x): 2025-07-17T07:11:36.1498769Z >>> return torch.mean(x, dim=0) 2025-07-17T07:11:36.1498849Z >>> 2025-07-17T07:11:36.1498941Z >>> # Initialize mask_fn 2025-07-17T07:11:36.1499032Z >>> def mask_fn(data): 2025-07-17T07:11:36.1499180Z >>> return torch.eye(data.shape).to(data.device) 2025-07-17T07:11:36.1499260Z >>> 2025-07-17T07:11:36.1499337Z >>> 2025-07-17T07:11:36.1499457Z >>> act_sparsifier.register_layer( 2025-07-17T07:11:36.1499550Z ... model.some_layer, 2025-07-17T07:11:36.1499651Z ... aggregate_fn=agg_fn, 2025-07-17T07:11:36.1499751Z ... reduce_fn=reduce_fn, 2025-07-17T07:11:36.1499839Z ... mask_fn=mask_fn, 2025-07-17T07:11:36.1499916Z ... ) 2025-07-17T07:11:36.1499998Z >>> 2025-07-17T07:11:36.1500095Z >>> # start training process 2025-07-17T07:11:36.1500195Z >>> for _ in [...]: 2025-07-17T07:11:36.1500283Z >>> # epoch starts 2025-07-17T07:11:36.1500450Z >>> # model.forward(), compute_loss() and model.backwards() 2025-07-17T07:11:36.1500546Z >>> # epoch ends 2025-07-17T07:11:36.1500651Z >>> act_sparsifier.step() 2025-07-17T07:11:36.1500750Z >>> # end training process 2025-07-17T07:11:36.1500865Z >>> sparsifier.squash_mask() 2025-07-17T07:11:36.1500967Z 2025-07-17T07:11:36.1501218Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1501295Z 2025-07-17T07:11:36.1501382Z warnings.warn(msg) 2025-07-17T07:11:36.1501463Z 2025-07-17T07:11:36.1501669Z --- Parse Warning: 36 / 136 --- 2025-07-17T07:11:36.1502877Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-07-17T07:11:36.1503133Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1503216Z 2025-07-17T07:11:36.1503414Z Abstract method that needs to be implemented by the child class. 2025-07-17T07:11:36.1503663Z The expected return type should is a dictionary of name to schedule_param value 2025-07-17T07:11:36.1503970Z The returned values will be updated in sparsifier when the scheduler step() function 2025-07-17T07:11:36.1504048Z is called. 2025-07-17T07:11:36.1504129Z 2025-07-17T07:11:36.1504255Z Example: 2025-07-17T07:11:36.1504362Z >>> def get_schedule_param(self): 2025-07-17T07:11:36.1504465Z ... new_param = {} 2025-07-17T07:11:36.1504616Z ... for name in self.sparsifier.data_groups.keys(): 2025-07-17T07:11:36.1504710Z ... new_param[name] = ( 2025-07-17T07:11:36.1504919Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-07-17T07:11:36.1504995Z ... ) 2025-07-17T07:11:36.1505085Z ... return new_param 2025-07-17T07:11:36.1505163Z 2025-07-17T07:11:36.1505494Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-07-17T07:11:36.1505583Z would be halved 2025-07-17T07:11:36.1505659Z 2025-07-17T07:11:36.1505905Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1505981Z 2025-07-17T07:11:36.1506072Z warnings.warn(msg) 2025-07-17T07:11:36.1506143Z 2025-07-17T07:11:36.1506329Z --- Parse Warning: 37 / 136 --- 2025-07-17T07:11:36.1507400Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=229. 2025-07-17T07:11:36.1507661Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1507827Z Squashes the sparse masks into the appropriate tensors. 2025-07-17T07:11:36.1507900Z 2025-07-17T07:11:36.1508111Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-07-17T07:11:36.1508288Z the module will have a `sparse_params` dict attached to it. 2025-07-17T07:11:36.1508364Z 2025-07-17T07:11:36.1508447Z Args: 2025-07-17T07:11:36.1508627Z params_to_keep: List of keys to save in the module or a dict 2025-07-17T07:11:36.1508784Z representing the modules and keys that will have 2025-07-17T07:11:36.1508909Z sparsity parameters saved 2025-07-17T07:11:36.1509115Z params_to_keep_per_layer: Dict to specify the params that should be 2025-07-17T07:11:36.1509266Z saved for specific layers. The keys in the dict 2025-07-17T07:11:36.1509415Z should be the module fqn, while the values should 2025-07-17T07:11:36.1509570Z be a list of strings with the names of the variables 2025-07-17T07:11:36.1509695Z to save in the `sparse_params` 2025-07-17T07:11:36.1509769Z 2025-07-17T07:11:36.1509855Z Examples: 2025-07-17T07:11:36.1510018Z >>> # xdoctest: +SKIP("locals are undefined") 2025-07-17T07:11:36.1510131Z >>> # Don't save any sparse params 2025-07-17T07:11:36.1510251Z >>> sparsifier.squash_mask() 2025-07-17T07:11:36.1510386Z >>> hasattr(model.submodule1, "sparse_params") 2025-07-17T07:11:36.1510466Z False 2025-07-17T07:11:36.1510547Z 2025-07-17T07:11:36.1510657Z >>> # Keep sparse params per layer 2025-07-17T07:11:36.1510763Z >>> sparsifier.squash_mask( 2025-07-17T07:11:36.1510878Z ... params_to_keep_per_layer={ 2025-07-17T07:11:36.1511003Z ... "submodule1.linear1": ("foo", "bar"), 2025-07-17T07:11:36.1511128Z ... "submodule2.linear42": ("baz",), 2025-07-17T07:11:36.1511220Z ... } 2025-07-17T07:11:36.1511295Z ... ) 2025-07-17T07:11:36.1511481Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T07:11:36.1511578Z {'foo': 42, 'bar': 24} 2025-07-17T07:11:36.1511733Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T07:11:36.1511830Z {'baz': 0.1} 2025-07-17T07:11:36.1511960Z 2025-07-17T07:11:36.1512080Z >>> # Keep sparse params for all layers 2025-07-17T07:11:36.1512255Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-07-17T07:11:36.1512405Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T07:11:36.1512496Z {'foo': 42, 'bar': 24} 2025-07-17T07:11:36.1512656Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T07:11:36.1512748Z {'foo': 42, 'bar': 24} 2025-07-17T07:11:36.1512830Z 2025-07-17T07:11:36.1513018Z >>> # Keep some sparse params for all layers, and specific ones for 2025-07-17T07:11:36.1513111Z >>> # some other layers 2025-07-17T07:11:36.1513229Z >>> sparsifier.squash_mask( 2025-07-17T07:11:36.1513339Z ... params_to_keep=("foo", "bar"), 2025-07-17T07:11:36.1513532Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-07-17T07:11:36.1513619Z ... ) 2025-07-17T07:11:36.1513768Z >>> print(model.submodule1.linear1.sparse_params) 2025-07-17T07:11:36.1513861Z {'foo': 42, 'bar': 24} 2025-07-17T07:11:36.1514030Z >>> print(model.submodule2.linear42.sparse_params) 2025-07-17T07:11:36.1514135Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-07-17T07:11:36.1514225Z 2025-07-17T07:11:36.1514474Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1514549Z 2025-07-17T07:11:36.1514652Z warnings.warn(msg) 2025-07-17T07:11:36.1514727Z 2025-07-17T07:11:36.1514907Z --- Parse Warning: 38 / 136 --- 2025-07-17T07:11:36.1515976Z /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. 2025-07-17T07:11:36.1516237Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1516325Z 2025-07-17T07:11:36.1516569Z Config object that specifies the supported data types passed as arguments to 2025-07-17T07:11:36.1516802Z quantize ops in the reference model spec, for input and output activations, 2025-07-17T07:11:36.1516908Z weights, and biases. 2025-07-17T07:11:36.1516986Z 2025-07-17T07:11:36.1517140Z For example, consider the following reference model: 2025-07-17T07:11:36.1517229Z 2025-07-17T07:11:36.1517380Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-07-17T07:11:36.1517455Z 2025-07-17T07:11:36.1517677Z The pattern in the square brackets refers to the reference pattern of 2025-07-17T07:11:36.1517937Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-07-17T07:11:36.1518165Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-07-17T07:11:36.1518384Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-07-17T07:11:36.1518598Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-07-17T07:11:36.1518713Z the second quantize op (quant2). 2025-07-17T07:11:36.1518784Z 2025-07-17T07:11:36.1518995Z Note that the dtype here does not refer to the interface dtypes of the 2025-07-17T07:11:36.1519209Z op. For example, the "input dtype" here is not the dtype of the input 2025-07-17T07:11:36.1519418Z tensor passed to the quantized linear op. Though it can still be the 2025-07-17T07:11:36.1519611Z same as the interface dtype, this is not always the case, e.g. the 2025-07-17T07:11:36.1519865Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-07-17T07:11:36.1520076Z specified in the DTypeConfig would still be quint8. The semantics of 2025-07-17T07:11:36.1520293Z dtypes here are the same as the semantics of the dtypes specified in 2025-07-17T07:11:36.1520426Z the observers. 2025-07-17T07:11:36.1520504Z 2025-07-17T07:11:36.1520718Z These dtypes are matched against the ones specified in the user's 2025-07-17T07:11:36.1520931Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-07-17T07:11:36.1521144Z specified in the DTypeConfig (if any), then we will quantize the given 2025-07-17T07:11:36.1521379Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-07-17T07:11:36.1521489Z the pattern will not be quantized. 2025-07-17T07:11:36.1521569Z 2025-07-17T07:11:36.1521663Z Example usage:: 2025-07-17T07:11:36.1521736Z 2025-07-17T07:11:36.1521845Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:36.1521950Z >>> dtype_config1 = DTypeConfig( 2025-07-17T07:11:36.1522051Z ... input_dtype=torch.quint8, 2025-07-17T07:11:36.1522161Z ... output_dtype=torch.quint8, 2025-07-17T07:11:36.1522265Z ... weight_dtype=torch.qint8, 2025-07-17T07:11:36.1522362Z ... bias_dtype=torch.float) 2025-07-17T07:11:36.1522444Z 2025-07-17T07:11:36.1522545Z >>> dtype_config2 = DTypeConfig( 2025-07-17T07:11:36.1522669Z ... input_dtype=DTypeWithConstraints( 2025-07-17T07:11:36.1522774Z ... dtype=torch.quint8, 2025-07-17T07:11:36.1522873Z ... quant_min_lower_bound=0, 2025-07-17T07:11:36.1522984Z ... quant_max_upper_bound=255, 2025-07-17T07:11:36.1523059Z ... ), 2025-07-17T07:11:36.1523181Z ... output_dtype=DTypeWithConstraints( 2025-07-17T07:11:36.1523280Z ... dtype=torch.quint8, 2025-07-17T07:11:36.1523380Z ... quant_min_lower_bound=0, 2025-07-17T07:11:36.1523485Z ... quant_max_upper_bound=255, 2025-07-17T07:11:36.1523569Z ... ), 2025-07-17T07:11:36.1523690Z ... weight_dtype=DTypeWithConstraints( 2025-07-17T07:11:36.1523784Z ... dtype=torch.qint8, 2025-07-17T07:11:36.1523903Z ... quant_min_lower_bound=-128, 2025-07-17T07:11:36.1524009Z ... quant_max_upper_bound=127, 2025-07-17T07:11:36.1524101Z ... ), 2025-07-17T07:11:36.1524200Z ... bias_dtype=torch.float) 2025-07-17T07:11:36.1524273Z 2025-07-17T07:11:36.1524390Z >>> dtype_config1.input_dtype 2025-07-17T07:11:36.1524476Z torch.quint8 2025-07-17T07:11:36.1524550Z 2025-07-17T07:11:36.1524662Z >>> dtype_config2.input_dtype 2025-07-17T07:11:36.1524743Z torch.quint8 2025-07-17T07:11:36.1524819Z 2025-07-17T07:11:36.1524958Z >>> dtype_config2.input_dtype_with_constraints 2025-07-17T07:11:36.1525495Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-07-17T07:11:36.1526359Z 2025-07-17T07:11:36.1526613Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1526695Z 2025-07-17T07:11:36.1526800Z warnings.warn(msg) 2025-07-17T07:11:36.1526874Z 2025-07-17T07:11:36.1527064Z --- Parse Warning: 39 / 136 --- 2025-07-17T07:11:36.1528105Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-07-17T07:11:36.1528365Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1528442Z 2025-07-17T07:11:36.1528757Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-07-17T07:11:36.1529082Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-07-17T07:11:36.1529393Z though the requirements on the type of model more-so depends on the specific report the user 2025-07-17T07:11:36.1529734Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-07-17T07:11:36.1530007Z a set of Detector classes, each of which generate reports on quantization configuration 2025-07-17T07:11:36.1530119Z issues a use might have. 2025-07-17T07:11:36.1530196Z 2025-07-17T07:11:36.1530325Z Currently supports generating reports on: 2025-07-17T07:11:36.1530565Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-07-17T07:11:36.1530916Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-07-17T07:11:36.1531262Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-07-17T07:11:36.1531474Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-07-17T07:11:36.1531553Z 2025-07-17T07:11:36.1531970Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-07-17T07:11:36.1532346Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-07-17T07:11:36.1532732Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-07-17T07:11:36.1532937Z has the capability to remove all the observers it inserted as well. 2025-07-17T07:11:36.1533011Z 2025-07-17T07:11:36.1533297Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-07-17T07:11:36.1533369Z 2025-07-17T07:11:36.1533745Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-07-17T07:11:36.1534067Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-07-17T07:11:36.1534142Z 2025-07-17T07:11:36.1534430Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-07-17T07:11:36.1534658Z This set is generated by calling the get_detector_name() of each detector 2025-07-17T07:11:36.1534732Z 2025-07-17T07:11:36.1535066Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-07-17T07:11:36.1535478Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-07-17T07:11:36.1535589Z can be removed at the end if desired 2025-07-17T07:11:36.1535669Z 2025-07-17T07:11:36.1535980Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-07-17T07:11:36.1536216Z This is to ensure we only insert observers once with the ModelReport instance 2025-07-17T07:11:36.1536337Z 2025-07-17T07:11:36.1536590Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-07-17T07:11:36.1536888Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-07-17T07:11:36.1537201Z instance. This also allows the functionality where we can generate the report multiple times 2025-07-17T07:11:36.1537338Z as long as we haven't removed the observers yet. 2025-07-17T07:11:36.1537422Z 2025-07-17T07:11:36.1537499Z Note: 2025-07-17T07:11:36.1537791Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-07-17T07:11:36.1538108Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-07-17T07:11:36.1538395Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-07-17T07:11:36.1538537Z the QuantizationTracer class. 2025-07-17T07:11:36.1538614Z 2025-07-17T07:11:36.1538710Z General Flow for Fx workflow: 2025-07-17T07:11:36.1539102Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-07-17T07:11:36.1539263Z 2.) Prepare your model with prepare_fx 2025-07-17T07:11:36.1539500Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-07-17T07:11:36.1539611Z 4.) Calibrate your model with data 2025-07-17T07:11:36.1539962Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-07-17T07:11:36.1540045Z Optional 2025-07-17T07:11:36.1540312Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-07-17T07:11:36.1540550Z 7.) To help in parsing report information and debugging, view report info as a: 2025-07-17T07:11:36.1540640Z - Table 2025-07-17T07:11:36.1540728Z - Histogram 2025-07-17T07:11:36.1540813Z - Line plot 2025-07-17T07:11:36.1541134Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-07-17T07:11:36.1541206Z 2025-07-17T07:11:36.1541328Z Example (with QuantizationTracer): 2025-07-17T07:11:36.1541424Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1541522Z >>> # get the necessary qconfig 2025-07-17T07:11:36.1541641Z >>> config = PrepareCustomConfig() 2025-07-17T07:11:36.1541790Z >>> skipped_module_names, skipped_module_classes = ( 2025-07-17T07:11:36.1541944Z ... get_skipped_module_name_and_classes(config, False) 2025-07-17T07:11:36.1542026Z ... ) 2025-07-17T07:11:36.1542099Z 2025-07-17T07:11:36.1542223Z >>> # initialize our model and get GraphModule 2025-07-17T07:11:36.1542320Z >>> model = SomeModel() 2025-07-17T07:11:36.1542565Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-07-17T07:11:36.1542729Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-07-17T07:11:36.1542810Z 2025-07-17T07:11:36.1542952Z >>> # get our set of detectors and ModelReport instance 2025-07-17T07:11:36.1543051Z >>> detector_set = set( 2025-07-17T07:11:36.1543125Z ... [ 2025-07-17T07:11:36.1543255Z ... DynamicStaticDetector(tolerance=0.5), 2025-07-17T07:11:36.1543446Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-07-17T07:11:36.1543522Z ... ] 2025-07-17T07:11:36.1543597Z ... ) 2025-07-17T07:11:36.1543804Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-07-17T07:11:36.1543878Z 2025-07-17T07:11:36.1544026Z >>> # now we insert the observers and calibrate the model 2025-07-17T07:11:36.1544280Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-07-17T07:11:36.1544407Z >>> for i in range(num_callibration_batches): 2025-07-17T07:11:36.1544573Z >>> example_input = get_callibration_input() 2025-07-17T07:11:36.1544702Z >>> tracer_model_with_observers(example_input) 2025-07-17T07:11:36.1544771Z 2025-07-17T07:11:36.1545036Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-07-17T07:11:36.1545182Z >>> reports = tracer_reporter.generate_model_report( 2025-07-17T07:11:36.1545293Z ... remove_inserted_observers=True 2025-07-17T07:11:36.1545374Z ... ) 2025-07-17T07:11:36.1545444Z 2025-07-17T07:11:36.1545659Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-07-17T07:11:36.1545817Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-07-17T07:11:36.1545890Z 2025-07-17T07:11:36.1546132Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-07-17T07:11:36.1546303Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-07-17T07:11:36.1546406Z 2025-07-17T07:11:36.1546689Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-07-17T07:11:36.1546974Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-07-17T07:11:36.1547050Z 2025-07-17T07:11:36.1547128Z 2025-07-17T07:11:36.1547375Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1547447Z 2025-07-17T07:11:36.1547544Z warnings.warn(msg) 2025-07-17T07:11:36.1547616Z 2025-07-17T07:11:36.1547831Z --- Parse Warning: 40 / 136 --- 2025-07-17T07:11:36.1549084Z /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. 2025-07-17T07:11:36.1549343Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1549429Z 2025-07-17T07:11:36.1549691Z Takes in optional filter values and generates two tables with desired information. 2025-07-17T07:11:36.1549780Z 2025-07-17T07:11:36.1549987Z The generated tables are presented in both a list-of-lists format 2025-07-17T07:11:36.1550062Z 2025-07-17T07:11:36.1550275Z The reason for the two tables are that they handle different things: 2025-07-17T07:11:36.1550432Z 1.) the first table handles all tensor level information 2025-07-17T07:11:36.1550640Z 2.) the second table handles and displays all channel based information 2025-07-17T07:11:36.1550723Z 2025-07-17T07:11:36.1551039Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-07-17T07:11:36.1551364Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-07-17T07:11:36.1551724Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-07-17T07:11:36.1551801Z 2025-07-17T07:11:36.1551900Z Tensor table columns: 2025-07-17T07:11:36.1552087Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-07-17T07:11:36.1552233Z ---- --------- --------- --------- --------- --------- 2025-07-17T07:11:36.1552316Z 2025-07-17T07:11:36.1552416Z Per-Channel table columns: 2025-07-17T07:11:36.1552629Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-07-17T07:11:36.1552798Z ---- --------- ------- --------- --------- --------- --------- 2025-07-17T07:11:36.1552869Z 2025-07-17T07:11:36.1552944Z Args: 2025-07-17T07:11:36.1553207Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T07:11:36.1553310Z contain this filter substring 2025-07-17T07:11:36.1553475Z Default = "", results in all the features being printed 2025-07-17T07:11:36.1553754Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T07:11:36.1553999Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T07:11:36.1554075Z 2025-07-17T07:11:36.1554178Z Returns a dictionary with two keys: 2025-07-17T07:11:36.1554343Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-07-17T07:11:36.1554466Z "tensor_level_info", "channel_level_info" 2025-07-17T07:11:36.1554568Z Each key maps to a tuple with: 2025-07-17T07:11:36.1554683Z A list of the headers of each table 2025-07-17T07:11:36.1554857Z A list of lists containing the table information row by row 2025-07-17T07:11:36.1555020Z The 0th index row will contain the headers of the columns 2025-07-17T07:11:36.1555141Z The rest of the rows will contain data 2025-07-17T07:11:36.1555239Z 2025-07-17T07:11:36.1555320Z Example Use: 2025-07-17T07:11:36.1555447Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1555590Z >>> mod_report_visualizer.generate_filtered_tables( 2025-07-17T07:11:36.1555823Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T07:11:36.1556100Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-07-17T07:11:36.1556172Z 2025-07-17T07:11:36.1556429Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1556501Z 2025-07-17T07:11:36.1556589Z warnings.warn(msg) 2025-07-17T07:11:36.1556667Z 2025-07-17T07:11:36.1556849Z --- Parse Warning: 41 / 136 --- 2025-07-17T07:11:36.1558127Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=399. 2025-07-17T07:11:36.1558398Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1558474Z 2025-07-17T07:11:36.1558746Z Takes in optional filter values and prints out formatted tables of the information. 2025-07-17T07:11:36.1558821Z 2025-07-17T07:11:36.1559162Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-07-17T07:11:36.1559328Z 1.) the first table handles all tensor level information 2025-07-17T07:11:36.1559539Z 2.) the second table handles and displays all channel based information 2025-07-17T07:11:36.1559613Z 2025-07-17T07:11:36.1559937Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-07-17T07:11:36.1560259Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-07-17T07:11:36.1560620Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-07-17T07:11:36.1560693Z 2025-07-17T07:11:36.1560788Z Tensor table columns: 2025-07-17T07:11:36.1560979Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-07-17T07:11:36.1561121Z ---- --------- --------- --------- --------- --------- 2025-07-17T07:11:36.1561192Z 2025-07-17T07:11:36.1561295Z Per-Channel table columns: 2025-07-17T07:11:36.1561367Z 2025-07-17T07:11:36.1561574Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-07-17T07:11:36.1561734Z ---- --------- ------- --------- --------- --------- --------- 2025-07-17T07:11:36.1561803Z 2025-07-17T07:11:36.1561882Z Args: 2025-07-17T07:11:36.1562139Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T07:11:36.1562274Z contain this filter substring 2025-07-17T07:11:36.1562435Z Default = "", results in all the features being printed 2025-07-17T07:11:36.1562691Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T07:11:36.1562931Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T07:11:36.1563010Z 2025-07-17T07:11:36.1563094Z Example Use: 2025-07-17T07:11:36.1563222Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1563380Z >>> mod_report_visualizer.generate_table_visualization( 2025-07-17T07:11:36.1563568Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T07:11:36.1563652Z ... ) 2025-07-17T07:11:36.1563837Z >>> # prints out neatly formatted table with per_channel_min info 2025-07-17T07:11:36.1563956Z >>> # for all modules in block 1 of the model 2025-07-17T07:11:36.1564064Z 2025-07-17T07:11:36.1564312Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1564385Z 2025-07-17T07:11:36.1564481Z warnings.warn(msg) 2025-07-17T07:11:36.1564555Z 2025-07-17T07:11:36.1564783Z --- Parse Warning: 42 / 136 --- 2025-07-17T07:11:36.1566059Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=564. 2025-07-17T07:11:36.1566316Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1566395Z 2025-07-17T07:11:36.1566623Z Takes in a feature and optional module_filter and plots of the desired data. 2025-07-17T07:11:36.1566697Z 2025-07-17T07:11:36.1566969Z For per channel features, it averages the value across the channels and plots a point 2025-07-17T07:11:36.1567226Z per module. The reason for this is that for models with hundreds of channels, it can 2025-07-17T07:11:36.1567505Z be hard to differentiate one channel line from another, and so the point of generating 2025-07-17T07:11:36.1567762Z a single average point per module is to give a sense of general trends that encourage 2025-07-17T07:11:36.1567851Z further deep dives. 2025-07-17T07:11:36.1567928Z 2025-07-17T07:11:36.1568002Z Note: 2025-07-17T07:11:36.1568259Z Only features in the report that have tensor value data are plottable by this class 2025-07-17T07:11:36.1568422Z When the tensor information is plotted, it will plot: 2025-07-17T07:11:36.1568550Z idx as the x val, feature value as the y_val 2025-07-17T07:11:36.1568711Z When the channel information is plotted, it will plot: 2025-07-17T07:11:36.1568970Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-07-17T07:11:36.1569193Z The reason for this is that we want to be able to compare values across the 2025-07-17T07:11:36.1569425Z channels for same layer, and it will be hard if values are staggered by idx 2025-07-17T07:11:36.1569593Z This means each module is represented by only 1 x value 2025-07-17T07:11:36.1569670Z Args: 2025-07-17T07:11:36.1569893Z feature_filter (str): Filters the features presented to only those that 2025-07-17T07:11:36.1569996Z contain this filter substring 2025-07-17T07:11:36.1570252Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T07:11:36.1570491Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T07:11:36.1570561Z 2025-07-17T07:11:36.1570647Z Example Use: 2025-07-17T07:11:36.1570769Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1570923Z >>> mod_report_visualizer.generate_plot_visualization( 2025-07-17T07:11:36.1571142Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T07:11:36.1571216Z ... ) 2025-07-17T07:11:36.1571391Z >>> # outputs line plot of per_channel_min information for all 2025-07-17T07:11:36.1571571Z >>> # modules in block1 of model each channel gets it's own line, 2025-07-17T07:11:36.1571737Z >>> # and it's plotted across the in-order modules on the x-axis 2025-07-17T07:11:36.1572001Z 2025-07-17T07:11:36.1572246Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1572316Z 2025-07-17T07:11:36.1572412Z warnings.warn(msg) 2025-07-17T07:11:36.1572486Z 2025-07-17T07:11:36.1572673Z --- Parse Warning: 43 / 136 --- 2025-07-17T07:11:36.1573979Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=643. 2025-07-17T07:11:36.1574360Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1574442Z 2025-07-17T07:11:36.1574719Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-07-17T07:11:36.1574793Z 2025-07-17T07:11:36.1574876Z Note: 2025-07-17T07:11:36.1575137Z Only features in the report that have tensor value data can be viewed as a histogram 2025-07-17T07:11:36.1575489Z If you want to plot a histogram from all the channel values of a specific feature for 2025-07-17T07:11:36.1575739Z a specific model, make sure to specify both the model and the feature properly 2025-07-17T07:11:36.1575978Z in the filters and you should be able to see a distribution of the channel data 2025-07-17T07:11:36.1576058Z 2025-07-17T07:11:36.1576138Z Args: 2025-07-17T07:11:36.1576393Z feature_filter (str, optional): Filters the features presented to only those that 2025-07-17T07:11:36.1576505Z contain this filter substring 2025-07-17T07:11:36.1576668Z Default = "", results in all the features being printed 2025-07-17T07:11:36.1576924Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-07-17T07:11:36.1577174Z Default = "", results in all the modules in the reports to be visible in the table 2025-07-17T07:11:36.1577391Z num_bins (int, optional): The number of bins to create the histogram with 2025-07-17T07:11:36.1577582Z Default = 10, the values will be split into 10 equal sized bins 2025-07-17T07:11:36.1577659Z 2025-07-17T07:11:36.1577741Z Example Use: 2025-07-17T07:11:36.1577840Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1578136Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-07-17T07:11:36.1578329Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-07-17T07:11:36.1578406Z ... ) 2025-07-17T07:11:36.1578677Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-07-17T07:11:36.1578930Z information is gathered across all channels for all modules in block 1 for the 2025-07-17T07:11:36.1579151Z per_channel_min and is displayed in a histogram of equally sized bins 2025-07-17T07:11:36.1579221Z 2025-07-17T07:11:36.1579470Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1579542Z 2025-07-17T07:11:36.1579633Z warnings.warn(msg) 2025-07-17T07:11:36.1579710Z 2025-07-17T07:11:36.1579892Z --- Parse Warning: 44 / 136 --- 2025-07-17T07:11:36.1580786Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py line=734. 2025-07-17T07:11:36.1581093Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1581475Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-07-17T07:11:36.1581656Z Label will only appear if CPU activity tracing is enabled. 2025-07-17T07:11:36.1581727Z 2025-07-17T07:11:36.1581848Z It is useful when tracing the code profile. 2025-07-17T07:11:36.1581925Z 2025-07-17T07:11:36.1582000Z Args: 2025-07-17T07:11:36.1582133Z name (str): Label assigned to the block of code. 2025-07-17T07:11:36.1582312Z node_id (int): ID of node, for distributed profiling. Unset in 2025-07-17T07:11:36.1582410Z non-distributed cases. 2025-07-17T07:11:36.1582480Z 2025-07-17T07:11:36.1582563Z Example: 2025-07-17T07:11:36.1582775Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-07-17T07:11:36.1582907Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-07-17T07:11:36.1583061Z >>> with torch.autograd.profiler.profile() as prof: 2025-07-17T07:11:36.1583194Z ... y = x**2 2025-07-17T07:11:36.1583353Z ... with torch.autograd.profiler.record_function( 2025-07-17T07:11:36.1583440Z ... "label-z" 2025-07-17T07:11:36.1583531Z ... ): # label the block 2025-07-17T07:11:36.1583620Z ... z = y**3 2025-07-17T07:11:36.1583706Z ... y.backward() 2025-07-17T07:11:36.1583801Z >>> # xdoctest: +IGNORE_WANT 2025-07-17T07:11:36.1583934Z >>> # NOTE: some columns were removed for brevity 2025-07-17T07:11:36.1584128Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-07-17T07:11:36.1584320Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T07:11:36.1584501Z Name Self CPU total % CPU time avg Number of Calls 2025-07-17T07:11:36.1584686Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T07:11:36.1584817Z pow 60.77% 47.470us 3 2025-07-17T07:11:36.1584936Z mul 21.73% 25.465us 2 2025-07-17T07:11:36.1585102Z PowBackward0 12.03% 121.891us 1 2025-07-17T07:11:36.1585319Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-07-17T07:11:36.1585453Z label-z 2.13% 12.421us 1 2025-07-17T07:11:36.1585651Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-07-17T07:11:36.1585833Z ----------------------------------- --------------- --------------- --------------- 2025-07-17T07:11:36.1585938Z Self CPU time total: 234.344us 2025-07-17T07:11:36.1586035Z CUDA time total: 0.000us 2025-07-17T07:11:36.1586109Z 2025-07-17T07:11:36.1586191Z 2025-07-17T07:11:36.1586439Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1586515Z 2025-07-17T07:11:36.1586610Z warnings.warn(msg) 2025-07-17T07:11:36.1586681Z 2025-07-17T07:11:36.1586856Z --- Parse Warning: 45 / 136 --- 2025-07-17T07:11:36.1587821Z /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=685. 2025-07-17T07:11:36.1588063Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:36.1588146Z 2025-07-17T07:11:36.1588440Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-07-17T07:11:36.1588699Z The submesh created consists of the dimensions and the communicators indicated by 2025-07-17T07:11:36.1588797Z ``mesh_dim_names`` 2025-07-17T07:11:36.1588872Z 2025-07-17T07:11:36.1588949Z Args: 2025-07-17T07:11:36.1589183Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-07-17T07:11:36.1589364Z mesh dimension of the DeviceMesh to create the submesh for. 2025-07-17T07:11:36.1589442Z Returns: 2025-07-17T07:11:36.1589548Z A :class:`DeviceMesh` object 2025-07-17T07:11:36.1589619Z 2025-07-17T07:11:36.1589897Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-07-17T07:11:36.1589997Z In the first example: 2025-07-17T07:11:36.1590244Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-07-17T07:11:36.1590516Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-07-17T07:11:36.1590734Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-07-17T07:11:36.1590996Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-07-17T07:11:36.1591220Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-07-17T07:11:36.1591433Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-07-17T07:11:36.1591515Z 2025-07-17T07:11:36.1591606Z In the second example: 2025-07-17T07:11:36.1591866Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-07-17T07:11:36.1592132Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-07-17T07:11:36.1592387Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-07-17T07:11:36.1592648Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-07-17T07:11:36.1592732Z 2025-07-17T07:11:36.1592820Z Example:: 2025-07-17T07:11:36.1592900Z 2025-07-17T07:11:36.1593002Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T07:11:36.1593170Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-07-17T07:11:36.1593257Z >>> 2025-07-17T07:11:36.1593451Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-07-17T07:11:36.1593587Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-07-17T07:11:36.1593843Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-07-17T07:11:36.1593937Z >>> tp_mesh = mesh_2d["tp"] 2025-07-17T07:11:36.1594032Z >>> dp_mesh = mesh_2d["dp"] 2025-07-17T07:11:36.1594117Z >>> 2025-07-17T07:11:36.1594212Z >>> # Initialize a 3D mesh. 2025-07-17T07:11:36.1594496Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-07-17T07:11:36.1594799Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-07-17T07:11:36.1594909Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-07-17T07:11:36.1595024Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-07-17T07:11:36.1595099Z 2025-07-17T07:11:36.1595773Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2025-07-17T07:11:36.1595861Z 2025-07-17T07:11:36.1596100Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-07-17T07:11:36.1596221Z ^ 2025-07-17T07:11:36.1596314Z warnings.warn(msg) 2025-07-17T07:11:36.1596410Z 2025-07-17T07:11:36.1596594Z --- Parse Warning: 46 / 136 --- 2025-07-17T07:11:36.1597573Z /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=2706. 2025-07-17T07:11:36.1597831Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1597909Z 2025-07-17T07:11:36.1598149Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-07-17T07:11:36.1598233Z 2025-07-17T07:11:36.1598475Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-07-17T07:11:36.1598663Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-07-17T07:11:36.1598741Z 2025-07-17T07:11:36.1598815Z Args: 2025-07-17T07:11:36.1599063Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-07-17T07:11:36.1599296Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-07-17T07:11:36.1599547Z matters and it needs to match with corresponding isend/irecv on the 2025-07-17T07:11:36.1599635Z remote end. 2025-07-17T07:11:36.1599715Z 2025-07-17T07:11:36.1599794Z Returns: 2025-07-17T07:11:36.1600047Z A list of distributed request objects returned by calling the corresponding 2025-07-17T07:11:36.1600140Z op in the op_list. 2025-07-17T07:11:36.1600213Z 2025-07-17T07:11:36.1600303Z Examples: 2025-07-17T07:11:36.1600406Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T07:11:36.1600589Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-07-17T07:11:36.1600749Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-07-17T07:11:36.1600952Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-07-17T07:11:36.1601056Z >>> recv_op = dist.P2POp( 2025-07-17T07:11:36.1601248Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-07-17T07:11:36.1601330Z ... ) 2025-07-17T07:11:36.1601469Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-07-17T07:11:36.1601557Z >>> for req in reqs: 2025-07-17T07:11:36.1601640Z >>> req.wait() 2025-07-17T07:11:36.1601731Z >>> recv_tensor 2025-07-17T07:11:36.1601822Z tensor([2, 3]) # Rank 0 2025-07-17T07:11:36.1601914Z tensor([0, 1]) # Rank 1 2025-07-17T07:11:36.1601997Z 2025-07-17T07:11:36.1602230Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-07-17T07:11:36.1602442Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-07-17T07:11:36.1602554Z lead to unexpected hang issues. 2025-07-17T07:11:36.1602627Z 2025-07-17T07:11:36.1602837Z In addition, if this API is the first collective call in the ``group`` 2025-07-17T07:11:36.1603052Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-07-17T07:11:36.1603272Z this API call; otherwise, the behavior is undefined. If this API call is 2025-07-17T07:11:36.1603491Z not the first collective call in the ``group``, batched P2P operations 2025-07-17T07:11:36.1603680Z involving only a subset of ranks of the ``group`` are allowed. 2025-07-17T07:11:36.1603754Z 2025-07-17T07:11:36.1604005Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1604080Z 2025-07-17T07:11:36.1604171Z warnings.warn(msg) 2025-07-17T07:11:36.1604250Z 2025-07-17T07:11:36.1604426Z --- Parse Warning: 47 / 136 --- 2025-07-17T07:11:36.1605369Z /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=2838. 2025-07-17T07:11:36.1605658Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1605730Z 2025-07-17T07:11:36.1606002Z Reduces the tensor data across all machines in a way that all get the final result. 2025-07-17T07:11:36.1606077Z 2025-07-17T07:11:36.1606296Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-07-17T07:11:36.1606379Z 2025-07-17T07:11:36.1606483Z Complex tensors are supported. 2025-07-17T07:11:36.1606562Z 2025-07-17T07:11:36.1606638Z Args: 2025-07-17T07:11:36.1606835Z tensor (Tensor): Input and output of the collective. The function 2025-07-17T07:11:36.1606937Z operates in-place. 2025-07-17T07:11:36.1607049Z op (optional): One of the values from 2025-07-17T07:11:36.1607160Z ``torch.distributed.ReduceOp`` 2025-07-17T07:11:36.1607392Z enum. Specifies an operation used for element-wise reductions. 2025-07-17T07:11:36.1607615Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:36.1607735Z the default process group will be used. 2025-07-17T07:11:36.1607978Z async_op (bool, optional): Whether this op should be an async op 2025-07-17T07:11:36.1608056Z 2025-07-17T07:11:36.1608142Z Returns: 2025-07-17T07:11:36.1608277Z Async work handle, if async_op is set to True. 2025-07-17T07:11:36.1608419Z None, if not async_op or if not part of the group 2025-07-17T07:11:36.1608508Z 2025-07-17T07:11:36.1608591Z Examples: 2025-07-17T07:11:36.1608694Z >>> # xdoctest: +SKIP("no rank") 2025-07-17T07:11:36.1608834Z >>> # All tensors below are of torch.int64 type. 2025-07-17T07:11:36.1608946Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:36.1609064Z >>> device = torch.device(f"cuda:{rank}") 2025-07-17T07:11:36.1609300Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-07-17T07:11:36.1609381Z >>> tensor 2025-07-17T07:11:36.1609504Z tensor([1, 2], device='cuda:0') # Rank 0 2025-07-17T07:11:36.1609615Z tensor([3, 4], device='cuda:1') # Rank 1 2025-07-17T07:11:36.1609744Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-07-17T07:11:36.1609836Z >>> tensor 2025-07-17T07:11:36.1609944Z tensor([4, 6], device='cuda:0') # Rank 0 2025-07-17T07:11:36.1610050Z tensor([4, 6], device='cuda:1') # Rank 1 2025-07-17T07:11:36.1610130Z 2025-07-17T07:11:36.1610261Z >>> # All tensors below are of torch.cfloat type. 2025-07-17T07:11:36.1610371Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:36.1610477Z >>> tensor = torch.tensor( 2025-07-17T07:11:36.1610616Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-07-17T07:11:36.1610719Z ... ) + 2 * rank * (1 + 1j) 2025-07-17T07:11:36.1610801Z >>> tensor 2025-07-17T07:11:36.1610938Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-07-17T07:11:36.1611080Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-07-17T07:11:36.1611211Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-07-17T07:11:36.1611291Z >>> tensor 2025-07-17T07:11:36.1611428Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-07-17T07:11:36.1611554Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-07-17T07:11:36.1611630Z 2025-07-17T07:11:36.1611716Z 2025-07-17T07:11:36.1611969Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1612047Z 2025-07-17T07:11:36.1612149Z warnings.warn(msg) 2025-07-17T07:11:36.1612225Z 2025-07-17T07:11:36.1612411Z --- Parse Warning: 48 / 136 --- 2025-07-17T07:11:36.1613365Z /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=3198. 2025-07-17T07:11:36.1613664Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1613757Z 2025-07-17T07:11:36.1613976Z Gathers picklable objects from the whole group in a single process. 2025-07-17T07:11:36.1614052Z 2025-07-17T07:11:36.1614288Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-07-17T07:11:36.1614432Z object must be picklable in order to be gathered. 2025-07-17T07:11:36.1614515Z 2025-07-17T07:11:36.1614589Z Args: 2025-07-17T07:11:36.1614714Z obj (Any): Input object. Must be picklable. 2025-07-17T07:11:36.1614926Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-07-17T07:11:36.1615107Z should be correctly sized as the size of the group for this 2025-07-17T07:11:36.1615392Z collective and will contain the output. Must be ``None`` on non-dst 2025-07-17T07:11:36.1615538Z ranks. (default is ``None``) 2025-07-17T07:11:36.1615853Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-07-17T07:11:36.1616098Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-07-17T07:11:36.1616328Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:36.1616512Z the default process group will be used. Default is ``None``. 2025-07-17T07:11:36.1616868Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-07-17T07:11:36.1616944Z 2025-07-17T07:11:36.1617023Z Returns: 2025-07-17T07:11:36.1617203Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-07-17T07:11:36.1617301Z output of the collective. 2025-07-17T07:11:36.1617378Z 2025-07-17T07:11:36.1617601Z .. note:: Note that this API differs slightly from the gather collective 2025-07-17T07:11:36.1617819Z since it does not provide an async_op handle and thus will be a blocking 2025-07-17T07:11:36.1617906Z call. 2025-07-17T07:11:36.1617981Z 2025-07-17T07:11:36.1618209Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-07-17T07:11:36.1618423Z of objects must be moved to the GPU device before communication takes 2025-07-17T07:11:36.1618562Z place. In this case, the device used is given by 2025-07-17T07:11:36.1618780Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-07-17T07:11:36.1618989Z ensure that this is set so that each rank has an individual GPU, via 2025-07-17T07:11:36.1619088Z ``torch.cuda.set_device()``. 2025-07-17T07:11:36.1619176Z 2025-07-17T07:11:36.1619257Z .. warning:: 2025-07-17T07:11:36.1619487Z Object collectives have a number of serious performance and scalability 2025-07-17T07:11:36.1619671Z limitations. See :ref:`object_collectives` for details. 2025-07-17T07:11:36.1619746Z 2025-07-17T07:11:36.1619825Z .. warning:: 2025-07-17T07:11:36.1620027Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-07-17T07:11:36.1620243Z known to be insecure. It is possible to construct malicious pickle data 2025-07-17T07:11:36.1620456Z which will execute arbitrary code during unpickling. Only call this 2025-07-17T07:11:36.1620570Z function with data you trust. 2025-07-17T07:11:36.1620644Z 2025-07-17T07:11:36.1620735Z .. warning:: 2025-07-17T07:11:36.1620940Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-07-17T07:11:36.1621159Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-07-17T07:11:36.1621331Z pickled. Please consider using :func:`gather` instead. 2025-07-17T07:11:36.1621406Z 2025-07-17T07:11:36.1621491Z Example:: 2025-07-17T07:11:36.1621657Z >>> # xdoctest: +SKIP("need process group init") 2025-07-17T07:11:36.1621830Z >>> # Note: Process group initialization omitted on each rank. 2025-07-17T07:11:36.1621944Z >>> import torch.distributed as dist 2025-07-17T07:11:36.1622064Z >>> # Assumes world_size of 3. 2025-07-17T07:11:36.1622234Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-07-17T07:11:36.1622360Z >>> output = [None for _ in gather_objects] 2025-07-17T07:11:36.1622454Z >>> dist.gather_object( 2025-07-17T07:11:36.1622567Z ... gather_objects[dist.get_rank()], 2025-07-17T07:11:36.1622705Z ... output if dist.get_rank() == 0 else None, 2025-07-17T07:11:36.1622784Z ... dst=0 2025-07-17T07:11:36.1622860Z ... ) 2025-07-17T07:11:36.1622956Z >>> # On rank 0 2025-07-17T07:11:36.1623034Z >>> output 2025-07-17T07:11:36.1623121Z ['foo', 12, {1: 2}] 2025-07-17T07:11:36.1623238Z 2025-07-17T07:11:36.1623489Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1623573Z 2025-07-17T07:11:36.1623663Z warnings.warn(msg) 2025-07-17T07:11:36.1623735Z 2025-07-17T07:11:36.1623985Z --- Parse Warning: 49 / 136 --- 2025-07-17T07:11:36.1624938Z /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=3794. 2025-07-17T07:11:36.1625194Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1625277Z 2025-07-17T07:11:36.1625412Z Gathers tensors from the whole group in a list. 2025-07-17T07:11:36.1625489Z 2025-07-17T07:11:36.1625639Z Complex and uneven sized tensors are supported. 2025-07-17T07:11:36.1625713Z 2025-07-17T07:11:36.1625806Z Args: 2025-07-17T07:11:36.1625982Z tensor_list (list[Tensor]): Output list. It should contain 2025-07-17T07:11:36.1626196Z correctly-sized tensors to be used for output of the collective. 2025-07-17T07:11:36.1626325Z Uneven sized tensors are supported. 2025-07-17T07:11:36.1626511Z tensor (Tensor): Tensor to be broadcast from current process. 2025-07-17T07:11:36.1626733Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:36.1626865Z the default process group will be used. 2025-07-17T07:11:36.1627051Z async_op (bool, optional): Whether this op should be an async op 2025-07-17T07:11:36.1627126Z 2025-07-17T07:11:36.1627217Z Returns: 2025-07-17T07:11:36.1627349Z Async work handle, if async_op is set to True. 2025-07-17T07:11:36.1627503Z None, if not async_op or if not part of the group 2025-07-17T07:11:36.1627577Z 2025-07-17T07:11:36.1627656Z Examples: 2025-07-17T07:11:36.1627801Z >>> # xdoctest: +SKIP("need process group init") 2025-07-17T07:11:36.1627936Z >>> # All tensors below are of torch.int64 dtype. 2025-07-17T07:11:36.1628046Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:36.1628178Z >>> device = torch.device(f"cuda:{rank}") 2025-07-17T07:11:36.1628270Z >>> tensor_list = [ 2025-07-17T07:11:36.1628480Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-07-17T07:11:36.1628570Z ... ] 2025-07-17T07:11:36.1628656Z >>> tensor_list 2025-07-17T07:11:36.1628863Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-07-17T07:11:36.1629050Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-07-17T07:11:36.1629270Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-07-17T07:11:36.1629364Z >>> tensor 2025-07-17T07:11:36.1629475Z tensor([1, 2], device='cuda:0') # Rank 0 2025-07-17T07:11:36.1629585Z tensor([3, 4], device='cuda:1') # Rank 1 2025-07-17T07:11:36.1629734Z >>> dist.all_gather(tensor_list, tensor) 2025-07-17T07:11:36.1629820Z >>> tensor_list 2025-07-17T07:11:36.1630015Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-07-17T07:11:36.1630215Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-07-17T07:11:36.1630291Z 2025-07-17T07:11:36.1630439Z >>> # All tensors below are of torch.cfloat dtype. 2025-07-17T07:11:36.1630550Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:36.1630640Z >>> tensor_list = [ 2025-07-17T07:11:36.1630859Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-07-17T07:11:36.1630934Z ... ] 2025-07-17T07:11:36.1631018Z >>> tensor_list 2025-07-17T07:11:36.1631271Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-07-17T07:11:36.1631599Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-07-17T07:11:36.1631749Z >>> tensor = torch.tensor( 2025-07-17T07:11:36.1631949Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-07-17T07:11:36.1632042Z ... ) + 2 * rank * (1 + 1j) 2025-07-17T07:11:36.1632133Z >>> tensor 2025-07-17T07:11:36.1632266Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-07-17T07:11:36.1632396Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-07-17T07:11:36.1632519Z >>> dist.all_gather(tensor_list, tensor) 2025-07-17T07:11:36.1632601Z >>> tensor_list 2025-07-17T07:11:36.1632838Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-07-17T07:11:36.1633080Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-07-17T07:11:36.1633156Z 2025-07-17T07:11:36.1633242Z 2025-07-17T07:11:36.1633498Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1633574Z 2025-07-17T07:11:36.1633674Z warnings.warn(msg) 2025-07-17T07:11:36.1633747Z 2025-07-17T07:11:36.1633943Z --- Parse Warning: 50 / 136 --- 2025-07-17T07:11:36.1634912Z /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=4500. 2025-07-17T07:11:36.1635170Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1635254Z 2025-07-17T07:11:36.1635502Z Split input tensor and then scatter the split list to all processes in a group. 2025-07-17T07:11:36.1635577Z 2025-07-17T07:11:36.1635840Z Later the received tensors are concatenated from all the processes in the group 2025-07-17T07:11:36.1635956Z and returned as a single output tensor. 2025-07-17T07:11:36.1636033Z 2025-07-17T07:11:36.1636149Z Complex tensors are supported. 2025-07-17T07:11:36.1636224Z 2025-07-17T07:11:36.1636304Z Args: 2025-07-17T07:11:36.1636480Z output (Tensor): Gathered concatenated output tensor. 2025-07-17T07:11:36.1636600Z input (Tensor): Input tensor to scatter. 2025-07-17T07:11:36.1636828Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-07-17T07:11:36.1637030Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-07-17T07:11:36.1637132Z equally by ``world_size``. 2025-07-17T07:11:36.1637352Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-07-17T07:11:36.1637546Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-07-17T07:11:36.1637649Z equally by ``world_size``. 2025-07-17T07:11:36.1637890Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:36.1638045Z the default process group will be used. 2025-07-17T07:11:36.1638251Z async_op (bool, optional): Whether this op should be an async op. 2025-07-17T07:11:36.1638327Z 2025-07-17T07:11:36.1638414Z Returns: 2025-07-17T07:11:36.1638560Z Async work handle, if async_op is set to True. 2025-07-17T07:11:36.1638701Z None, if not async_op or if not part of the group. 2025-07-17T07:11:36.1638780Z 2025-07-17T07:11:36.1638877Z .. warning:: 2025-07-17T07:11:36.1639048Z `all_to_all_single` is experimental and subject to change. 2025-07-17T07:11:36.1639124Z 2025-07-17T07:11:36.1639218Z Examples: 2025-07-17T07:11:36.1639334Z >>> # xdoctest: +SKIP("Undefined rank") 2025-07-17T07:11:36.1639444Z >>> input = torch.arange(4) + rank * 4 2025-07-17T07:11:36.1639537Z >>> input 2025-07-17T07:11:36.1639634Z tensor([0, 1, 2, 3]) # Rank 0 2025-07-17T07:11:36.1639764Z tensor([4, 5, 6, 7]) # Rank 1 2025-07-17T07:11:36.1639857Z tensor([8, 9, 10, 11]) # Rank 2 2025-07-17T07:11:36.1639951Z tensor([12, 13, 14, 15]) # Rank 3 2025-07-17T07:11:36.1640158Z >>> output = torch.empty([4], dtype=torch.int64) 2025-07-17T07:11:36.1640280Z >>> dist.all_to_all_single(output, input) 2025-07-17T07:11:36.1640361Z >>> output 2025-07-17T07:11:36.1640467Z tensor([0, 4, 8, 12]) # Rank 0 2025-07-17T07:11:36.1640561Z tensor([1, 5, 9, 13]) # Rank 1 2025-07-17T07:11:36.1640656Z tensor([2, 6, 10, 14]) # Rank 2 2025-07-17T07:11:36.1640757Z tensor([3, 7, 11, 15]) # Rank 3 2025-07-17T07:11:36.1640833Z 2025-07-17T07:11:36.1640988Z >>> # Essentially, it is similar to following operation: 2025-07-17T07:11:36.1641132Z >>> scatter_list = list(input.chunk(world_size)) 2025-07-17T07:11:36.1641266Z >>> gather_list = list(output.chunk(world_size)) 2025-07-17T07:11:36.1641379Z >>> for i in range(world_size): 2025-07-17T07:11:36.1641607Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-07-17T07:11:36.1641686Z 2025-07-17T07:11:36.1641810Z >>> # Another example with uneven split 2025-07-17T07:11:36.1641897Z >>> input 2025-07-17T07:11:36.1642045Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-07-17T07:11:36.1642214Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-07-17T07:11:36.1642362Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-07-17T07:11:36.1642527Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-07-17T07:11:36.1642613Z >>> input_splits 2025-07-17T07:11:36.1642729Z [2, 2, 1, 1] # Rank 0 2025-07-17T07:11:36.1642852Z [3, 2, 2, 2] # Rank 1 2025-07-17T07:11:36.1642969Z [2, 1, 1, 1] # Rank 2 2025-07-17T07:11:36.1643083Z [2, 2, 2, 1] # Rank 3 2025-07-17T07:11:36.1643186Z >>> output_splits 2025-07-17T07:11:36.1643300Z [2, 3, 2, 2] # Rank 0 2025-07-17T07:11:36.1643428Z [2, 2, 1, 2] # Rank 1 2025-07-17T07:11:36.1643540Z [1, 2, 1, 2] # Rank 2 2025-07-17T07:11:36.1643654Z [1, 2, 1, 1] # Rank 3 2025-07-17T07:11:36.1643755Z >>> output = ... 2025-07-17T07:11:36.1643956Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-07-17T07:11:36.1644037Z >>> output 2025-07-17T07:11:36.1644199Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-07-17T07:11:36.1644376Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-07-17T07:11:36.1644537Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-07-17T07:11:36.1644684Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-07-17T07:11:36.1644762Z 2025-07-17T07:11:36.1644850Z 2025-07-17T07:11:36.1645005Z >>> # Another example with tensors of torch.cfloat type. 2025-07-17T07:11:36.1645106Z >>> input = torch.tensor( 2025-07-17T07:11:36.1645251Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-07-17T07:11:36.1645340Z ... ) + 4 * rank * (1 + 1j) 2025-07-17T07:11:36.1645425Z >>> input 2025-07-17T07:11:36.1645607Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-07-17T07:11:36.1645770Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-07-17T07:11:36.1645981Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-07-17T07:11:36.1646160Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-07-17T07:11:36.1646346Z >>> output = torch.empty([4], dtype=torch.int64) 2025-07-17T07:11:36.1646478Z >>> dist.all_to_all_single(output, input) 2025-07-17T07:11:36.1646558Z >>> output 2025-07-17T07:11:36.1646726Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-07-17T07:11:36.1646903Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-07-17T07:11:36.1647068Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-07-17T07:11:36.1647243Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-07-17T07:11:36.1647318Z 2025-07-17T07:11:36.1647561Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1647643Z 2025-07-17T07:11:36.1647734Z warnings.warn(msg) 2025-07-17T07:11:36.1647810Z 2025-07-17T07:11:36.1648001Z --- Parse Warning: 51 / 136 --- 2025-07-17T07:11:36.1648943Z /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=4642. 2025-07-17T07:11:36.1649209Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1649284Z 2025-07-17T07:11:36.1649653Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-07-17T07:11:36.1649746Z 2025-07-17T07:11:36.1649853Z Complex tensors are supported. 2025-07-17T07:11:36.1649931Z 2025-07-17T07:11:36.1650019Z Args: 2025-07-17T07:11:36.1650235Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-07-17T07:11:36.1650319Z per rank. 2025-07-17T07:11:36.1650554Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-07-17T07:11:36.1650781Z group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:36.1650913Z the default process group will be used. 2025-07-17T07:11:36.1651104Z async_op (bool, optional): Whether this op should be an async op. 2025-07-17T07:11:36.1651179Z 2025-07-17T07:11:36.1651267Z Returns: 2025-07-17T07:11:36.1651401Z Async work handle, if async_op is set to True. 2025-07-17T07:11:36.1651541Z None, if not async_op or if not part of the group. 2025-07-17T07:11:36.1651625Z 2025-07-17T07:11:36.1651708Z .. warning:: 2025-07-17T07:11:36.1651850Z `all_to_all` is experimental and subject to change. 2025-07-17T07:11:36.1651934Z 2025-07-17T07:11:36.1652014Z Examples: 2025-07-17T07:11:36.1652128Z >>> # xdoctest: +SKIP("Undefined rank") 2025-07-17T07:11:36.1652275Z >>> input = torch.arange(4) + rank * 4 2025-07-17T07:11:36.1652379Z >>> input = list(input.chunk(4)) 2025-07-17T07:11:36.1652465Z >>> input 2025-07-17T07:11:36.1652630Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-07-17T07:11:36.1652787Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-07-17T07:11:36.1652953Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-07-17T07:11:36.1653120Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-07-17T07:11:36.1653296Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-07-17T07:11:36.1653413Z >>> dist.all_to_all(output, input) 2025-07-17T07:11:36.1653493Z >>> output 2025-07-17T07:11:36.1653660Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-07-17T07:11:36.1653814Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-07-17T07:11:36.1654000Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-07-17T07:11:36.1654164Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-07-17T07:11:36.1654240Z 2025-07-17T07:11:36.1654444Z >>> # Essentially, it is similar to following operation: 2025-07-17T07:11:36.1654548Z >>> scatter_list = input 2025-07-17T07:11:36.1654644Z >>> gather_list = output 2025-07-17T07:11:36.1654747Z >>> for i in range(world_size): 2025-07-17T07:11:36.1654975Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-07-17T07:11:36.1655052Z 2025-07-17T07:11:36.1655144Z >>> input 2025-07-17T07:11:36.1655362Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-07-17T07:11:36.1655521Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-07-17T07:11:36.1655681Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-07-17T07:11:36.1655835Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-07-17T07:11:36.1655923Z >>> input_splits 2025-07-17T07:11:36.1656055Z [2, 2, 1, 1] # Rank 0 2025-07-17T07:11:36.1656170Z [3, 2, 2, 2] # Rank 1 2025-07-17T07:11:36.1656296Z [2, 1, 1, 1] # Rank 2 2025-07-17T07:11:36.1656406Z [2, 2, 2, 1] # Rank 3 2025-07-17T07:11:36.1656495Z >>> output_splits 2025-07-17T07:11:36.1656620Z [2, 3, 2, 2] # Rank 0 2025-07-17T07:11:36.1656730Z [2, 2, 1, 2] # Rank 1 2025-07-17T07:11:36.1656841Z [1, 2, 1, 2] # Rank 2 2025-07-17T07:11:36.1656968Z [1, 2, 1, 1] # Rank 3 2025-07-17T07:11:36.1657090Z >>> input = list(input.split(input_splits)) 2025-07-17T07:11:36.1657178Z >>> input 2025-07-17T07:11:36.1657380Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-07-17T07:11:36.1657577Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-07-17T07:11:36.1657793Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-07-17T07:11:36.1657988Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-07-17T07:11:36.1658073Z >>> output = ... 2025-07-17T07:11:36.1658191Z >>> dist.all_to_all(output, input) 2025-07-17T07:11:36.1658267Z >>> output 2025-07-17T07:11:36.1658469Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-07-17T07:11:36.1658694Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-07-17T07:11:36.1658888Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-07-17T07:11:36.1659098Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-07-17T07:11:36.1659172Z 2025-07-17T07:11:36.1659326Z >>> # Another example with tensors of torch.cfloat type. 2025-07-17T07:11:36.1659433Z >>> input = torch.tensor( 2025-07-17T07:11:36.1659565Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-07-17T07:11:36.1659666Z ... ) + 4 * rank * (1 + 1j) 2025-07-17T07:11:36.1659770Z >>> input = list(input.chunk(4)) 2025-07-17T07:11:36.1659845Z >>> input 2025-07-17T07:11:36.1660065Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-07-17T07:11:36.1660294Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-07-17T07:11:36.1660509Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-07-17T07:11:36.1660782Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-07-17T07:11:36.1660962Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-07-17T07:11:36.1661079Z >>> dist.all_to_all(output, input) 2025-07-17T07:11:36.1661158Z >>> output 2025-07-17T07:11:36.1661363Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-07-17T07:11:36.1661575Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-07-17T07:11:36.1661783Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-07-17T07:11:36.1661986Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-07-17T07:11:36.1662077Z 2025-07-17T07:11:36.1662153Z 2025-07-17T07:11:36.1662412Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1662487Z 2025-07-17T07:11:36.1662579Z warnings.warn(msg) 2025-07-17T07:11:36.1662668Z 2025-07-17T07:11:36.1662866Z --- Parse Warning: 52 / 136 --- 2025-07-17T07:11:36.1663731Z /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. 2025-07-17T07:11:36.1664000Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1664079Z 2025-07-17T07:11:36.1664198Z Module ``torch.distributed.launch``. 2025-07-17T07:11:36.1664289Z 2025-07-17T07:11:36.1664537Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-07-17T07:11:36.1664699Z training processes on each of the training nodes. 2025-07-17T07:11:36.1664778Z 2025-07-17T07:11:36.1664869Z .. warning:: 2025-07-17T07:11:36.1664953Z 2025-07-17T07:11:36.1665207Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-07-17T07:11:36.1665283Z 2025-07-17T07:11:36.1665524Z The utility can be used for single-node distributed training, in which one or 2025-07-17T07:11:36.1665758Z more processes per node will be spawned. The utility can be used for either 2025-07-17T07:11:36.1665972Z CPU training or GPU training. If the utility is used for GPU training, 2025-07-17T07:11:36.1666224Z each distributed process will be operating on a single GPU. This can achieve 2025-07-17T07:11:36.1666454Z well-improved single-node training performance. It can also be used in 2025-07-17T07:11:36.1666727Z multi-node distributed training, by spawning up multiple processes on each node 2025-07-17T07:11:36.1666955Z for well-improved multi-node distributed training performance as well. 2025-07-17T07:11:36.1667214Z This will especially be beneficial for systems with multiple Infiniband 2025-07-17T07:11:36.1667472Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-07-17T07:11:36.1667593Z aggregated communication bandwidth. 2025-07-17T07:11:36.1667671Z 2025-07-17T07:11:36.1667912Z In both cases of single-node distributed training or multi-node distributed 2025-07-17T07:11:36.1668145Z training, this utility will launch the given number of processes per node 2025-07-17T07:11:36.1668380Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-07-17T07:11:36.1668593Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-07-17T07:11:36.1668792Z and each process will be operating on a single GPU from *GPU 0 to 2025-07-17T07:11:36.1668906Z GPU (nproc_per_node - 1)*. 2025-07-17T07:11:36.1668981Z 2025-07-17T07:11:36.1669108Z **How to use this module:** 2025-07-17T07:11:36.1669194Z 2025-07-17T07:11:36.1669348Z 1. Single-Node multi-process distributed training 2025-07-17T07:11:36.1669427Z 2025-07-17T07:11:36.1669513Z :: 2025-07-17T07:11:36.1669587Z 2025-07-17T07:11:36.1669892Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T07:11:36.1670080Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-07-17T07:11:36.1670201Z arguments of your training script) 2025-07-17T07:11:36.1670289Z 2025-07-17T07:11:36.1670494Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-07-17T07:11:36.1670566Z 2025-07-17T07:11:36.1670652Z 2025-07-17T07:11:36.1670792Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-07-17T07:11:36.1670864Z 2025-07-17T07:11:36.1670954Z :: 2025-07-17T07:11:36.1671029Z 2025-07-17T07:11:36.1671256Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T07:11:36.1671425Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-07-17T07:11:36.1671630Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-07-17T07:11:36.1671942Z and all other arguments of your training script) 2025-07-17T07:11:36.1672021Z 2025-07-17T07:11:36.1672097Z Node 2: 2025-07-17T07:11:36.1672183Z 2025-07-17T07:11:36.1672263Z :: 2025-07-17T07:11:36.1672339Z 2025-07-17T07:11:36.1672586Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-07-17T07:11:36.1672740Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-07-17T07:11:36.1672943Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-07-17T07:11:36.1673105Z and all other arguments of your training script) 2025-07-17T07:11:36.1673184Z 2025-07-17T07:11:36.1673363Z 3. To look up what optional arguments this module offers: 2025-07-17T07:11:36.1673443Z 2025-07-17T07:11:36.1673521Z :: 2025-07-17T07:11:36.1673610Z 2025-07-17T07:11:36.1673749Z python -m torch.distributed.launch --help 2025-07-17T07:11:36.1673825Z 2025-07-17T07:11:36.1673918Z 2025-07-17T07:11:36.1674020Z **Important Notices:** 2025-07-17T07:11:36.1674097Z 2025-07-17T07:11:36.1674296Z 1. This utility and multi-process distributed (single-node or 2025-07-17T07:11:36.1674546Z multi-node) GPU training currently only achieves the best performance using 2025-07-17T07:11:36.1674792Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-07-17T07:11:36.1674897Z use for GPU training. 2025-07-17T07:11:36.1674975Z 2025-07-17T07:11:36.1675197Z 2. In your training program, you must parse the command-line argument: 2025-07-17T07:11:36.1675428Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-07-17T07:11:36.1675652Z If your training program uses GPUs, you should ensure that your code only 2025-07-17T07:11:36.1675912Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-07-17T07:11:36.1675989Z 2025-07-17T07:11:36.1676095Z Parsing the local_rank argument 2025-07-17T07:11:36.1676182Z 2025-07-17T07:11:36.1676261Z :: 2025-07-17T07:11:36.1676336Z 2025-07-17T07:11:36.1676438Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1676526Z >>> import argparse 2025-07-17T07:11:36.1676655Z >>> parser = argparse.ArgumentParser() 2025-07-17T07:11:36.1676842Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-07-17T07:11:36.1676943Z >>> args = parser.parse_args() 2025-07-17T07:11:36.1677028Z 2025-07-17T07:11:36.1677149Z Set your device to local rank using either 2025-07-17T07:11:36.1677222Z 2025-07-17T07:11:36.1677312Z :: 2025-07-17T07:11:36.1677389Z 2025-07-17T07:11:36.1677582Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-07-17T07:11:36.1677704Z 2025-07-17T07:11:36.1677776Z or 2025-07-17T07:11:36.1677848Z 2025-07-17T07:11:36.1677937Z :: 2025-07-17T07:11:36.1678010Z 2025-07-17T07:11:36.1678226Z >>> with torch.cuda.device(args.local_rank): 2025-07-17T07:11:36.1678330Z >>> # your code to run 2025-07-17T07:11:36.1678410Z >>> ... 2025-07-17T07:11:36.1678496Z 2025-07-17T07:11:36.1678597Z .. versionchanged:: 2.0.0 2025-07-17T07:11:36.1678672Z 2025-07-17T07:11:36.1678930Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-07-17T07:11:36.1679165Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-07-17T07:11:36.1679304Z previously used underscored ``--local_rank``. 2025-07-17T07:11:36.1679396Z 2025-07-17T07:11:36.1679632Z For backward compatibility, it may be necessary for users to handle both 2025-07-17T07:11:36.1679900Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-07-17T07:11:36.1680131Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-07-17T07:11:36.1680385Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-07-17T07:11:36.1680626Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-07-17T07:11:36.1680773Z including ``"--local-rank"`` should be sufficient. 2025-07-17T07:11:36.1680851Z 2025-07-17T07:11:36.1681095Z 3. In your training program, you are supposed to call the following function 2025-07-17T07:11:36.1681331Z at the beginning to start the distributed backend. It is strongly recommended 2025-07-17T07:11:36.1681546Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-07-17T07:11:36.1681747Z but ``env://`` is the one that is officially supported by this module. 2025-07-17T07:11:36.1681827Z 2025-07-17T07:11:36.1681918Z :: 2025-07-17T07:11:36.1681996Z 2025-07-17T07:11:36.1682198Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-07-17T07:11:36.1682343Z >>> init_method='env://') 2025-07-17T07:11:36.1682424Z 2025-07-17T07:11:36.1682660Z 4. In your training program, you can either use regular distributed functions 2025-07-17T07:11:36.1682905Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-07-17T07:11:36.1683107Z training program uses GPUs for training and you would like to use 2025-07-17T07:11:36.1683301Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-07-17T07:11:36.1683404Z here is how to configure it. 2025-07-17T07:11:36.1683482Z 2025-07-17T07:11:36.1683574Z :: 2025-07-17T07:11:36.1683650Z 2025-07-17T07:11:36.1683841Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-07-17T07:11:36.1683992Z >>> device_ids=[args.local_rank], 2025-07-17T07:11:36.1684156Z >>> output_device=args.local_rank) 2025-07-17T07:11:36.1684232Z 2025-07-17T07:11:36.1684485Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-07-17T07:11:36.1684711Z that your code will be operating on. This is generally the local rank of the 2025-07-17T07:11:36.1685016Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-07-17T07:11:36.1685272Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-07-17T07:11:36.1685352Z utility 2025-07-17T07:11:36.1685440Z 2025-07-17T07:11:36.1685682Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-07-17T07:11:36.1685891Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-07-17T07:11:36.1686115Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-07-17T07:11:36.1686342Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-07-17T07:11:36.1686524Z will not pass ``--local-rank`` when you specify this flag. 2025-07-17T07:11:36.1686650Z 2025-07-17T07:11:36.1686739Z .. warning:: 2025-07-17T07:11:36.1686830Z 2025-07-17T07:11:36.1687030Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-07-17T07:11:36.1687217Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-07-17T07:11:36.1687349Z write to a networked filesystem. See 2025-07-17T07:11:36.1687566Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-07-17T07:11:36.1687728Z how things can go wrong if you don't do this correctly. 2025-07-17T07:11:36.1687817Z 2025-07-17T07:11:36.1687895Z 2025-07-17T07:11:36.1687970Z 2025-07-17T07:11:36.1688056Z 2025-07-17T07:11:36.1688303Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1688400Z 2025-07-17T07:11:36.1688496Z warnings.warn(msg) 2025-07-17T07:11:36.1688571Z 2025-07-17T07:11:36.1688794Z --- Parse Warning: 53 / 136 --- 2025-07-17T07:11:36.1689839Z /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. 2025-07-17T07:11:36.1690099Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1690183Z 2025-07-17T07:11:36.1690415Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-07-17T07:11:36.1690569Z Needs to be called on all ranks in an SPMD fashion. 2025-07-17T07:11:36.1690645Z 2025-07-17T07:11:36.1690722Z Args: 2025-07-17T07:11:36.1691002Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-07-17T07:11:36.1691173Z of shards that represent the local shards on this rank. 2025-07-17T07:11:36.1691399Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-07-17T07:11:36.1691528Z shape of the overall sharded tensor. 2025-07-17T07:11:36.1691603Z 2025-07-17T07:11:36.1691687Z Keyword args: 2025-07-17T07:11:36.1691963Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-07-17T07:11:36.1692084Z the default process group will be used. 2025-07-17T07:11:36.1692264Z init_rrefs (bool, optional): Whether or not to initialize 2025-07-17T07:11:36.1692470Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-07-17T07:11:36.1692656Z Need to initialize the RPC Framework if specified as ``True``. 2025-07-17T07:11:36.1692763Z Default: ``False``. 2025-07-17T07:11:36.1692840Z 2025-07-17T07:11:36.1692949Z Returns: 2025-07-17T07:11:36.1693111Z A :class:`ShardedTensor` object handle on this rank 2025-07-17T07:11:36.1693191Z 2025-07-17T07:11:36.1693270Z 2025-07-17T07:11:36.1693368Z Examples: 2025-07-17T07:11:36.1693625Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-07-17T07:11:36.1693823Z each shard have a (5, 5) local tensor, we can do it like below: 2025-07-17T07:11:36.1693905Z 2025-07-17T07:11:36.1693992Z on rank 0: 2025-07-17T07:11:36.1694128Z >>> # xdoctest: +SKIP("not distributed") 2025-07-17T07:11:36.1694251Z >>> local_shard_metadata = ShardMetadata( 2025-07-17T07:11:36.1694353Z >>> shard_offsets=[0, 0], 2025-07-17T07:11:36.1694468Z >>> shard_lengths=[5, 5], 2025-07-17T07:11:36.1694574Z >>> placement="rank:0/cuda:0" 2025-07-17T07:11:36.1694654Z >>> ) 2025-07-17T07:11:36.1694863Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-07-17T07:11:36.1695085Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-07-17T07:11:36.1695232Z 2025-07-17T07:11:36.1695334Z on rank 1: 2025-07-17T07:11:36.1695521Z >>> # xdoctest: +SKIP("not distributed") 2025-07-17T07:11:36.1695655Z >>> local_shard_metadata = ShardMetadata( 2025-07-17T07:11:36.1695756Z >>> shard_offsets=[5, 0], 2025-07-17T07:11:36.1695853Z >>> shard_lengths=[5, 5], 2025-07-17T07:11:36.1695974Z >>> placement="rank:1/cuda:1" 2025-07-17T07:11:36.1696051Z >>> ) 2025-07-17T07:11:36.1696241Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-07-17T07:11:36.1696448Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-07-17T07:11:36.1696523Z 2025-07-17T07:11:36.1696774Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1696864Z 2025-07-17T07:11:36.1696957Z warnings.warn(msg) 2025-07-17T07:11:36.1697049Z 2025-07-17T07:11:36.1697239Z --- Parse Warning: 54 / 136 --- 2025-07-17T07:11:36.1698336Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=835. 2025-07-17T07:11:36.1698609Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1698685Z 2025-07-17T07:11:36.1698942Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-07-17T07:11:36.1699067Z size and sharding spec on each rank. 2025-07-17T07:11:36.1699145Z 2025-07-17T07:11:36.1699237Z Args: 2025-07-17T07:11:36.1699461Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-07-17T07:11:36.1699715Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-07-17T07:11:36.1699894Z The specification describing how to shard the Tensor. 2025-07-17T07:11:36.1700056Z global_size (Sequence[int]): Size of the sharded tensor. 2025-07-17T07:11:36.1700305Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-07-17T07:11:36.1700407Z Default: None 2025-07-17T07:11:36.1700570Z init_rrefs (bool, optional): Whether or not to initialize 2025-07-17T07:11:36.1700787Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-07-17T07:11:36.1700975Z Need to initialize the RPC Framework if specified as ``True``. 2025-07-17T07:11:36.1701071Z Default: ``False``. 2025-07-17T07:11:36.1701158Z 2025-07-17T07:11:36.1701239Z Returns: 2025-07-17T07:11:36.1701479Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-07-17T07:11:36.1701606Z tensor stored in the current rank. 2025-07-17T07:11:36.1701709Z 2025-07-17T07:11:36.1701792Z Examples: 2025-07-17T07:11:36.1701897Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1702032Z >>> # All tensors below are of torch.int64 type. 2025-07-17T07:11:36.1702149Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:36.1702329Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-07-17T07:11:36.1702528Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-07-17T07:11:36.1702630Z >>> local_tensor 2025-07-17T07:11:36.1702728Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-07-17T07:11:36.1702828Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-07-17T07:11:36.1702932Z >>> sharding_dim = 0 2025-07-17T07:11:36.1703049Z >>> sharding_spec = ChunkShardingSpec( 2025-07-17T07:11:36.1703144Z dim=sharding_dim, 2025-07-17T07:11:36.1703249Z placements=[ 2025-07-17T07:11:36.1703342Z "rank:0/cuda:0", 2025-07-17T07:11:36.1703478Z "rank:1/cuda:1", 2025-07-17T07:11:36.1703574Z ], 2025-07-17T07:11:36.1703653Z ) 2025-07-17T07:11:36.1703800Z >>> st = ShardedTensor._init_from_local_tensor( 2025-07-17T07:11:36.1703972Z ... local_tensor, sharding_spec, [2, 4] 2025-07-17T07:11:36.1704055Z ... ) 2025-07-17T07:11:36.1704154Z >>> st 2025-07-17T07:11:36.1704246Z ShardedTensor( 2025-07-17T07:11:36.1704353Z ShardedTensorMetadata( 2025-07-17T07:11:36.1704458Z shards_metadata=[ 2025-07-17T07:11:36.1704729Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-07-17T07:11:36.1704992Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-07-17T07:11:36.1705083Z ], 2025-07-17T07:11:36.1705184Z size=torch.Size([2, 4]) 2025-07-17T07:11:36.1705280Z ) 2025-07-17T07:11:36.1705378Z >>> st.local_tensor() 2025-07-17T07:11:36.1705474Z tensor([1, 2, 3, 4]) # Rank 0 2025-07-17T07:11:36.1705580Z tensor([3, 4, 5, 6]) # Rank 1 2025-07-17T07:11:36.1705658Z 2025-07-17T07:11:36.1705930Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-07-17T07:11:36.1706179Z rank validations, and we only validate the local shard on the current rank. 2025-07-17T07:11:36.1706390Z We fully rely on the user to ensure local tensor is sharded based on the 2025-07-17T07:11:36.1706481Z sharding spec. 2025-07-17T07:11:36.1706570Z 2025-07-17T07:11:36.1706821Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1706908Z 2025-07-17T07:11:36.1707000Z warnings.warn(msg) 2025-07-17T07:11:36.1707078Z 2025-07-17T07:11:36.1707276Z --- Parse Warning: 55 / 136 --- 2025-07-17T07:11:36.1708326Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1076. 2025-07-17T07:11:36.1708594Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1708682Z 2025-07-17T07:11:36.1708929Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-07-17T07:11:36.1709034Z single local shard. 2025-07-17T07:11:36.1709109Z 2025-07-17T07:11:36.1709330Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-07-17T07:11:36.1709581Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-07-17T07:11:36.1709683Z we swap local shards directly. 2025-07-17T07:11:36.1709940Z For more generic cases, we merge different shards across different ranks and split 2025-07-17T07:11:36.1710200Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-07-17T07:11:36.1710303Z 2025-07-17T07:11:36.1710380Z Args: 2025-07-17T07:11:36.1710677Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-07-17T07:11:36.1710843Z specification describing how the tensor is sharded. 2025-07-17T07:11:36.1710932Z 2025-07-17T07:11:36.1711011Z Returns: 2025-07-17T07:11:36.1711209Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-07-17T07:11:36.1711295Z 2025-07-17T07:11:36.1711375Z Examples: 2025-07-17T07:11:36.1711471Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1711598Z >>> # We have 2 process groups, 2 ranks. 2025-07-17T07:11:36.1711766Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-07-17T07:11:36.1711886Z >>> tensor = torch.stack([tensor, tensor]) 2025-07-17T07:11:36.1711977Z >>> tensor 2025-07-17T07:11:36.1712117Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-07-17T07:11:36.1712244Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-07-17T07:11:36.1712356Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-07-17T07:11:36.1712522Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-07-17T07:11:36.1712631Z >>> sharding_dim = 0 2025-07-17T07:11:36.1712739Z >>> spec = ChunkShardingSpec( 2025-07-17T07:11:36.1712833Z dim=sharding_dim, 2025-07-17T07:11:36.1712935Z placements=[ 2025-07-17T07:11:36.1713026Z "rank:0/cuda:0", 2025-07-17T07:11:36.1713116Z "rank:1/cuda:1", 2025-07-17T07:11:36.1713215Z "rank:2/cuda:2", 2025-07-17T07:11:36.1713304Z "rank:3/cuda:3", 2025-07-17T07:11:36.1713383Z ], 2025-07-17T07:11:36.1713470Z ) 2025-07-17T07:11:36.1713573Z >>> current_offsets = [0] * 2 2025-07-17T07:11:36.1713690Z >>> current_offsets[0] = rank * 2 2025-07-17T07:11:36.1713805Z >>> shard_metadata = ShardMetadata( 2025-07-17T07:11:36.1713951Z shard_offsets=copy.deepcopy(current_offsets), 2025-07-17T07:11:36.1714068Z shard_sizes=tensor.size(), 2025-07-17T07:11:36.1714191Z placement=spec.placements[rank], 2025-07-17T07:11:36.1714269Z ) 2025-07-17T07:11:36.1714370Z >>> local_shards = [ 2025-07-17T07:11:36.1714452Z Shard( 2025-07-17T07:11:36.1714544Z tensor=tensor, 2025-07-17T07:11:36.1714657Z metadata=shard_metadata, 2025-07-17T07:11:36.1714734Z ) 2025-07-17T07:11:36.1714813Z ] 2025-07-17T07:11:36.1715049Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-07-17T07:11:36.1715143Z >>> sharding_dim = 1 2025-07-17T07:11:36.1715275Z >>> resharding_spec = ChunkShardingSpec( 2025-07-17T07:11:36.1715365Z dim=sharding_dim, 2025-07-17T07:11:36.1715458Z placements=[ 2025-07-17T07:11:36.1715560Z "rank:0/cuda:0", 2025-07-17T07:11:36.1715652Z "rank:1/cuda:1", 2025-07-17T07:11:36.1715745Z "rank:2/cuda:2", 2025-07-17T07:11:36.1715848Z "rank:3/cuda:3", 2025-07-17T07:11:36.1715927Z ], 2025-07-17T07:11:36.1716007Z ) 2025-07-17T07:11:36.1716126Z >>> st.reshard(resharding_spec) 2025-07-17T07:11:36.1716239Z >>> tensor = st.local_shards()[0].tensor 2025-07-17T07:11:36.1716333Z >>> tensor 2025-07-17T07:11:36.1716471Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-07-17T07:11:36.1716605Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-07-17T07:11:36.1716752Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-07-17T07:11:36.1716891Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-07-17T07:11:36.1716975Z 2025-07-17T07:11:36.1717240Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1717346Z 2025-07-17T07:11:36.1717441Z warnings.warn(msg) 2025-07-17T07:11:36.1717527Z 2025-07-17T07:11:36.1717717Z --- Parse Warning: 56 / 136 --- 2025-07-17T07:11:36.1718732Z /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. 2025-07-17T07:11:36.1718988Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1719062Z 2025-07-17T07:11:36.1719289Z Representation of a sharding plan, describes how to shard a module 2025-07-17T07:11:36.1719556Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-07-17T07:11:36.1719842Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-07-17T07:11:36.1720126Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-07-17T07:11:36.1720201Z 2025-07-17T07:11:36.1720290Z Args: 2025-07-17T07:11:36.1720602Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-07-17T07:11:36.1720769Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-07-17T07:11:36.1721057Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-07-17T07:11:36.1721306Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-07-17T07:11:36.1721423Z a parameter to a `ShardingSpec`. 2025-07-17T07:11:36.1721683Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-07-17T07:11:36.1721782Z to a `Sharder` object. 2025-07-17T07:11:36.1722120Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-07-17T07:11:36.1722380Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-07-17T07:11:36.1722614Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-07-17T07:11:36.1722716Z Default: `None` 2025-07-17T07:11:36.1722962Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-07-17T07:11:36.1723199Z a module's sharded output to be returned as a Tensor from its local shards to 2025-07-17T07:11:36.1723452Z ensure further processing in a data parallel fashion. ("" in list means the 2025-07-17T07:11:36.1723544Z root module). 2025-07-17T07:11:36.1723641Z Default: None 2025-07-17T07:11:36.1723722Z Example: 2025-07-17T07:11:36.1724000Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-07-17T07:11:36.1724294Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-07-17T07:11:36.1724367Z 2025-07-17T07:11:36.1724539Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-07-17T07:11:36.1724656Z >>> class MyModule(nn.Module): 2025-07-17T07:11:36.1724760Z >>> def __init__(self) -> None: 2025-07-17T07:11:36.1724865Z >>> super().__init__() 2025-07-17T07:11:36.1724963Z >>> self.fc1 = nn.Linear() 2025-07-17T07:11:36.1725061Z >>> self.gelu = nn.GELU() 2025-07-17T07:11:36.1725171Z >>> self.fc2 = nn.Linear() 2025-07-17T07:11:36.1725266Z >>> self.relu = nn.Linear() 2025-07-17T07:11:36.1725346Z >>> 2025-07-17T07:11:36.1725453Z >>> def forward(self, input): 2025-07-17T07:11:36.1725621Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-07-17T07:11:36.1725696Z 2025-07-17T07:11:36.1725781Z 2025-07-17T07:11:36.1725937Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-07-17T07:11:36.1726041Z >>> sharding_plan = ShardingPlan( 2025-07-17T07:11:36.1726129Z >>> plan={ 2025-07-17T07:11:36.1726227Z >>> "fc1.weight": spec1, 2025-07-17T07:11:36.1726334Z >>> "fc2.weight": spec2 2025-07-17T07:11:36.1726413Z >>> }, 2025-07-17T07:11:36.1726503Z >>> output_plan={ 2025-07-17T07:11:36.1726606Z >>> "fc2": output_spec 2025-07-17T07:11:36.1726683Z >>> }, 2025-07-17T07:11:36.1726789Z >>> return_local_tensor=["fc2"] 2025-07-17T07:11:36.1726881Z >>> ) 2025-07-17T07:11:36.1726955Z 2025-07-17T07:11:36.1727204Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1727287Z 2025-07-17T07:11:36.1727377Z warnings.warn(msg) 2025-07-17T07:11:36.1727451Z 2025-07-17T07:11:36.1727644Z --- Parse Warning: 57 / 136 --- 2025-07-17T07:11:36.1728835Z /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. 2025-07-17T07:11:36.1729104Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1729180Z 2025-07-17T07:11:36.1729283Z Run post-localSGD algorithm. 2025-07-17T07:11:36.1729366Z 2025-07-17T07:11:36.1729600Z This DDP communication hook is used for running post-localSGD algorithm, 2025-07-17T07:11:36.1729755Z by combining with a model averaging component (e.g., 2025-07-17T07:11:36.1730086Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-07-17T07:11:36.1730197Z that runs after the optimizer step. 2025-07-17T07:11:36.1730281Z 2025-07-17T07:11:36.1730361Z Args: 2025-07-17T07:11:36.1730585Z state (PostLocalSGDState): State information to run post-localSGD. 2025-07-17T07:11:36.1730870Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-07-17T07:11:36.1731293Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-07-17T07:11:36.1731537Z Note that since DDP comm hook only supports single process single device mode, 2025-07-17T07:11:36.1731692Z only exactly one tensor is stored in this bucket. 2025-07-17T07:11:36.1731767Z 2025-07-17T07:11:36.1731853Z Returns: 2025-07-17T07:11:36.1732094Z Future handler of the communication, which updates the gradients in place. 2025-07-17T07:11:36.1732166Z 2025-07-17T07:11:36.1732259Z Example:: 2025-07-17T07:11:36.1732352Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1732604Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-07-17T07:11:36.1732732Z start_localSGD_iter=10) 2025-07-17T07:11:36.1732901Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T07:11:36.1733237Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-07-17T07:11:36.1733587Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-07-17T07:11:36.1733661Z 2025-07-17T07:11:36.1733918Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1734061Z 2025-07-17T07:11:36.1734187Z warnings.warn(msg) 2025-07-17T07:11:36.1734340Z 2025-07-17T07:11:36.1734558Z --- Parse Warning: 58 / 136 --- 2025-07-17T07:11:36.1735783Z /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. 2025-07-17T07:11:36.1736322Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1736453Z 2025-07-17T07:11:36.1736642Z Implement PowerSGD algorithm. 2025-07-17T07:11:36.1736755Z 2025-07-17T07:11:36.1737014Z This DDP communication hook implements PowerSGD gradient compression 2025-07-17T07:11:36.1737316Z algorithm described in the `paper `_. 2025-07-17T07:11:36.1737637Z Once gradient tensors are aggregated across all workers, this hook applies 2025-07-17T07:11:36.1737791Z compression as follows: 2025-07-17T07:11:36.1737965Z 2025-07-17T07:11:36.1738438Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-07-17T07:11:36.1738547Z 2025-07-17T07:11:36.1739033Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-07-17T07:11:36.1739211Z 2025-07-17T07:11:36.1739715Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-07-17T07:11:36.1739874Z 2025-07-17T07:11:36.1740023Z 2. Handles uncompressed tensors: 2025-07-17T07:11:36.1740177Z 2025-07-17T07:11:36.1740703Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-07-17T07:11:36.1740904Z 2025-07-17T07:11:36.1741278Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-07-17T07:11:36.1741390Z 2025-07-17T07:11:36.1741705Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-07-17T07:11:36.1741815Z 2025-07-17T07:11:36.1742064Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-07-17T07:11:36.1742499Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-07-17T07:11:36.1742608Z 2025-07-17T07:11:36.1742793Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-07-17T07:11:36.1742948Z 2025-07-17T07:11:36.1743088Z 3.3. Allreduces Ps as a batch; 2025-07-17T07:11:36.1743247Z 2025-07-17T07:11:36.1743429Z 3.4. Orthogonalizes each P in Ps; 2025-07-17T07:11:36.1743539Z 2025-07-17T07:11:36.1743815Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-07-17T07:11:36.1743924Z 2025-07-17T07:11:36.1744060Z 3.6. Allreduces Qs as a batch; 2025-07-17T07:11:36.1744221Z 2025-07-17T07:11:36.1744566Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-07-17T07:11:36.1744683Z 2025-07-17T07:11:36.1745172Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-07-17T07:11:36.1745491Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-07-17T07:11:36.1745987Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-07-17T07:11:36.1746131Z 2025-07-17T07:11:36.1746259Z Args: 2025-07-17T07:11:36.1746782Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-07-17T07:11:36.1747176Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-07-17T07:11:36.1747360Z and ``min_compression_rate``. 2025-07-17T07:11:36.1747795Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-07-17T07:11:36.1748109Z Note that since DDP comm hook only supports single process single device mode, 2025-07-17T07:11:36.1748395Z only exactly one tensor is stored in this bucket. 2025-07-17T07:11:36.1748504Z 2025-07-17T07:11:36.1748617Z Returns: 2025-07-17T07:11:36.1748938Z Future handler of the communication, which updates the gradients in place. 2025-07-17T07:11:36.1749028Z 2025-07-17T07:11:36.1749278Z Example:: 2025-07-17T07:11:36.1749407Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1749712Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-07-17T07:11:36.1749949Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-07-17T07:11:36.1750139Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-07-17T07:11:36.1750230Z 2025-07-17T07:11:36.1750616Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1750727Z 2025-07-17T07:11:36.1750855Z warnings.warn(msg) 2025-07-17T07:11:36.1751039Z 2025-07-17T07:11:36.1751290Z --- Parse Warning: 59 / 136 --- 2025-07-17T07:11:36.1752574Z /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. 2025-07-17T07:11:36.1752887Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1752999Z 2025-07-17T07:11:36.1753258Z Averages parameters periodically after the warm-up stage. 2025-07-17T07:11:36.1753376Z 2025-07-17T07:11:36.1753687Z This can be used for running `post-local SGD `_, 2025-07-17T07:11:36.1753952Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-07-17T07:11:36.1754234Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-07-17T07:11:36.1754386Z 2025-07-17T07:11:36.1754503Z Args: 2025-07-17T07:11:36.1754732Z period (int): The number of steps per model averaging. 2025-07-17T07:11:36.1755062Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-07-17T07:11:36.1755269Z Otherwise, only DDP needs to be used. 2025-07-17T07:11:36.1755564Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-07-17T07:11:36.1755713Z model averaging is skipped. 2025-07-17T07:11:36.1755938Z process_group: The process group to be used for all-reduce. 2025-07-17T07:11:36.1756157Z If ``None``, the default process group, which 2025-07-17T07:11:36.1756361Z is created by :func:`torch.distributed.init_process_group`, 2025-07-17T07:11:36.1756543Z will be used. (default: ``None``) 2025-07-17T07:11:36.1756712Z 2025-07-17T07:11:36.1756831Z Example:: 2025-07-17T07:11:36.1756953Z 2025-07-17T07:11:36.1757156Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1757263Z >>> import torch 2025-07-17T07:11:36.1757498Z >>> import torch.distributed as dist 2025-07-17T07:11:36.1757846Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-07-17T07:11:36.1758155Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-07-17T07:11:36.1758328Z >>> import torch.nn as nn 2025-07-17T07:11:36.1758440Z >>> 2025-07-17T07:11:36.1758628Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-07-17T07:11:36.1767413Z >>> torch.cuda.set_device(rank) 2025-07-17T07:11:36.1767624Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-07-17T07:11:36.1767780Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T07:11:36.1767929Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T07:11:36.1768014Z >>> ) 2025-07-17T07:11:36.1768169Z >>> # Register a post-localSGD communication hook. 2025-07-17T07:11:36.1768575Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-07-17T07:11:36.1768750Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T07:11:36.1768841Z >>> 2025-07-17T07:11:36.1769108Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-07-17T07:11:36.1769264Z >>> # After 100 steps, run model averaging every 4 steps. 2025-07-17T07:11:36.1769591Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T07:11:36.1769840Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-07-17T07:11:36.1769945Z >>> for step in range(0, 200): 2025-07-17T07:11:36.1770055Z >>> optimizer.zero_grad() 2025-07-17T07:11:36.1770163Z >>> loss = loss_fn(output, labels) 2025-07-17T07:11:36.1770297Z >>> loss.backward() 2025-07-17T07:11:36.1770395Z >>> optimizer.step() 2025-07-17T07:11:36.1770586Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-07-17T07:11:36.1770849Z >>> # inter-node communication only occurs every 4 iterations after 2025-07-17T07:11:36.1770976Z >>> # the initial ``warmup_steps`` period. 2025-07-17T07:11:36.1771133Z >>> averager.average_parameters(model.parameters()) 2025-07-17T07:11:36.1771220Z 2025-07-17T07:11:36.1771469Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1771551Z 2025-07-17T07:11:36.1771642Z warnings.warn(msg) 2025-07-17T07:11:36.1771718Z 2025-07-17T07:11:36.1772124Z --- Parse Warning: 60 / 136 --- 2025-07-17T07:11:36.1773354Z /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. 2025-07-17T07:11:36.1773622Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1773705Z 2025-07-17T07:11:36.1774041Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-07-17T07:11:36.1774125Z 2025-07-17T07:11:36.1774432Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-07-17T07:11:36.1774634Z by using different periods concurrently after the warm-up stage. 2025-07-17T07:11:36.1775046Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-07-17T07:11:36.1775452Z that supports `post-local SGD `_, which essentially only supports 2025-07-17T07:11:36.1775762Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-07-17T07:11:36.1776125Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-07-17T07:11:36.1776426Z Similarly, the process groups within this class do not have such an intra-machine process 2025-07-17T07:11:36.1776708Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-07-17T07:11:36.1776781Z 2025-07-17T07:11:36.1776860Z Args: 2025-07-17T07:11:36.1777127Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-07-17T07:11:36.1777323Z process group size, used for initializing process groups of 2025-07-17T07:11:36.1777548Z different sizes in a hierarchy to average parameters concurrently. 2025-07-17T07:11:36.1777765Z Particularly, at each iteration, there will be at most a single 2025-07-17T07:11:36.1777996Z process group that runs averaging -- the period of such group should 2025-07-17T07:11:36.1778302Z have the largest period which the current step can be divided by. 2025-07-17T07:11:36.1778469Z For example, if the dict has three keys: 2, 4, and 8, 2025-07-17T07:11:36.1778672Z then this means totally three process groups will be created to 2025-07-17T07:11:36.1778886Z average parameters every 2, 4, and 8 iterations, respectively. 2025-07-17T07:11:36.1779073Z At the 4th iteration, only the second process group will run 2025-07-17T07:11:36.1779258Z averaging, because the first process group should be a 2025-07-17T07:11:36.1779471Z subset of the second process group, and no need to execute the first 2025-07-17T07:11:36.1779592Z process group redundantly. 2025-07-17T07:11:36.1779836Z On the other hand, the third process group can only be triggered 2025-07-17T07:11:36.1780119Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-07-17T07:11:36.1780422Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-07-17T07:11:36.1780874Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-07-17T07:11:36.1781046Z If ``None``, the default process group, which is created 2025-07-17T07:11:36.1781261Z by :func:`torch.distributed.init_process_group`, will be used. 2025-07-17T07:11:36.1781377Z (default: ``None``) 2025-07-17T07:11:36.1781451Z 2025-07-17T07:11:36.1781557Z Example:: 2025-07-17T07:11:36.1781673Z >>> # xdoctest: +SKIP('undefined rank') 2025-07-17T07:11:36.1781791Z >>> from collections import OrderedDict 2025-07-17T07:11:36.1781891Z >>> import torch 2025-07-17T07:11:36.1782006Z >>> import torch.distributed as dist 2025-07-17T07:11:36.1782287Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-07-17T07:11:36.1782385Z >>> PostLocalSGDState, 2025-07-17T07:11:36.1782478Z >>> post_localSGD_hook, 2025-07-17T07:11:36.1782566Z >>> ) 2025-07-17T07:11:36.1782935Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-07-17T07:11:36.1783031Z >>> import torch.nn as nn 2025-07-17T07:11:36.1783116Z >>> 2025-07-17T07:11:36.1783288Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-07-17T07:11:36.1783400Z >>> torch.cuda.set_device(rank) 2025-07-17T07:11:36.1783529Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-07-17T07:11:36.1783678Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T07:11:36.1783820Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T07:11:36.1783897Z >>> ) 2025-07-17T07:11:36.1784039Z >>> # Register a post-localSGD communication hook. 2025-07-17T07:11:36.1784319Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-07-17T07:11:36.1784433Z >>> subgroup, _ = dist.new_subgroups() 2025-07-17T07:11:36.1784740Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-07-17T07:11:36.1784905Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T07:11:36.1784981Z >>> 2025-07-17T07:11:36.1785269Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-07-17T07:11:36.1785384Z >>> # the 16 processes every 16 iterations. 2025-07-17T07:11:36.1785668Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-07-17T07:11:36.1785990Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-07-17T07:11:36.1786311Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T07:11:36.1786573Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-07-17T07:11:36.1786729Z >>> # After 100 steps, run model averaging at two levels. 2025-07-17T07:11:36.1786828Z >>> for step in range(0, 200): 2025-07-17T07:11:36.1786939Z >>> optimizer.zero_grad() 2025-07-17T07:11:36.1787047Z >>> loss = loss_fn(output, labels) 2025-07-17T07:11:36.1787139Z >>> loss.backward() 2025-07-17T07:11:36.1787242Z >>> optimizer.step() 2025-07-17T07:11:36.1787390Z >>> # Average parameters after ``optimizer.step()``. 2025-07-17T07:11:36.1787709Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-07-17T07:11:36.1787872Z >>> averager.average_parameters(model.parameters()) 2025-07-17T07:11:36.1787948Z 2025-07-17T07:11:36.1788096Z .. warning :: 2025-07-17T07:11:36.1788346Z The last group size in the dict must be the size of the provided ``process_group``, 2025-07-17T07:11:36.1788576Z which indicates model averaging at the highest level of the hierarchy. 2025-07-17T07:11:36.1788888Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-07-17T07:11:36.1788962Z 2025-07-17T07:11:36.1789048Z .. warning :: 2025-07-17T07:11:36.1789288Z `HierarchicalModelAverager` is experimental and subject to change. 2025-07-17T07:11:36.1789362Z 2025-07-17T07:11:36.1789611Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1789697Z 2025-07-17T07:11:36.1789790Z warnings.warn(msg) 2025-07-17T07:11:36.1789877Z 2025-07-17T07:11:36.1790087Z --- Parse Warning: 61 / 136 --- 2025-07-17T07:11:36.1791187Z /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. 2025-07-17T07:11:36.1791456Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1791528Z 2025-07-17T07:11:36.1791813Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-07-17T07:11:36.1792064Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-07-17T07:11:36.1792137Z 2025-07-17T07:11:36.1792307Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-07-17T07:11:36.1792379Z 2025-07-17T07:11:36.1792463Z .. warning:: 2025-07-17T07:11:36.1792641Z Current implementation only supports loading Tensors. 2025-07-17T07:11:36.1792712Z 2025-07-17T07:11:36.1792824Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.1792927Z >>> sd = {"mode": model} 2025-07-17T07:11:36.1793011Z >>> dcp.load( 2025-07-17T07:11:36.1793091Z >>> sd, 2025-07-17T07:11:36.1793254Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-07-17T07:11:36.1793373Z >>> planner=DynamicMetaLoadPlanner(), 2025-07-17T07:11:36.1793482Z >>> checkpoint_id="path_to_model.pt" 2025-07-17T07:11:36.1793569Z >>> ) 2025-07-17T07:11:36.1793643Z 2025-07-17T07:11:36.1793900Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1793975Z 2025-07-17T07:11:36.1794067Z warnings.warn(msg) 2025-07-17T07:11:36.1794155Z 2025-07-17T07:11:36.1794331Z --- Parse Warning: 62 / 136 --- 2025-07-17T07:11:36.1795378Z /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. 2025-07-17T07:11:36.1795681Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1795753Z 2025-07-17T07:11:36.1796130Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-07-17T07:11:36.1796450Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-07-17T07:11:36.1796560Z metadata file, like Torch Save files. 2025-07-17T07:11:36.1796644Z 2025-07-17T07:11:36.1796815Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-07-17T07:11:36.1796889Z 2025-07-17T07:11:36.1796974Z .. warning:: 2025-07-17T07:11:36.1797156Z Current implementation only supports loading Tensors. 2025-07-17T07:11:36.1797259Z 2025-07-17T07:11:36.1797370Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.1797472Z >>> sd = {"mode": model} 2025-07-17T07:11:36.1797552Z >>> dcp.load( 2025-07-17T07:11:36.1797632Z >>> sd, 2025-07-17T07:11:36.1797838Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-07-17T07:11:36.1797960Z >>> planner=DynamicMetaLoadPlanner(), 2025-07-17T07:11:36.1798072Z >>> checkpoint_id="path_to_model.pt" 2025-07-17T07:11:36.1798159Z >>> ) 2025-07-17T07:11:36.1798238Z 2025-07-17T07:11:36.1798501Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1798573Z 2025-07-17T07:11:36.1798664Z warnings.warn(msg) 2025-07-17T07:11:36.1798748Z 2025-07-17T07:11:36.1798924Z --- Parse Warning: 63 / 136 --- 2025-07-17T07:11:36.1799996Z /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. 2025-07-17T07:11:36.1800268Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1800345Z 2025-07-17T07:11:36.1800560Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-07-17T07:11:36.1800631Z 2025-07-17T07:11:36.1800793Z This is the current recommended way to checkpoint FSDP. 2025-07-17T07:11:36.1800894Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1801039Z >>> import torch.distributed.checkpoint as dist_cp 2025-07-17T07:11:36.1801118Z >>> # Save 2025-07-17T07:11:36.1801220Z >>> model: torch.nn.Model 2025-07-17T07:11:36.1801330Z >>> optim_params = model.parameters() 2025-07-17T07:11:36.1801469Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-07-17T07:11:36.1801554Z >>> # Save 2025-07-17T07:11:36.1801766Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-07-17T07:11:36.1801860Z >>> state_dict = { 2025-07-17T07:11:36.1802023Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-07-17T07:11:36.1802128Z >>> "model": model.state_dict() 2025-07-17T07:11:36.1802220Z >>> } 2025-07-17T07:11:36.1802318Z >>> dist_cp.save_state_dict( 2025-07-17T07:11:36.1802418Z >>> state_dict=optim_state, 2025-07-17T07:11:36.1802600Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-07-17T07:11:36.1802729Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-07-17T07:11:36.1802808Z >>> ) 2025-07-17T07:11:36.1802894Z >>> 2025-07-17T07:11:36.1802973Z >>> # Load 2025-07-17T07:11:36.1803197Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-07-17T07:11:36.1803329Z >>> model_state_dict = model_tp.state_dict() 2025-07-17T07:11:36.1803422Z >>> checkpoint = { 2025-07-17T07:11:36.1803537Z >>> "model": model_state_dict 2025-07-17T07:11:36.1803639Z >>> } 2025-07-17T07:11:36.1803739Z >>> dist_cp.load_state_dict( 2025-07-17T07:11:36.1803848Z >>> state_dict=checkpoint, 2025-07-17T07:11:36.1804037Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-07-17T07:11:36.1804167Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-07-17T07:11:36.1804255Z >>> ) 2025-07-17T07:11:36.1804401Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-07-17T07:11:36.1804478Z >>> 2025-07-17T07:11:36.1804656Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-07-17T07:11:36.1804751Z >>> model_state_dict, 2025-07-17T07:11:36.1804870Z >>> optimizer_key="optimizer", 2025-07-17T07:11:36.1805046Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-07-17T07:11:36.1805122Z >>> ) 2025-07-17T07:11:36.1805210Z >>> 2025-07-17T07:11:36.1805377Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-07-17T07:11:36.1805502Z >>> model, optim, optim_state["optimizer"] 2025-07-17T07:11:36.1805590Z >>> ) 2025-07-17T07:11:36.1805667Z >>> 2025-07-17T07:11:36.1805845Z >>> optim.load_state_dict(flattened_osd) 2025-07-17T07:11:36.1805929Z 2025-07-17T07:11:36.1806177Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1806255Z 2025-07-17T07:11:36.1806353Z warnings.warn(msg) 2025-07-17T07:11:36.1806431Z 2025-07-17T07:11:36.1806621Z --- Parse Warning: 64 / 136 --- 2025-07-17T07:11:36.1807598Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=122. 2025-07-17T07:11:36.1807858Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1807947Z 2025-07-17T07:11:36.1808232Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-07-17T07:11:36.1808308Z 2025-07-17T07:11:36.1808613Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-07-17T07:11:36.1808689Z 2025-07-17T07:11:36.1808973Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-07-17T07:11:36.1809084Z will be visible to the whole process. 2025-07-17T07:11:36.1809159Z 2025-07-17T07:11:36.1809441Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-07-17T07:11:36.1809515Z 2025-07-17T07:11:36.1809633Z 1) set_up_planner - called on all ranks. 2025-07-17T07:11:36.1809765Z Signals the start of a checkpoint save. 2025-07-17T07:11:36.1809840Z 2025-07-17T07:11:36.1809961Z 2) create_local_plan - called on all ranks. 2025-07-17T07:11:36.1810257Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-07-17T07:11:36.1810335Z 2025-07-17T07:11:36.1810523Z 3) create_global_plan - called on the coordinator rank only. 2025-07-17T07:11:36.1810723Z Takes the SavePlan from all ranks and make any global decision. 2025-07-17T07:11:36.1810801Z 2025-07-17T07:11:36.1810920Z 4) finish_plan - called on all ranks. 2025-07-17T07:11:36.1811133Z This gives each rank a chance to adjust to global planning decisions. 2025-07-17T07:11:36.1811210Z 2025-07-17T07:11:36.1811370Z 5) resolve_data - called multiple times on each rank 2025-07-17T07:11:36.1811570Z Lookups a value on the `state_dict` for the storage layer to write. 2025-07-17T07:11:36.1811645Z 2025-07-17T07:11:36.1811953Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-07-17T07:11:36.1812132Z most changes can be expressed by changes in a single method. 2025-07-17T07:11:36.1812218Z 2025-07-17T07:11:36.1812361Z There are 3 usual patterns of extension: 2025-07-17T07:11:36.1812439Z 2025-07-17T07:11:36.1812699Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-07-17T07:11:36.1812927Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-07-17T07:11:36.1813004Z 2025-07-17T07:11:36.1813126Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.1813256Z >>> class RenamePlanner(DefaultSavePlanner): 2025-07-17T07:11:36.1813356Z >>> def set_up_planner( 2025-07-17T07:11:36.1813447Z >>> self, 2025-07-17T07:11:36.1813554Z >>> state_dict: STATE_DICT_TYPE, 2025-07-17T07:11:36.1813689Z >>> storage_meta: Optional[StorageMeta], 2025-07-17T07:11:36.1813790Z >>> is_coordinator: bool, 2025-07-17T07:11:36.1813876Z >>> ) -> None: 2025-07-17T07:11:36.1813990Z >>> # prefix all keys with `foo_`` 2025-07-17T07:11:36.1814288Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-07-17T07:11:36.1814394Z 2025-07-17T07:11:36.1814739Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-07-17T07:11:36.1814863Z 2025-07-17T07:11:36.1814975Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.1815106Z >>> class FP16Planner(DefaultSavePlanner): 2025-07-17T07:11:36.1815296Z >>> def create_local_plan(self): 2025-07-17T07:11:36.1815426Z >>> plan = super().create_local_plan() 2025-07-17T07:11:36.1815519Z >>> for p in plan: 2025-07-17T07:11:36.1815629Z >>> if p.tensor_data is not None: 2025-07-17T07:11:36.1815796Z >>> p.tensor_data.properties.dtype = torch.float16 2025-07-17T07:11:36.1815884Z >>> return plan 2025-07-17T07:11:36.1815961Z >>> 2025-07-17T07:11:36.1816079Z >>> def resolve_data(self, write_item): 2025-07-17T07:11:36.1816206Z >>> item = super().resolve_data(write_item) 2025-07-17T07:11:36.1816480Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-07-17T07:11:36.1816564Z 2025-07-17T07:11:36.1816905Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-07-17T07:11:36.1816992Z 2025-07-17T07:11:36.1817102Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.1817211Z >>> from itertools import zip_longest 2025-07-17T07:11:36.1817326Z >>> from dataclasses import replace 2025-07-17T07:11:36.1817489Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-07-17T07:11:36.1817761Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-07-17T07:11:36.1817896Z >>> # This sample doesn't handle ShardedTensors 2025-07-17T07:11:36.1818020Z >>> def create_global_plan(self, all_plans): 2025-07-17T07:11:36.1818169Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-07-17T07:11:36.1818273Z >>> items_per_rank = [ 2025-07-17T07:11:36.1818405Z >>> [item for item in items if item is not None] 2025-07-17T07:11:36.1818572Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-07-17T07:11:36.1818652Z >>> ] 2025-07-17T07:11:36.1818743Z >>> all_plans = [ 2025-07-17T07:11:36.1818862Z >>> replace(plan, items=items) 2025-07-17T07:11:36.1819046Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-07-17T07:11:36.1819125Z >>> ] 2025-07-17T07:11:36.1819270Z >>> return super().create_global_plan(all_plans) 2025-07-17T07:11:36.1819344Z 2025-07-17T07:11:36.1819606Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-07-17T07:11:36.1819880Z accomplished by having each rank contribute their data items in the local plan and 2025-07-17T07:11:36.1819992Z the global planner aggregate them: 2025-07-17T07:11:36.1820108Z 2025-07-17T07:11:36.1820217Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.1820371Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-07-17T07:11:36.1820512Z >>> def create_local_plan(self) -> SavePlan: 2025-07-17T07:11:36.1820625Z >>> plan = super().create_local_plan() 2025-07-17T07:11:36.1820783Z >>> return replace(plan, planner_data="per-rank-data") 2025-07-17T07:11:36.1820870Z >>> 2025-07-17T07:11:36.1821165Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-07-17T07:11:36.1821369Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-07-17T07:11:36.1821521Z >>> merged_data = [p.planner_data for p in global_plan] 2025-07-17T07:11:36.1821687Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-07-17T07:11:36.1821831Z >>> return global_plan, metadata 2025-07-17T07:11:36.1821910Z 2025-07-17T07:11:36.1822157Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1822243Z 2025-07-17T07:11:36.1822337Z warnings.warn(msg) 2025-07-17T07:11:36.1822460Z 2025-07-17T07:11:36.1822666Z --- Parse Warning: 65 / 136 --- 2025-07-17T07:11:36.1823640Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=305. 2025-07-17T07:11:36.1823912Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1823990Z 2025-07-17T07:11:36.1824274Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-07-17T07:11:36.1824362Z 2025-07-17T07:11:36.1824648Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-07-17T07:11:36.1824731Z 2025-07-17T07:11:36.1825013Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-07-17T07:11:36.1825124Z will be visible to the whole process. 2025-07-17T07:11:36.1825211Z 2025-07-17T07:11:36.1825485Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-07-17T07:11:36.1825560Z 2025-07-17T07:11:36.1825685Z 1) set_up_planner - called on all ranks. 2025-07-17T07:11:36.1825810Z Signals the start of loading a checkpoint. 2025-07-17T07:11:36.1825890Z 2025-07-17T07:11:36.1826012Z 2) create_local_plan - called on all ranks. 2025-07-17T07:11:36.1826293Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-07-17T07:11:36.1826368Z 2025-07-17T07:11:36.1826551Z 3) create_global_plan - called on the coordinator rank only. 2025-07-17T07:11:36.1826746Z Takes the LoadPlan from all ranks and make any global decision. 2025-07-17T07:11:36.1826832Z 2025-07-17T07:11:36.1826972Z 4) load_bytes - called multiple times on each rank 2025-07-17T07:11:36.1827138Z This is called once per non-tensor value in state_dict. 2025-07-17T07:11:36.1827221Z 2025-07-17T07:11:36.1827444Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-07-17T07:11:36.1827620Z They are called in pair for each Tensor value in state_dict. 2025-07-17T07:11:36.1827705Z 2025-07-17T07:11:36.1828004Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-07-17T07:11:36.1828182Z most changes can be expressed by changes in a single method. 2025-07-17T07:11:36.1828266Z 2025-07-17T07:11:36.1828393Z There are two usual patterns of extension: 2025-07-17T07:11:36.1828475Z 2025-07-17T07:11:36.1828723Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-07-17T07:11:36.1828978Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-07-17T07:11:36.1829238Z to keep a reference to the original state_dict as load happens in place so 2025-07-17T07:11:36.1829361Z we need to be able to perform it in place 2025-07-17T07:11:36.1829438Z 2025-07-17T07:11:36.1829556Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.1829687Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-07-17T07:11:36.1829778Z >>> def set_up_planner( 2025-07-17T07:11:36.1829863Z >>> self, 2025-07-17T07:11:36.1829970Z >>> state_dict: STATE_DICT_TYPE, 2025-07-17T07:11:36.1830072Z >>> metadata: Metadata, 2025-07-17T07:11:36.1830173Z >>> is_coordinator: bool, 2025-07-17T07:11:36.1830258Z >>> ) -> None: 2025-07-17T07:11:36.1830389Z >>> self.original_state_dict = state_dict 2025-07-17T07:11:36.1830560Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-07-17T07:11:36.1830662Z >>> 2025-07-17T07:11:36.1830787Z >>> if self.flatten_sharded_tensors: 2025-07-17T07:11:36.1830940Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-07-17T07:11:36.1831016Z >>> 2025-07-17T07:11:36.1831173Z >>> if self.flatten_state_dict: 2025-07-17T07:11:36.1831353Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-07-17T07:11:36.1831427Z >>> 2025-07-17T07:11:36.1831545Z >>> self.state_dict = state_dict 2025-07-17T07:11:36.1831644Z >>> self.metadata = metadata 2025-07-17T07:11:36.1831771Z >>> self.is_coordinator = is_coordinator 2025-07-17T07:11:36.1831846Z >>> 2025-07-17T07:11:36.1831964Z >>> def load_bytes(self, read_item, value): 2025-07-17T07:11:36.1832075Z >>> # Remove the "foo_" prefix 2025-07-17T07:11:36.1832390Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-07-17T07:11:36.1832468Z 2025-07-17T07:11:36.1832552Z 2025-07-17T07:11:36.1832806Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-07-17T07:11:36.1832881Z 2025-07-17T07:11:36.1832996Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.1833150Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-07-17T07:11:36.1833271Z >>> def resolve_tensor(self, read_item): 2025-07-17T07:11:36.1833397Z >>> tensor = super().resolve_tensor(read_item) 2025-07-17T07:11:36.1833536Z >>> return torch.empty_like(tensor, device="cpu") 2025-07-17T07:11:36.1833623Z >>> 2025-07-17T07:11:36.1833748Z >>> def commit_tensor(self, read_item, tensor): 2025-07-17T07:11:36.1833898Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-07-17T07:11:36.1833982Z 2025-07-17T07:11:36.1834228Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1834303Z 2025-07-17T07:11:36.1834406Z warnings.warn(msg) 2025-07-17T07:11:36.1834485Z 2025-07-17T07:11:36.1834678Z --- Parse Warning: 66 / 136 --- 2025-07-17T07:11:36.1835666Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-07-17T07:11:36.1835920Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1836003Z 2025-07-17T07:11:36.1836161Z Return the model state_dict and optimizers state_dict. 2025-07-17T07:11:36.1836236Z 2025-07-17T07:11:36.1836460Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-07-17T07:11:36.1836709Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-07-17T07:11:36.1836961Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-07-17T07:11:36.1837173Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-07-17T07:11:36.1837406Z with a different number of trainers and/or different parallelisms. 2025-07-17T07:11:36.1837667Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-07-17T07:11:36.1837750Z these APIs. 2025-07-17T07:11:36.1837868Z 3.) sanity checking the result state_dict. 2025-07-17T07:11:36.1837951Z 2025-07-17T07:11:36.1838155Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-07-17T07:11:36.1838396Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-07-17T07:11:36.1838626Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-07-17T07:11:36.1838828Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-07-17T07:11:36.1839036Z ``module.named_buffers()`` when the module is not distributed by any 2025-07-17T07:11:36.1839311Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-07-17T07:11:36.1839520Z a parameter, there will be a conversion from the parameter IDs to the 2025-07-17T07:11:36.1839637Z canonical FQNs when calling this API. 2025-07-17T07:11:36.1839767Z 2025-07-17T07:11:36.1839979Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-07-17T07:11:36.1840205Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-07-17T07:11:36.1840339Z optimizer parameter IDs to the canonical FQNs. 2025-07-17T07:11:36.1840423Z 2025-07-17T07:11:36.1840502Z Example: 2025-07-17T07:11:36.1840592Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1840689Z >>> import torch 2025-07-17T07:11:36.1840918Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T07:11:36.1841111Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-07-17T07:11:36.1841344Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-07-17T07:11:36.1841419Z 2025-07-17T07:11:36.1841544Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-07-17T07:11:36.1841738Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-07-17T07:11:36.1841855Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-07-17T07:11:36.1842038Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-07-17T07:11:36.1842114Z 2025-07-17T07:11:36.1842185Z 2025-07-17T07:11:36.1842422Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-07-17T07:11:36.1842585Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-07-17T07:11:36.1842685Z ... fsdp_model, fsdp_optim 2025-07-17T07:11:36.1842768Z ... ) 2025-07-17T07:11:36.1842839Z 2025-07-17T07:11:36.1843046Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-07-17T07:11:36.1843157Z >>> # the asserts will fail. 2025-07-17T07:11:36.1843281Z >>> assert ddp_state_dict == fsdp_state_dict 2025-07-17T07:11:36.1843432Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-07-17T07:11:36.1843506Z 2025-07-17T07:11:36.1843581Z 2025-07-17T07:11:36.1843664Z Args: 2025-07-17T07:11:36.1843796Z model (nn.Module): the nn.Module to the model. 2025-07-17T07:11:36.1843975Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-07-17T07:11:36.1844136Z The optimizers that are used to optimize ``model``. 2025-07-17T07:11:36.1844406Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-07-17T07:11:36.1844510Z that belong to the submodules. 2025-07-17T07:11:36.1844683Z options (StateDictOptions): the options to control how 2025-07-17T07:11:36.1844881Z model state_dict and optimizer state_dict should be returned. See 2025-07-17T07:11:36.1845003Z `StateDictOptions` for the details. 2025-07-17T07:11:36.1845102Z 2025-07-17T07:11:36.1845178Z Returns: 2025-07-17T07:11:36.1845374Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-07-17T07:11:36.1845448Z 2025-07-17T07:11:36.1845673Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-07-17T07:11:36.1845756Z 2025-07-17T07:11:36.1846001Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1846074Z 2025-07-17T07:11:36.1846172Z warnings.warn(msg) 2025-07-17T07:11:36.1846247Z 2025-07-17T07:11:36.1846436Z --- Parse Warning: 67 / 136 --- 2025-07-17T07:11:36.1847405Z /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. 2025-07-17T07:11:36.1847660Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1847776Z 2025-07-17T07:11:36.1847965Z Load a checkpoint into a distributed state dict in SPMD style. 2025-07-17T07:11:36.1848040Z 2025-07-17T07:11:36.1848306Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-07-17T07:11:36.1848531Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-07-17T07:11:36.1848748Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-07-17T07:11:36.1848824Z costs). 2025-07-17T07:11:36.1848898Z 2025-07-17T07:11:36.1849081Z Each rank will try to read the least amount of data necessary 2025-07-17T07:11:36.1849309Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-07-17T07:11:36.1849548Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-07-17T07:11:36.1849630Z 2025-07-17T07:11:36.1849884Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-07-17T07:11:36.1850149Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-07-17T07:11:36.1850313Z ``load_state_dict`` once the deserialization is complete. 2025-07-17T07:11:36.1850569Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-07-17T07:11:36.1850724Z it in the ``state_dict`` with the deserialized object. 2025-07-17T07:11:36.1850798Z 2025-07-17T07:11:36.1850883Z .. warning:: 2025-07-17T07:11:36.1851056Z All tensors in ``state_dict`` must be allocated on their 2025-07-17T07:11:36.1851216Z destination device *prior to* calling this function. 2025-07-17T07:11:36.1851289Z 2025-07-17T07:11:36.1851527Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-07-17T07:11:36.1851610Z on state_dict. 2025-07-17T07:11:36.1851695Z 2025-07-17T07:11:36.1851779Z .. warning:: 2025-07-17T07:11:36.1851978Z Users must call `load_state_dict` on the root module to ensure load 2025-07-17T07:11:36.1852164Z pos-processing and non-tensor data properly propagates. 2025-07-17T07:11:36.1852238Z 2025-07-17T07:11:36.1852311Z .. note: 2025-07-17T07:11:36.1852543Z If no process group is initialized, this function will assume the intent 2025-07-17T07:11:36.1852761Z is to load a checkpoint into the local process. This can be useful in the 2025-07-17T07:11:36.1853001Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-07-17T07:11:36.1853104Z or ShardedTensor) 2025-07-17T07:11:36.1853180Z 2025-07-17T07:11:36.1853262Z .. note: 2025-07-17T07:11:36.1853399Z Rank 0 is assumed to be the coordinator rank. 2025-07-17T07:11:36.1853472Z 2025-07-17T07:11:36.1853557Z Args: 2025-07-17T07:11:36.1853761Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-07-17T07:11:36.1853899Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T07:11:36.1854139Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T07:11:36.1854340Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T07:11:36.1854499Z It can also be a key if the storage is a key-value store. 2025-07-17T07:11:36.1854603Z (Default: ``None``) 2025-07-17T07:11:36.1854727Z storage_reader (Optional[StorageReader]): 2025-07-17T07:11:36.1854931Z Instance of StorageWriter used to perform reads. If this is not 2025-07-17T07:11:36.1855128Z specified, DCP will automatically infer the reader based on the 2025-07-17T07:11:36.1855411Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T07:11:36.1855528Z be raised. (Default: ``None``) 2025-07-17T07:11:36.1855639Z planner (Optional[LoadPlanner]): 2025-07-17T07:11:36.1855861Z Instance of LoadPlanner. If this is not specified, the default 2025-07-17T07:11:36.1855998Z planner will be used. (Default: ``None``) 2025-07-17T07:11:36.1856124Z process_group (Optional[ProcessGroup]): 2025-07-17T07:11:36.1856357Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T07:11:36.1856454Z (Default: ``None``) 2025-07-17T07:11:36.1856665Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-07-17T07:11:36.1856922Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-07-17T07:11:36.1857004Z Returns: 2025-07-17T07:11:36.1857085Z None. 2025-07-17T07:11:36.1857169Z 2025-07-17T07:11:36.1857249Z Examples 2025-07-17T07:11:36.1857342Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1857440Z >>> my_model = MyModule() 2025-07-17T07:11:36.1857571Z >>> optimizer = Adagrad(my_model.parameters()) 2025-07-17T07:11:36.1857691Z >>> model_state_dict = my_model.state_dict() 2025-07-17T07:11:36.1857932Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-07-17T07:11:36.1858023Z ... "/checkpoint/1" 2025-07-17T07:11:36.1858102Z ... ) 2025-07-17T07:11:36.1858179Z 2025-07-17T07:11:36.1858328Z >>> torch.distributed.checkpoint.load_state_dict( 2025-07-17T07:11:36.1858441Z >>> state_dict=model_state_dict, 2025-07-17T07:11:36.1858554Z >>> storage_reader=fs_storage_reader, 2025-07-17T07:11:36.1858629Z >>> ) 2025-07-17T07:11:36.1858712Z 2025-07-17T07:11:36.1858905Z >>> # module.load_state_dict() function might have customized steps 2025-07-17T07:11:36.1859025Z >>> # to flush the state_dict, must call it to 2025-07-17T07:11:36.1859132Z >>> # ensure correct behavior. 2025-07-17T07:11:36.1859254Z >>> my_model.load_state_dict(model_state_dict) 2025-07-17T07:11:36.1859334Z 2025-07-17T07:11:36.1859415Z .. note:: 2025-07-17T07:11:36.1859620Z load_state_dict uses collectives to coordinate reads across ranks. 2025-07-17T07:11:36.1859836Z For NCCL-based process groups, internal tensor representations of 2025-07-17T07:11:36.1860065Z objects must be moved to the GPU device before communication takes place. 2025-07-17T07:11:36.1860278Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-07-17T07:11:36.1860507Z and it is the user's responsibility to ensure that this is set so that each 2025-07-17T07:11:36.1860686Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-07-17T07:11:36.1860773Z 2025-07-17T07:11:36.1861022Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1861098Z 2025-07-17T07:11:36.1861199Z warnings.warn(msg) 2025-07-17T07:11:36.1861276Z 2025-07-17T07:11:36.1861469Z --- Parse Warning: 68 / 136 --- 2025-07-17T07:11:36.1862451Z /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=96. 2025-07-17T07:11:36.1862745Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1862819Z 2025-07-17T07:11:36.1862947Z Save a distributed model in SPMD style. 2025-07-17T07:11:36.1863026Z 2025-07-17T07:11:36.1863220Z This function is different from ``torch.save()`` as it handles 2025-07-17T07:11:36.1863478Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-07-17T07:11:36.1863555Z 2025-07-17T07:11:36.1863816Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-07-17T07:11:36.1863963Z save will call ``state_dict`` before serialization. 2025-07-17T07:11:36.1864038Z 2025-07-17T07:11:36.1864131Z .. warning:: 2025-07-17T07:11:36.1864390Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-07-17T07:11:36.1864487Z for saved state_dicts. 2025-07-17T07:11:36.1864569Z 2025-07-17T07:11:36.1864652Z .. warning:: 2025-07-17T07:11:36.1864913Z If using the `process_group` argument, make sure that only its ranks 2025-07-17T07:11:36.1865117Z call `save_state_dict` and that all data in state_dict belong to it. 2025-07-17T07:11:36.1865190Z 2025-07-17T07:11:36.1865274Z .. note:: 2025-07-17T07:11:36.1865525Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-07-17T07:11:36.1865781Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-07-17T07:11:36.1865889Z group needs to be passed in. 2025-07-17T07:11:36.1865964Z 2025-07-17T07:11:36.1866038Z .. note:: 2025-07-17T07:11:36.1866307Z If no process group is available, this function assumes the intention is to save the 2025-07-17T07:11:36.1866418Z state_dict in the local process. 2025-07-17T07:11:36.1866500Z 2025-07-17T07:11:36.1866578Z .. note: 2025-07-17T07:11:36.1866709Z Rank 0 is assumed to be the coordinator rank. 2025-07-17T07:11:36.1866792Z 2025-07-17T07:11:36.1866872Z 2025-07-17T07:11:36.1866948Z Args: 2025-07-17T07:11:36.1867104Z state_dict (Dict[str, Any]): The state_dict to save. 2025-07-17T07:11:36.1867245Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T07:11:36.1867451Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T07:11:36.1867656Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T07:11:36.1867816Z It can also be a key if the storage is a key-value store. 2025-07-17T07:11:36.1867921Z (Default: ``None``) 2025-07-17T07:11:36.1868047Z storage_writer (Optional[StorageWriter]): 2025-07-17T07:11:36.1868246Z Instance of StorageWriter used to perform writes. If this is not 2025-07-17T07:11:36.1868461Z specified, DCP will automatically infer the writer based on the 2025-07-17T07:11:36.1868661Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T07:11:36.1868767Z be raised. (Default: ``None``) 2025-07-17T07:11:36.1868886Z planner (Optional[SavePlanner]): 2025-07-17T07:11:36.1869081Z Instance of SavePlanner. If this is not specified, the default 2025-07-17T07:11:36.1869201Z planner will be used. (Default: ``None``) 2025-07-17T07:11:36.1869336Z process_group (Optional[ProcessGroup]): 2025-07-17T07:11:36.1869512Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T07:11:36.1869614Z (Default: ``None``) 2025-07-17T07:11:36.1869702Z no_dist (bool): 2025-07-17T07:11:36.1869864Z If ``True``, this function will assume the intent is to load 2025-07-17T07:11:36.1870042Z a checkpoint without using cross-rank synchronization. 2025-07-17T07:11:36.1870164Z (Default: ``False``) 2025-07-17T07:11:36.1870239Z 2025-07-17T07:11:36.1870329Z Returns: 2025-07-17T07:11:36.1870484Z Metadata: Metadata object for the saved checkpoint. 2025-07-17T07:11:36.1870561Z 2025-07-17T07:11:36.1870651Z Example: 2025-07-17T07:11:36.1870745Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1870848Z >>> my_model = MyModule() 2025-07-17T07:11:36.1870923Z 2025-07-17T07:11:36.1871027Z >>> state_dict = {"model": my_model} 2025-07-17T07:11:36.1871109Z 2025-07-17T07:11:36.1871339Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-07-17T07:11:36.1871431Z ... "/checkpoint/1" 2025-07-17T07:11:36.1871516Z ... ) 2025-07-17T07:11:36.1871643Z >>> torch.distributed.checkpoint.save( 2025-07-17T07:11:36.1871739Z >>> state_dict=state_dict, 2025-07-17T07:11:36.1872046Z >>> storage_writer=fs_storage_writer, 2025-07-17T07:11:36.1872195Z >>> ) 2025-07-17T07:11:36.1872269Z 2025-07-17T07:11:36.1872360Z .. note:: 2025-07-17T07:11:36.1872570Z save_state_dict uses collectives to coordinate writes across ranks. 2025-07-17T07:11:36.1872851Z For NCCL-based process groups, internal tensor representations of 2025-07-17T07:11:36.1873082Z objects must be moved to the GPU device before communication takes place. 2025-07-17T07:11:36.1873295Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-07-17T07:11:36.1873504Z and it is the user's responsibility to ensure that this is set so that 2025-07-17T07:11:36.1873699Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-07-17T07:11:36.1873773Z 2025-07-17T07:11:36.1874025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1874100Z 2025-07-17T07:11:36.1874200Z warnings.warn(msg) 2025-07-17T07:11:36.1874279Z 2025-07-17T07:11:36.1874479Z --- Parse Warning: 69 / 136 --- 2025-07-17T07:11:36.1875503Z /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=221. 2025-07-17T07:11:36.1875764Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1876025Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-07-17T07:11:36.1876320Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-07-17T07:11:36.1876400Z 2025-07-17T07:11:36.1876493Z .. warning:: 2025-07-17T07:11:36.1876652Z This feature is experimental and subject to change. 2025-07-17T07:11:36.1876793Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-07-17T07:11:36.1876876Z 2025-07-17T07:11:36.1876957Z Args: 2025-07-17T07:11:36.1877105Z state_dict (Dict[str, Any]): The state_dict to save. 2025-07-17T07:11:36.1877253Z checkpoint_id (Union[str, os.PathLike, None]): 2025-07-17T07:11:36.1877464Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-07-17T07:11:36.1877659Z depends on the storage. It can be a path to a folder or to a file. 2025-07-17T07:11:36.1877830Z It can also be a key if the storage is a key-value store. 2025-07-17T07:11:36.1877923Z (Default: ``None``) 2025-07-17T07:11:36.1878059Z storage_writer (Optional[StorageWriter]): 2025-07-17T07:11:36.1878264Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-07-17T07:11:36.1878498Z this is not specified, DCP will automatically infer the writer based on the 2025-07-17T07:11:36.1878706Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-07-17T07:11:36.1878855Z be raised. (Default: ``None``) 2025-07-17T07:11:36.1878968Z planner (Optional[SavePlanner]): 2025-07-17T07:11:36.1879175Z Instance of SavePlanner. If this is not specified, the default 2025-07-17T07:11:36.1879301Z planner will be used. (Default: ``None``) 2025-07-17T07:11:36.1879435Z process_group (Optional[ProcessGroup]): 2025-07-17T07:11:36.1879611Z ProcessGroup to be used for cross-rank synchronization. 2025-07-17T07:11:36.1879704Z (Default: ``None``) 2025-07-17T07:11:36.1879866Z async_checkpointer_type (AsyncCheckpointerType): 2025-07-17T07:11:36.1880033Z whether to do checkpoint in separate thread or process 2025-07-17T07:11:36.1880172Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-07-17T07:11:36.1880279Z async_stager (AsyncStager): 2025-07-17T07:11:36.1880563Z provides staging implementation. If storage_writer implements AsyncStager 2025-07-17T07:11:36.1880779Z and async_stager is provided, async_stager will be used for staging 2025-07-17T07:11:36.1880857Z 2025-07-17T07:11:36.1880938Z Returns: 2025-07-17T07:11:36.1881201Z Future: A future holding the resultant Metadata object from `save`. 2025-07-17T07:11:36.1881276Z 2025-07-17T07:11:36.1881357Z Example: 2025-07-17T07:11:36.1881457Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.1881554Z >>> my_model = MyModule() 2025-07-17T07:11:36.1881629Z 2025-07-17T07:11:36.1881746Z >>> state_dict = {"model": my_model} 2025-07-17T07:11:36.1881819Z 2025-07-17T07:11:36.1882049Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-07-17T07:11:36.1882151Z ... "/checkpoint/1" 2025-07-17T07:11:36.1882230Z ... ) 2025-07-17T07:11:36.1882442Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-07-17T07:11:36.1882550Z >>> state_dict=state_dict, 2025-07-17T07:11:36.1882675Z >>> storage_writer=fs_storage_writer, 2025-07-17T07:11:36.1882760Z >>> ) 2025-07-17T07:11:36.1882842Z >>> 2025-07-17T07:11:36.1882936Z >>> # ... do some work ... 2025-07-17T07:11:36.1883021Z >>> 2025-07-17T07:11:36.1883128Z >>> checkpoint_future.result() 2025-07-17T07:11:36.1883200Z 2025-07-17T07:11:36.1883286Z 2025-07-17T07:11:36.1883535Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1883610Z 2025-07-17T07:11:36.1883709Z warnings.warn(msg) 2025-07-17T07:11:36.1883784Z 2025-07-17T07:11:36.1883973Z --- Parse Warning: 70 / 136 --- 2025-07-17T07:11:36.1885039Z /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. 2025-07-17T07:11:36.1885301Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1885383Z 2025-07-17T07:11:36.1885582Z Initialize rendezvous event object and record its operations. 2025-07-17T07:11:36.1885657Z 2025-07-17T07:11:36.1885743Z Args: 2025-07-17T07:11:36.1885867Z run_id (str): The run id of the rendezvous. 2025-07-17T07:11:36.1886017Z message (str): The message describing the event. 2025-07-17T07:11:36.1886265Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-07-17T07:11:36.1886443Z name (str): Event name. (E.g. Current action being performed). 2025-07-17T07:11:36.1886561Z hostname (str): Hostname of the node. 2025-07-17T07:11:36.1886699Z pid (Optional[int]): The process id of the node. 2025-07-17T07:11:36.1886941Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-07-17T07:11:36.1887236Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-07-17T07:11:36.1887395Z rank (Optional[int]): The rank of the node, if known. 2025-07-17T07:11:36.1887478Z Returns: 2025-07-17T07:11:36.1887556Z None 2025-07-17T07:11:36.1887636Z Example: 2025-07-17T07:11:36.1887762Z >>> # See DynamicRendezvousHandler class 2025-07-17T07:11:36.1887846Z >>> def _record( 2025-07-17T07:11:36.1887925Z ... self, 2025-07-17T07:11:36.1888019Z ... message: str, 2025-07-17T07:11:36.1888154Z ... node_state: NodeState = NodeState.RUNNING, 2025-07-17T07:11:36.1888258Z ... rank: Optional[int] = None, 2025-07-17T07:11:36.1888343Z ... ) -> None: 2025-07-17T07:11:36.1888456Z ... construct_and_record_rdzv_event( 2025-07-17T07:11:36.1888611Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-07-17T07:11:36.1888758Z ... run_id=self._settings.run_id, 2025-07-17T07:11:36.1888849Z ... message=message, 2025-07-17T07:11:36.1888950Z ... node_state=node_state, 2025-07-17T07:11:36.1889110Z ... hostname=self._this_node.addr, 2025-07-17T07:11:36.1889214Z ... pid=self._this_node.pid, 2025-07-17T07:11:36.1889342Z ... local_id=self._this_node.local_id, 2025-07-17T07:11:36.1889431Z ... rank=rank, 2025-07-17T07:11:36.1889507Z ... ) 2025-07-17T07:11:36.1889590Z 2025-07-17T07:11:36.1889843Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1889916Z 2025-07-17T07:11:36.1890018Z warnings.warn(msg) 2025-07-17T07:11:36.1890096Z 2025-07-17T07:11:36.1890286Z --- Parse Warning: 71 / 136 --- 2025-07-17T07:11:36.1891222Z /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. 2025-07-17T07:11:36.1891490Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1891578Z 2025-07-17T07:11:36.1891744Z This configures FSDP-native mixed precision training. 2025-07-17T07:11:36.1891820Z 2025-07-17T07:11:36.1891914Z Attributes: 2025-07-17T07:11:36.1892147Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-07-17T07:11:36.1892350Z parameters during forward and backward and thus the dtype for 2025-07-17T07:11:36.1892570Z forward and backward computation. Outside forward and backward, the 2025-07-17T07:11:36.1892761Z *sharded* parameters are kept in full precision (e.g. for the 2025-07-17T07:11:36.1892976Z optimizer step), and for model checkpointing, the parameters are 2025-07-17T07:11:36.1893132Z always saved in full precision. (Default: ``None``) 2025-07-17T07:11:36.1893343Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-07-17T07:11:36.1893565Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-07-17T07:11:36.1893738Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-07-17T07:11:36.1893940Z the ``param_dtype`` value, still running gradient reduction in low 2025-07-17T07:11:36.1894148Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-07-17T07:11:36.1894343Z to force gradient reduction to run in full precision. (Default: 2025-07-17T07:11:36.1894430Z ``None``) 2025-07-17T07:11:36.1894640Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-07-17T07:11:36.1894837Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-07-17T07:11:36.1895042Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-07-17T07:11:36.1895372Z dtype thereafter. For model checkpointing, the buffers are saved 2025-07-17T07:11:36.1895563Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-07-17T07:11:36.1895651Z ``None``) 2025-07-17T07:11:36.1895845Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-07-17T07:11:36.1896065Z gradients to full precision after the backward pass in preparation 2025-07-17T07:11:36.1896261Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-07-17T07:11:36.1896456Z in the dtype used for gradient reduction, which can save memory if 2025-07-17T07:11:36.1896665Z using a custom optimizer that supports running in low precision. 2025-07-17T07:11:36.1896761Z (Default: ``False``) 2025-07-17T07:11:36.1896972Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-07-17T07:11:36.1897202Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-07-17T07:11:36.1897409Z that parameter and input dtypes match for forward computation, as 2025-07-17T07:11:36.1897660Z required by many ops. This may need to be set to ``True`` when only 2025-07-17T07:11:36.1897878Z applying mixed precision to some but not all FSDP modules, in which 2025-07-17T07:11:36.1898080Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-07-17T07:11:36.1898180Z (Default: ``False``) 2025-07-17T07:11:36.1898394Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-07-17T07:11:36.1898596Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-07-17T07:11:36.1898775Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-07-17T07:11:36.1898910Z this does not do anything. (Default: ``True``) 2025-07-17T07:11:36.1899131Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-07-17T07:11:36.1899310Z module classes to ignore for mixed precision when using an 2025-07-17T07:11:36.1899493Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-07-17T07:11:36.1899702Z applied to them separately with mixed precision disabled (meaning 2025-07-17T07:11:36.1899895Z that the final FSDP construction would deviate from the specified 2025-07-17T07:11:36.1900086Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-07-17T07:11:36.1900278Z not do anything. This API is experimental and subject to change. 2025-07-17T07:11:36.1900383Z (Default: ``(_BatchNorm,)``) 2025-07-17T07:11:36.1900466Z 2025-07-17T07:11:36.1900634Z .. note:: This API is experimental and subject to change. 2025-07-17T07:11:36.1900707Z 2025-07-17T07:11:36.1900930Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-07-17T07:11:36.1901011Z 2025-07-17T07:11:36.1901196Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-07-17T07:11:36.1901301Z precision, but buffers are not. 2025-07-17T07:11:36.1901378Z 2025-07-17T07:11:36.1901591Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-07-17T07:11:36.1901795Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-07-17T07:11:36.1902021Z Disabling FSDP's mixed precision for those norm modules only means that 2025-07-17T07:11:36.1902238Z the affine parameters are kept in ``float32``. However, this incurs 2025-07-17T07:11:36.1902467Z separate all-gathers and reduce-scatters for those norm modules, which 2025-07-17T07:11:36.1902685Z may be inefficient, so if the workload permits, the user should prefer 2025-07-17T07:11:36.1902826Z to still apply mixed precision to those modules. 2025-07-17T07:11:36.1902903Z 2025-07-17T07:11:36.1903155Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-07-17T07:11:36.1903354Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-07-17T07:11:36.1903578Z modules will have FSDP applied to them separately with mixed precision 2025-07-17T07:11:36.1903757Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-07-17T07:11:36.1903834Z 2025-07-17T07:11:36.1904042Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-07-17T07:11:36.1904248Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-07-17T07:11:36.1904416Z its ``cast_root_forward_inputs`` takes precedence over its 2025-07-17T07:11:36.1904595Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-07-17T07:11:36.1904803Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-07-17T07:11:36.1905018Z sufficient for the typical case where each FSDP instance has the same 2025-07-17T07:11:36.1905274Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-07-17T07:11:36.1905449Z ``param_dtype`` at the beginning of the model's forward pass. 2025-07-17T07:11:36.1905579Z 2025-07-17T07:11:36.1905781Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-07-17T07:11:36.1906015Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-07-17T07:11:36.1906223Z values to configure casting inputs or not before each instance's 2025-07-17T07:11:36.1906409Z forward. In such a case, since the casts happen before each FSDP 2025-07-17T07:11:36.1906620Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-07-17T07:11:36.1906856Z submodules run before its FSDP submodules to avoid the activation dtype 2025-07-17T07:11:36.1907057Z being changed due to a different ``MixedPrecision`` configuration. 2025-07-17T07:11:36.1907148Z 2025-07-17T07:11:36.1907229Z Example:: 2025-07-17T07:11:36.1907304Z 2025-07-17T07:11:36.1907443Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1907611Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-07-17T07:11:36.1907704Z >>> model[1] = FSDP( 2025-07-17T07:11:36.1907804Z >>> model[1], 2025-07-17T07:11:36.1908107Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-07-17T07:11:36.1908186Z >>> ) 2025-07-17T07:11:36.1908289Z >>> model = FSDP( 2025-07-17T07:11:36.1908372Z >>> model, 2025-07-17T07:11:36.1908686Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-07-17T07:11:36.1908761Z >>> ) 2025-07-17T07:11:36.1908836Z 2025-07-17T07:11:36.1909051Z The above shows a working example. On the other hand, if ``model[1]`` 2025-07-17T07:11:36.1909247Z were replaced with ``model[0]``, meaning that the submodule using 2025-07-17T07:11:36.1909466Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-07-17T07:11:36.1909691Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-07-17T07:11:36.1909767Z ones. 2025-07-17T07:11:36.1909844Z 2025-07-17T07:11:36.1909927Z 2025-07-17T07:11:36.1910170Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1910255Z 2025-07-17T07:11:36.1910342Z warnings.warn(msg) 2025-07-17T07:11:36.1910414Z 2025-07-17T07:11:36.1910628Z --- Parse Warning: 72 / 136 --- 2025-07-17T07:11:36.1911582Z /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. 2025-07-17T07:11:36.1911877Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1911962Z 2025-07-17T07:11:36.1912165Z ``FullStateDictConfig`` is a config class meant to be used with 2025-07-17T07:11:36.1912373Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-07-17T07:11:36.1912568Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-07-17T07:11:36.1912782Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-07-17T07:11:36.1912988Z is meant to be used via the :func:`state_dict_type` context manager as 2025-07-17T07:11:36.1913067Z follows: 2025-07-17T07:11:36.1913143Z 2025-07-17T07:11:36.1913281Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1913511Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T07:11:36.1913634Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-07-17T07:11:36.1913869Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-07-17T07:11:36.1914085Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-07-17T07:11:36.1914207Z >>> state = fsdp.state_dict() 2025-07-17T07:11:36.1914490Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-07-17T07:11:36.1914723Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-07-17T07:11:36.1914966Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-07-17T07:11:36.1915062Z >>> if dist.get_rank() == 0: 2025-07-17T07:11:36.1915238Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-07-17T07:11:36.1915386Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-07-17T07:11:36.1915500Z >>> model.load_state_dict(state_dict) 2025-07-17T07:11:36.1915742Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-07-17T07:11:36.1915983Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-07-17T07:11:36.1916070Z >>> fsdp = FSDP( 2025-07-17T07:11:36.1916166Z ... model, 2025-07-17T07:11:36.1916304Z ... device_id=torch.cuda.current_device(), 2025-07-17T07:11:36.1916406Z ... auto_wrap_policy=..., 2025-07-17T07:11:36.1916522Z ... sync_module_states=True, 2025-07-17T07:11:36.1916598Z ... ) 2025-07-17T07:11:36.1916805Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-07-17T07:11:36.1916893Z 2025-07-17T07:11:36.1916981Z Attributes: 2025-07-17T07:11:36.1917185Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-07-17T07:11:36.1917378Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-07-17T07:11:36.1917528Z ranks save the full state dict. (Default: ``False``) 2025-07-17T07:11:36.1917615Z 2025-07-17T07:11:36.1917859Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1917931Z 2025-07-17T07:11:36.1918034Z warnings.warn(msg) 2025-07-17T07:11:36.1918107Z 2025-07-17T07:11:36.1918292Z --- Parse Warning: 73 / 136 --- 2025-07-17T07:11:36.1919505Z /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=637. 2025-07-17T07:11:36.1919763Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1920016Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-07-17T07:11:36.1920088Z 2025-07-17T07:11:36.1920346Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-07-17T07:11:36.1920589Z The target module does not have to be a FSDP module. If the target 2025-07-17T07:11:36.1920794Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-07-17T07:11:36.1920885Z 2025-07-17T07:11:36.1921081Z .. note:: This API should be called for only the top-level (root) 2025-07-17T07:11:36.1921162Z module. 2025-07-17T07:11:36.1921245Z 2025-07-17T07:11:36.1921449Z .. note:: This API enables users to transparently use the conventional 2025-07-17T07:11:36.1921632Z ``state_dict`` API to take model checkpoints in cases where the 2025-07-17T07:11:36.1921844Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-07-17T07:11:36.1922046Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-07-17T07:11:36.1922285Z instances, while dispatching into `sharded_state_dict` implementation 2025-07-17T07:11:36.1922402Z for FSDP: 2025-07-17T07:11:36.1922474Z 2025-07-17T07:11:36.1922566Z Example:: 2025-07-17T07:11:36.1922642Z 2025-07-17T07:11:36.1922773Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1922935Z >>> model = DDP(FSDP(...)) 2025-07-17T07:11:36.1923043Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:36.1923128Z >>> model, 2025-07-17T07:11:36.1923267Z >>> StateDictType.SHARDED_STATE_DICT, 2025-07-17T07:11:36.1923474Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-07-17T07:11:36.1923706Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-07-17T07:11:36.1923791Z >>> ) 2025-07-17T07:11:36.1923916Z >>> param_state_dict = model.state_dict() 2025-07-17T07:11:36.1924096Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-07-17T07:11:36.1924179Z 2025-07-17T07:11:36.1924260Z Args: 2025-07-17T07:11:36.1924399Z module (torch.nn.Module): Root module. 2025-07-17T07:11:36.1924631Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-07-17T07:11:36.1924863Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-07-17T07:11:36.1924987Z target ``state_dict_type``. 2025-07-17T07:11:36.1925234Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-07-17T07:11:36.1925359Z for the optimizer state dict. 2025-07-17T07:11:36.1925440Z 2025-07-17T07:11:36.1925519Z Returns: 2025-07-17T07:11:36.1925750Z A StateDictSettings that include the previous state_dict type and 2025-07-17T07:11:36.1925863Z configuration for the module. 2025-07-17T07:11:36.1925942Z 2025-07-17T07:11:36.1926202Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1926279Z 2025-07-17T07:11:36.1926368Z warnings.warn(msg) 2025-07-17T07:11:36.1926457Z 2025-07-17T07:11:36.1926639Z --- Parse Warning: 74 / 136 --- 2025-07-17T07:11:36.1927838Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=795. 2025-07-17T07:11:36.1928096Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1928335Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-07-17T07:11:36.1928420Z 2025-07-17T07:11:36.1928738Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-07-17T07:11:36.1928870Z :meth:`set_state_dict_type` for the detail. 2025-07-17T07:11:36.1928972Z 2025-07-17T07:11:36.1929055Z Example:: 2025-07-17T07:11:36.1929142Z 2025-07-17T07:11:36.1929276Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1929379Z >>> model = DDP(FSDP(...)) 2025-07-17T07:11:36.1929502Z >>> with FSDP.state_dict_type( 2025-07-17T07:11:36.1929587Z >>> model, 2025-07-17T07:11:36.1929712Z >>> StateDictType.SHARDED_STATE_DICT, 2025-07-17T07:11:36.1929807Z >>> ): 2025-07-17T07:11:36.1929925Z >>> checkpoint = model.state_dict() 2025-07-17T07:11:36.1929998Z 2025-07-17T07:11:36.1930089Z Args: 2025-07-17T07:11:36.1930209Z module (torch.nn.Module): Root module. 2025-07-17T07:11:36.1930446Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-07-17T07:11:36.1930699Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-07-17T07:11:36.1930855Z configuration for the target ``state_dict_type``. 2025-07-17T07:11:36.1931153Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-07-17T07:11:36.1931350Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-07-17T07:11:36.1931423Z 2025-07-17T07:11:36.1931681Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1931753Z 2025-07-17T07:11:36.1931848Z warnings.warn(msg) 2025-07-17T07:11:36.1931924Z 2025-07-17T07:11:36.1932100Z --- Parse Warning: 75 / 136 --- 2025-07-17T07:11:36.1933307Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1808. 2025-07-17T07:11:36.1933570Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1933642Z 2025-07-17T07:11:36.1933892Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-07-17T07:11:36.1933967Z 2025-07-17T07:11:36.1934165Z The given state-dict can be transformed to one of three types: 2025-07-17T07:11:36.1934460Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-07-17T07:11:36.1934533Z 2025-07-17T07:11:36.1934770Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-07-17T07:11:36.1934982Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-07-17T07:11:36.1935067Z avoid OOM. 2025-07-17T07:11:36.1935155Z 2025-07-17T07:11:36.1935481Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-07-17T07:11:36.1935683Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-07-17T07:11:36.1935775Z memory. 2025-07-17T07:11:36.1935846Z 2025-07-17T07:11:36.1936065Z For local state_dict, no transformation will be performed. But a state 2025-07-17T07:11:36.1936303Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-07-17T07:11:36.1936411Z nature (this is not supported yet). 2025-07-17T07:11:36.1936498Z 2025-07-17T07:11:36.1936583Z Example:: 2025-07-17T07:11:36.1936657Z 2025-07-17T07:11:36.1936792Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1937023Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T07:11:36.1937175Z >>> from torch.distributed.fsdp import StateDictType 2025-07-17T07:11:36.1937359Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-07-17T07:11:36.1937554Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-07-17T07:11:36.1937690Z >>> # Save a checkpoint 2025-07-17T07:11:36.1937784Z >>> model, optim = ... 2025-07-17T07:11:36.1937884Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:36.1937972Z >>> model, 2025-07-17T07:11:36.1938092Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T07:11:36.1938216Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T07:11:36.1938363Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T07:11:36.1938438Z >>> ) 2025-07-17T07:11:36.1938545Z >>> state_dict = model.state_dict() 2025-07-17T07:11:36.1938721Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-07-17T07:11:36.1938860Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-07-17T07:11:36.1938967Z >>> # Load a checkpoint 2025-07-17T07:11:36.1939063Z >>> model, optim = ... 2025-07-17T07:11:36.1939212Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-07-17T07:11:36.1939355Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:36.1939437Z >>> model, 2025-07-17T07:11:36.1939550Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T07:11:36.1939685Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T07:11:36.1939866Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T07:11:36.1939944Z >>> ) 2025-07-17T07:11:36.1940064Z >>> model.load_state_dict(state_dict) 2025-07-17T07:11:36.1940208Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-07-17T07:11:36.1940326Z >>> model, optim, optim_state_dict 2025-07-17T07:11:36.1940401Z >>> ) 2025-07-17T07:11:36.1940517Z >>> optim.load_state_dict(optim_state_dict) 2025-07-17T07:11:36.1940600Z 2025-07-17T07:11:36.1940676Z Args: 2025-07-17T07:11:36.1940863Z model (torch.nn.Module): Root module (which may or may not be a 2025-07-17T07:11:36.1941066Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-07-17T07:11:36.1941195Z were passed into the optimizer ``optim``. 2025-07-17T07:11:36.1941371Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-07-17T07:11:36.1941467Z parameters. 2025-07-17T07:11:36.1941675Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-07-17T07:11:36.1941887Z transform. If the value is None, optim.state_dict() will be used. ( 2025-07-17T07:11:36.1941976Z Default: ``None``) 2025-07-17T07:11:36.1942205Z group (dist.ProcessGroup): Model's process group across which parameters 2025-07-17T07:11:36.1942393Z are sharded or ``None`` if using the default process group. ( 2025-07-17T07:11:36.1942483Z Default: ``None``) 2025-07-17T07:11:36.1942560Z 2025-07-17T07:11:36.1942650Z Returns: 2025-07-17T07:11:36.1942832Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-07-17T07:11:36.1942994Z ``model``. The sharding of the optimizer state is based on 2025-07-17T07:11:36.1943104Z ``state_dict_type``. 2025-07-17T07:11:36.1943177Z 2025-07-17T07:11:36.1943434Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1943515Z 2025-07-17T07:11:36.1943609Z warnings.warn(msg) 2025-07-17T07:11:36.1943695Z 2025-07-17T07:11:36.1943884Z --- Parse Warning: 76 / 136 --- 2025-07-17T07:11:36.1945106Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1906. 2025-07-17T07:11:36.1945380Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1945456Z 2025-07-17T07:11:36.1945821Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-07-17T07:11:36.1945932Z 2025-07-17T07:11:36.1946095Z Given a ``optim_state_dict`` that is transformed through 2025-07-17T07:11:36.1946315Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-07-17T07:11:36.1946523Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-07-17T07:11:36.1946707Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-07-17T07:11:36.1946796Z 2025-07-17T07:11:36.1946920Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.1947164Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-07-17T07:11:36.1947317Z >>> from torch.distributed.fsdp import StateDictType 2025-07-17T07:11:36.1947489Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-07-17T07:11:36.1947694Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-07-17T07:11:36.1947784Z >>> # Save a checkpoint 2025-07-17T07:11:36.1947903Z >>> model, optim = ... 2025-07-17T07:11:36.1948013Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:36.1948091Z >>> model, 2025-07-17T07:11:36.1948203Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T07:11:36.1948383Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T07:11:36.1948522Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T07:11:36.1948609Z >>> ) 2025-07-17T07:11:36.1948715Z >>> state_dict = model.state_dict() 2025-07-17T07:11:36.1948824Z >>> original_osd = optim.state_dict() 2025-07-17T07:11:36.1948956Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-07-17T07:11:36.1949033Z >>> model, 2025-07-17T07:11:36.1949110Z >>> optim, 2025-07-17T07:11:36.1949226Z >>> optim_state_dict=original_osd 2025-07-17T07:11:36.1949301Z >>> ) 2025-07-17T07:11:36.1949440Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-07-17T07:11:36.1949541Z >>> # Load a checkpoint 2025-07-17T07:11:36.1949635Z >>> model, optim = ... 2025-07-17T07:11:36.1949796Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-07-17T07:11:36.1949900Z >>> FSDP.set_state_dict_type( 2025-07-17T07:11:36.1949982Z >>> model, 2025-07-17T07:11:36.1950106Z >>> StateDictType.FULL_STATE_DICT, 2025-07-17T07:11:36.1950228Z >>> FullStateDictConfig(rank0_only=False), 2025-07-17T07:11:36.1950366Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-07-17T07:11:36.1950452Z >>> ) 2025-07-17T07:11:36.1950560Z >>> model.load_state_dict(state_dict) 2025-07-17T07:11:36.1950706Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-07-17T07:11:36.1950824Z >>> model, optim, optim_state_dict 2025-07-17T07:11:36.1950901Z >>> ) 2025-07-17T07:11:36.1951021Z >>> optim.load_state_dict(optim_state_dict) 2025-07-17T07:11:36.1951112Z 2025-07-17T07:11:36.1951192Z Args: 2025-07-17T07:11:36.1951399Z model (torch.nn.Module): Root module (which may or may not be a 2025-07-17T07:11:36.1951597Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-07-17T07:11:36.1951731Z were passed into the optimizer ``optim``. 2025-07-17T07:11:36.1951919Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-07-17T07:11:36.1952009Z parameters. 2025-07-17T07:11:36.1952217Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-07-17T07:11:36.1952431Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-07-17T07:11:36.1952622Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-07-17T07:11:36.1952805Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-07-17T07:11:36.1952996Z load_directly (bool): If this is set to True, this API will also 2025-07-17T07:11:36.1953192Z call optim.load_state_dict(result) before returning the result. 2025-07-17T07:11:36.1953453Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-07-17T07:11:36.1953552Z (Default: ``False``) 2025-07-17T07:11:36.1953791Z group (dist.ProcessGroup): Model's process group across which parameters 2025-07-17T07:11:36.1953987Z are sharded or ``None`` if using the default process group. ( 2025-07-17T07:11:36.1954080Z Default: ``None``) 2025-07-17T07:11:36.1954167Z 2025-07-17T07:11:36.1954415Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1954491Z 2025-07-17T07:11:36.1954592Z warnings.warn(msg) 2025-07-17T07:11:36.1954669Z 2025-07-17T07:11:36.1954848Z --- Parse Warning: 77 / 136 --- 2025-07-17T07:11:36.1955871Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=129. 2025-07-17T07:11:36.1956164Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1956238Z 2025-07-17T07:11:36.1956521Z RemoteModule instance can only be created after RPC initialization. 2025-07-17T07:11:36.1956598Z 2025-07-17T07:11:36.1956799Z It creates a user-specified module on a specified remote node. 2025-07-17T07:11:36.1957027Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-07-17T07:11:36.1957127Z executed on the remote node. 2025-07-17T07:11:36.1957369Z It takes care of autograd recording to ensure the backward pass propagates 2025-07-17T07:11:36.1957525Z gradients back to the corresponding remote module. 2025-07-17T07:11:36.1957875Z It can be shared across processors using `RPC framework `__, 2025-07-17T07:11:36.1958082Z without incurring any overheads of copying the actual module, 2025-07-17T07:11:36.1958283Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-07-17T07:11:36.1958401Z pointing to the remote module. 2025-07-17T07:11:36.1958480Z 2025-07-17T07:11:36.1958677Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-07-17T07:11:36.1958883Z the ``forward`` method of the module returned by the ``module_cls``. 2025-07-17T07:11:36.1958963Z 2025-07-17T07:11:36.1959270Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-07-17T07:11:36.1959355Z 2025-07-17T07:11:36.1959601Z Particularly, to create a hybrid model, typically the local modules should be 2025-07-17T07:11:36.1959977Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-07-17T07:11:36.1960068Z Hybrid Example: 2025-07-17T07:11:36.1960182Z >>> class HybridModel(nn.Module): 2025-07-17T07:11:36.1960303Z >>> def __init__(self) -> None: 2025-07-17T07:11:36.1960412Z >>> nn.Module.__init__(self) 2025-07-17T07:11:36.1960550Z >>> self.remote_embedding = RemoteModule(...) 2025-07-17T07:11:36.1960683Z >>> self.local_linear = nn.Linear(...) 2025-07-17T07:11:36.1960761Z 2025-07-17T07:11:36.1960956Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-07-17T07:11:36.1961211Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-07-17T07:11:36.1961415Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-07-17T07:11:36.1961553Z ``def forward(input: Tensor) -> Tensor:`` and 2025-07-17T07:11:36.1961715Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-07-17T07:11:36.1961792Z 2025-07-17T07:11:36.1961887Z .. note:: 2025-07-17T07:11:36.1962028Z If the remote module is placed on a cuda device, 2025-07-17T07:11:36.1962290Z any input CPU tensors will be automatically moved to the same cuda device, 2025-07-17T07:11:36.1962700Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-07-17T07:11:36.1962782Z 2025-07-17T07:11:36.1962864Z Args: 2025-07-17T07:11:36.1963166Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T07:11:36.1963455Z The device can be a local device or a remote device specified by one of the following remote 2025-07-17T07:11:36.1963546Z formats: 2025-07-17T07:11:36.1963623Z 2025-07-17T07:11:36.1963761Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-07-17T07:11:36.1963920Z 2. "/" (ex: "trainer0/cuda:0"). 2025-07-17T07:11:36.1963998Z 2025-07-17T07:11:36.1964240Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T07:11:36.1964397Z module_cls (nn.Module): For example, 2025-07-17T07:11:36.1964505Z >>> class MyModule(nn.Module): 2025-07-17T07:11:36.1964618Z >>> def forward(input): 2025-07-17T07:11:36.1964777Z >>> return input + 1 2025-07-17T07:11:36.1964856Z >>> 2025-07-17T07:11:36.1964967Z >>> module_cls = MyModule 2025-07-17T07:11:36.1965166Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-07-17T07:11:36.1965353Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-07-17T07:11:36.1965647Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-07-17T07:11:36.1965879Z to be created. The type object should be decorated by @torch.jit.interface. 2025-07-17T07:11:36.1966093Z If not provided, the generated RemoteModule is not torchscript-able. 2025-07-17T07:11:36.1966334Z Warning, this is an experimental API and susceptible to frequent changes. 2025-07-17T07:11:36.1966418Z 2025-07-17T07:11:36.1966510Z Returns: 2025-07-17T07:11:36.1966751Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T07:11:36.1966980Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-07-17T07:11:36.1967261Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T07:11:36.1967402Z on the user-provided module on the remote side. 2025-07-17T07:11:36.1967478Z 2025-07-17T07:11:36.1967577Z Example:: 2025-07-17T07:11:36.1967724Z Run the following code in two different processes: 2025-07-17T07:11:36.1967811Z 2025-07-17T07:11:36.1967925Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:36.1968014Z >>> # On worker 0: 2025-07-17T07:11:36.1968115Z >>> import torch 2025-07-17T07:11:36.1968238Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:36.1968347Z >>> from torch import nn, Tensor 2025-07-17T07:11:36.1968574Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T07:11:36.1968653Z >>> 2025-07-17T07:11:36.1968795Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T07:11:36.1968929Z >>> remote_linear_module = RemoteModule( 2025-07-17T07:11:36.1969053Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T07:11:36.1969132Z >>> ) 2025-07-17T07:11:36.1969248Z >>> input = torch.randn(128, 20) 2025-07-17T07:11:36.1969400Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-07-17T07:11:36.1969511Z >>> ret = ret_fut.wait() 2025-07-17T07:11:36.1969609Z >>> rpc.shutdown() 2025-07-17T07:11:36.1969685Z 2025-07-17T07:11:36.1969786Z >>> # On worker 1: 2025-07-17T07:11:36.1969874Z >>> import torch 2025-07-17T07:11:36.1969997Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:36.1970091Z >>> 2025-07-17T07:11:36.1970255Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T07:11:36.1970348Z >>> rpc.shutdown() 2025-07-17T07:11:36.1970436Z 2025-07-17T07:11:36.1970687Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1970764Z 2025-07-17T07:11:36.1970864Z warnings.warn(msg) 2025-07-17T07:11:36.1970937Z 2025-07-17T07:11:36.1971139Z --- Parse Warning: 78 / 136 --- 2025-07-17T07:11:36.1972354Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=506. 2025-07-17T07:11:36.1972616Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1972707Z 2025-07-17T07:11:36.1973028Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-07-17T07:11:36.1973164Z 2025-07-17T07:11:36.1973503Z This alternate initialization method can be particularly useful if we want to create multiple 2025-07-17T07:11:36.1973881Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-07-17T07:11:36.1973976Z 2025-07-17T07:11:36.1974252Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-07-17T07:11:36.1974425Z which is not supported. The recommended way is as follows: 2025-07-17T07:11:36.1974514Z 2025-07-17T07:11:36.1974628Z 1. the sender creates a RemoteModule; 2025-07-17T07:11:36.1974763Z 2. the sender sends its ``module_rref`` over RPC; 2025-07-17T07:11:36.1975108Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-07-17T07:11:36.1975249Z 2025-07-17T07:11:36.1975353Z Example:: 2025-07-17T07:11:36.1975506Z Run the following code in two different processes: 2025-07-17T07:11:36.1975586Z 2025-07-17T07:11:36.1975712Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:36.1975800Z >>> # On worker 0: 2025-07-17T07:11:36.1975893Z >>> import torch 2025-07-17T07:11:36.1976030Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:36.1976134Z >>> from torch import nn, Tensor 2025-07-17T07:11:36.1976352Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T07:11:36.1976443Z >>> 2025-07-17T07:11:36.1976578Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T07:11:36.1976684Z >>> remote_module = RemoteModule( 2025-07-17T07:11:36.1976818Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T07:11:36.1976898Z >>> ) 2025-07-17T07:11:36.1976987Z >>> 2025-07-17T07:11:36.1977096Z >>> remote_module1 = rpc.rpc_sync( 2025-07-17T07:11:36.1977188Z >>> "worker1/cpu", 2025-07-17T07:11:36.1977325Z >>> RemoteModule.init_from_module_rref, 2025-07-17T07:11:36.1977476Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-07-17T07:11:36.1977551Z >>> ) 2025-07-17T07:11:36.1977657Z >>> rpc.shutdown() 2025-07-17T07:11:36.1977730Z 2025-07-17T07:11:36.1977818Z >>> # On worker 1: 2025-07-17T07:11:36.1977914Z >>> import torch 2025-07-17T07:11:36.1978034Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:36.1978112Z >>> 2025-07-17T07:11:36.1978255Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T07:11:36.1978347Z >>> rpc.shutdown() 2025-07-17T07:11:36.1978436Z 2025-07-17T07:11:36.1978517Z Args: 2025-07-17T07:11:36.1978812Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T07:11:36.1979107Z The device can be a local device or a remote device specified by one of the following remote 2025-07-17T07:11:36.1979234Z formats: 2025-07-17T07:11:36.1979310Z 2025-07-17T07:11:36.1979461Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-07-17T07:11:36.1979609Z 2. "/" (ex: "trainer0/cuda:0"). 2025-07-17T07:11:36.1979689Z 2025-07-17T07:11:36.1979943Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T07:11:36.1980192Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-07-17T07:11:36.1980308Z the created remote module. 2025-07-17T07:11:36.1980586Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-07-17T07:11:36.1980817Z to be created. The type object should be decorated by @torch.jit.interface. 2025-07-17T07:11:36.1981046Z If not provided, the generated RemoteModule is not torchscript-able. 2025-07-17T07:11:36.1981279Z Warning, this is an experimental API and susceptible to frequent changes. 2025-07-17T07:11:36.1981385Z 2025-07-17T07:11:36.1981479Z Returns: 2025-07-17T07:11:36.1981718Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T07:11:36.1982005Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-07-17T07:11:36.1982281Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T07:11:36.1982424Z on the user-provided module on the remote side. 2025-07-17T07:11:36.1982513Z 2025-07-17T07:11:36.1982765Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1982845Z 2025-07-17T07:11:36.1982950Z warnings.warn(msg) 2025-07-17T07:11:36.1983028Z 2025-07-17T07:11:36.1983231Z --- Parse Warning: 79 / 136 --- 2025-07-17T07:11:36.1984242Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=598. 2025-07-17T07:11:36.1984507Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1984603Z 2025-07-17T07:11:36.1984834Z A RemoteModule instance can only be created after RPC initialization. 2025-07-17T07:11:36.1984913Z 2025-07-17T07:11:36.1985120Z It creates a user-specified module on a specified remote node. 2025-07-17T07:11:36.1985351Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-07-17T07:11:36.1985471Z executed on the remote node. 2025-07-17T07:11:36.1985706Z It takes care of autograd recording to ensure the backward pass propagates 2025-07-17T07:11:36.1985864Z gradients back to the corresponding remote module. 2025-07-17T07:11:36.1985956Z 2025-07-17T07:11:36.1986173Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-07-17T07:11:36.1986392Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-07-17T07:11:36.1986654Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-07-17T07:11:36.1986855Z and ``forward`` are the same as the ``forward`` method of the module 2025-07-17T07:11:36.1986966Z returned by the ``module_cls``. 2025-07-17T07:11:36.1987060Z 2025-07-17T07:11:36.1987256Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-07-17T07:11:36.1987515Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-07-17T07:11:36.1987738Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-07-17T07:11:36.1987814Z 2025-07-17T07:11:36.1987949Z | ``def forward(input: Tensor) -> Tensor:`` 2025-07-17T07:11:36.1988111Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-07-17T07:11:36.1988191Z 2025-07-17T07:11:36.1988315Z Args: 2025-07-17T07:11:36.1988613Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-07-17T07:11:36.1988977Z The format should be "/", where the device field can be parsed as torch.device type. 2025-07-17T07:11:36.1989114Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-07-17T07:11:36.1989355Z In addition, the device field can be optional and the default value is "cpu". 2025-07-17T07:11:36.1989611Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-07-17T07:11:36.1989688Z 2025-07-17T07:11:36.1989798Z >>> class MyModule(nn.Module): 2025-07-17T07:11:36.1989912Z >>> def forward(input): 2025-07-17T07:11:36.1990011Z >>> return input + 1 2025-07-17T07:11:36.1990089Z >>> 2025-07-17T07:11:36.1990199Z >>> module_cls = MyModule 2025-07-17T07:11:36.1990306Z 2025-07-17T07:11:36.1990515Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-07-17T07:11:36.1990701Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-07-17T07:11:36.1990777Z 2025-07-17T07:11:36.1990921Z Returns: 2025-07-17T07:11:36.1991164Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-07-17T07:11:36.1991392Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-07-17T07:11:36.1991669Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-07-17T07:11:36.1991812Z on the user-provided module on the remote side. 2025-07-17T07:11:36.1991890Z 2025-07-17T07:11:36.1991987Z Example:: 2025-07-17T07:11:36.1992139Z Run the following code in two different processes: 2025-07-17T07:11:36.1992228Z 2025-07-17T07:11:36.1992339Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:36.1992432Z >>> # On worker 0: 2025-07-17T07:11:36.1992531Z >>> import torch 2025-07-17T07:11:36.1992653Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:36.1992761Z >>> from torch import nn, Tensor 2025-07-17T07:11:36.1992992Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-07-17T07:11:36.1993071Z >>> 2025-07-17T07:11:36.1993210Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T07:11:36.1993342Z >>> remote_linear_module = RemoteModule( 2025-07-17T07:11:36.1993464Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-07-17T07:11:36.1993555Z >>> ) 2025-07-17T07:11:36.1993659Z >>> input = torch.randn(128, 20) 2025-07-17T07:11:36.1993810Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-07-17T07:11:36.1993919Z >>> ret = ret_fut.wait() 2025-07-17T07:11:36.1994011Z >>> rpc.shutdown() 2025-07-17T07:11:36.1994091Z 2025-07-17T07:11:36.1994190Z >>> # On worker 1: 2025-07-17T07:11:36.1994278Z >>> import torch 2025-07-17T07:11:36.1994402Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:36.1994487Z >>> 2025-07-17T07:11:36.1994625Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T07:11:36.1994715Z >>> rpc.shutdown() 2025-07-17T07:11:36.1994801Z 2025-07-17T07:11:36.1994991Z Furthermore, a more practical example that is combined with 2025-07-17T07:11:36.1995487Z `DistributedDataParallel `__ (DDP) 2025-07-17T07:11:36.1995811Z can be found in this `tutorial `__. 2025-07-17T07:11:36.1995889Z 2025-07-17T07:11:36.1996147Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.1996222Z 2025-07-17T07:11:36.1996316Z warnings.warn(msg) 2025-07-17T07:11:36.1996403Z 2025-07-17T07:11:36.1996618Z --- Parse Warning: 80 / 136 --- 2025-07-17T07:11:36.1997624Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=129. 2025-07-17T07:11:36.1997884Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.1997960Z 2025-07-17T07:11:36.1998206Z DistributedOptimizer takes remote references to parameters scattered 2025-07-17T07:11:36.1998442Z across workers and applies the given optimizer locally for each parameter. 2025-07-17T07:11:36.1998517Z 2025-07-17T07:11:36.1998756Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-07-17T07:11:36.1998905Z to retrieve the gradients for specific parameters. 2025-07-17T07:11:36.1998984Z 2025-07-17T07:11:36.1999124Z Concurrent calls to 2025-07-17T07:11:36.1999332Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-07-17T07:11:36.1999482Z either from the same or different clients, will 2025-07-17T07:11:36.1999748Z be serialized on each worker -- as each worker's optimizer can only work 2025-07-17T07:11:36.1999955Z on one set of gradients at a time. However, there is no guarantee that 2025-07-17T07:11:36.2000207Z the full forward-backward-optimizer sequence will execute for one client 2025-07-17T07:11:36.2000419Z at a time. This means that the gradients being applied may not correspond 2025-07-17T07:11:36.2000642Z to the latest forward pass executed on a given worker. Also, there is no 2025-07-17T07:11:36.2000767Z guaranteed ordering across workers. 2025-07-17T07:11:36.2000843Z 2025-07-17T07:11:36.2001110Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-07-17T07:11:36.2001336Z by default, so that optimizer updates are not blocked by the Python Global 2025-07-17T07:11:36.2001587Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-07-17T07:11:36.2001838Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-07-17T07:11:36.2002095Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-07-17T07:11:36.2002203Z for your own custom optimizers. 2025-07-17T07:11:36.2002294Z 2025-07-17T07:11:36.2002374Z Args: 2025-07-17T07:11:36.2002580Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-07-17T07:11:36.2002684Z instantiate on each worker. 2025-07-17T07:11:36.2002888Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-07-17T07:11:36.2002993Z to optimize. 2025-07-17T07:11:36.2003204Z args: arguments to pass to the optimizer constructor on each worker. 2025-07-17T07:11:36.2003423Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-07-17T07:11:36.2003519Z 2025-07-17T07:11:36.2003605Z Example:: 2025-07-17T07:11:36.2003718Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:36.2003889Z >>> import torch.distributed.autograd as dist_autograd 2025-07-17T07:11:36.2004017Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:36.2004131Z >>> from torch import optim 2025-07-17T07:11:36.2004316Z >>> from torch.distributed.optim import DistributedOptimizer 2025-07-17T07:11:36.2004393Z >>> 2025-07-17T07:11:36.2004532Z >>> with dist_autograd.context() as context_id: 2025-07-17T07:11:36.2004626Z >>> # Forward pass. 2025-07-17T07:11:36.2004823Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-07-17T07:11:36.2005022Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-07-17T07:11:36.2005142Z >>> loss = rref1.to_here() + rref2.to_here() 2025-07-17T07:11:36.2005221Z >>> 2025-07-17T07:11:36.2005322Z >>> # Backward pass. 2025-07-17T07:11:36.2005497Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-07-17T07:11:36.2005584Z >>> 2025-07-17T07:11:36.2005672Z >>> # Optimizer. 2025-07-17T07:11:36.2005796Z >>> dist_optim = DistributedOptimizer( 2025-07-17T07:11:36.2005891Z >>> optim.SGD, 2025-07-17T07:11:36.2005982Z >>> [rref1, rref2], 2025-07-17T07:11:36.2006066Z >>> lr=0.05, 2025-07-17T07:11:36.2006151Z >>> ) 2025-07-17T07:11:36.2006261Z >>> dist_optim.step(context_id) 2025-07-17T07:11:36.2006336Z 2025-07-17T07:11:36.2006502Z __ https://github.com/pytorch/tutorials/pull/1465 2025-07-17T07:11:36.2006579Z 2025-07-17T07:11:36.2006825Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2006913Z 2025-07-17T07:11:36.2007004Z warnings.warn(msg) 2025-07-17T07:11:36.2007091Z 2025-07-17T07:11:36.2007342Z --- Parse Warning: 81 / 136 --- 2025-07-17T07:11:36.2008472Z /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. 2025-07-17T07:11:36.2008745Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2008822Z 2025-07-17T07:11:36.2009208Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-07-17T07:11:36.2009369Z This optimizer runs local optimizer at every step. 2025-07-17T07:11:36.2009707Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-07-17T07:11:36.2009788Z 2025-07-17T07:11:36.2009869Z Args: 2025-07-17T07:11:36.2009973Z optim: The local optimizer. 2025-07-17T07:11:36.2010201Z averager: A model averager instance to run post-localSGD algorithm. 2025-07-17T07:11:36.2010279Z 2025-07-17T07:11:36.2010364Z Example:: 2025-07-17T07:11:36.2010450Z 2025-07-17T07:11:36.2010578Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.2010671Z >>> import torch 2025-07-17T07:11:36.2010794Z >>> import torch.distributed as dist 2025-07-17T07:11:36.2011064Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-07-17T07:11:36.2011170Z >>> import torch.nn as nn 2025-07-17T07:11:36.2011362Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-07-17T07:11:36.2011630Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-07-17T07:11:36.2011742Z >>> PostLocalSGDState, 2025-07-17T07:11:36.2011839Z >>> post_localSGD_hook, 2025-07-17T07:11:36.2011915Z >>> ) 2025-07-17T07:11:36.2012006Z >>> 2025-07-17T07:11:36.2012160Z >>> model = nn.parallel.DistributedDataParallel( 2025-07-17T07:11:36.2012300Z >>> module, device_ids=[rank], output_device=rank 2025-07-17T07:11:36.2012391Z >>> ) 2025-07-17T07:11:36.2012470Z >>> 2025-07-17T07:11:36.2012614Z >>> # Register a post-localSGD communication hook. 2025-07-17T07:11:36.2012922Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-07-17T07:11:36.2013081Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-07-17T07:11:36.2013166Z >>> 2025-07-17T07:11:36.2013366Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-07-17T07:11:36.2013611Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-07-17T07:11:36.2013784Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-07-17T07:11:36.2013995Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-07-17T07:11:36.2014109Z >>> opt = PostLocalSGDOptimizer( 2025-07-17T07:11:36.2014251Z >>> optim=local_optim, 2025-07-17T07:11:36.2014500Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-07-17T07:11:36.2014586Z >>> ) 2025-07-17T07:11:36.2014669Z >>> 2025-07-17T07:11:36.2014889Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-07-17T07:11:36.2015311Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-07-17T07:11:36.2015693Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-07-17T07:11:36.2015796Z >>> for step in range(0, 200): 2025-07-17T07:11:36.2015902Z >>> opt.zero_grad() 2025-07-17T07:11:36.2016014Z >>> loss = loss_fn(output, labels) 2025-07-17T07:11:36.2016121Z >>> loss.backward() 2025-07-17T07:11:36.2016244Z >>> opt.step() 2025-07-17T07:11:36.2016323Z 2025-07-17T07:11:36.2016583Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2016660Z 2025-07-17T07:11:36.2016752Z warnings.warn(msg) 2025-07-17T07:11:36.2016842Z 2025-07-17T07:11:36.2017082Z --- Parse Warning: 82 / 136 --- 2025-07-17T07:11:36.2018188Z /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. 2025-07-17T07:11:36.2018457Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2018534Z 2025-07-17T07:11:36.2018950Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-07-17T07:11:36.2019026Z 2025-07-17T07:11:36.2019266Z The sharing is done as described by `ZeRO `_. 2025-07-17T07:11:36.2019350Z 2025-07-17T07:11:36.2019493Z The local optimizer instance in each rank is only 2025-07-17T07:11:36.2019730Z responsible for updating approximately ``1 / world_size`` parameters and 2025-07-17T07:11:36.2019935Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-07-17T07:11:36.2020178Z parameters are updated locally, each rank will broadcast its parameters to 2025-07-17T07:11:36.2020361Z all other peers to keep all model replicas in the same state. 2025-07-17T07:11:36.2020550Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-07-17T07:11:36.2020805Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-07-17T07:11:36.2020902Z memory consumption. 2025-07-17T07:11:36.2020980Z 2025-07-17T07:11:36.2021233Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-07-17T07:11:36.2021468Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-07-17T07:11:36.2021705Z not divided among ranks. The partition is arbitrary and might not match the 2025-07-17T07:11:36.2021832Z the parameter registration or usage order. 2025-07-17T07:11:36.2021909Z 2025-07-17T07:11:36.2021991Z Arguments: 2025-07-17T07:11:36.2022186Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-07-17T07:11:36.2022365Z or :class:`dict` s giving all parameters, which will be sharded 2025-07-17T07:11:36.2022454Z across ranks. 2025-07-17T07:11:36.2022533Z 2025-07-17T07:11:36.2022616Z Keyword Args: 2025-07-17T07:11:36.2022838Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-07-17T07:11:36.2022936Z optimizer. 2025-07-17T07:11:36.2023141Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-07-17T07:11:36.2023339Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-07-17T07:11:36.2023513Z :meth:`torch.distributed.init_process_group`). 2025-07-17T07:11:36.2023738Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-07-17T07:11:36.2023954Z packed into buckets to speed up communication, and ``param.data`` 2025-07-17T07:11:36.2024149Z fields point to bucket views at different offsets; if ``False``, 2025-07-17T07:11:36.2024347Z each individual parameter is communicated separately, and each 2025-07-17T07:11:36.2024505Z ``params.data`` stays intact (default: ``False``). 2025-07-17T07:11:36.2024695Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-07-17T07:11:36.2024894Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-07-17T07:11:36.2025103Z synchronization; this requires (1) either a functional optimizer 2025-07-17T07:11:36.2025281Z for the ``optimizer_class`` argument or one with a functional 2025-07-17T07:11:36.2025489Z equivalent and (2) registering a DDP communication hook 2025-07-17T07:11:36.2025687Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-07-17T07:11:36.2025898Z parameters are packed into buckets matching those in 2025-07-17T07:11:36.2026060Z :class:`DistributedDataParallel`, meaning that the 2025-07-17T07:11:36.2026207Z ``parameters_as_bucket_view`` argument is ignored. 2025-07-17T07:11:36.2026401Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-07-17T07:11:36.2026489Z (per normal). 2025-07-17T07:11:36.2026584Z (default: ``False``) 2025-07-17T07:11:36.2026795Z **defaults: any trailing arguments, which are forwarded to the local 2025-07-17T07:11:36.2026880Z optimizer. 2025-07-17T07:11:36.2026955Z 2025-07-17T07:11:36.2027047Z Example:: 2025-07-17T07:11:36.2027120Z 2025-07-17T07:11:36.2027214Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2027319Z >>> import torch.nn as nn 2025-07-17T07:11:36.2027518Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-07-17T07:11:36.2027718Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-07-17T07:11:36.2027944Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-07-17T07:11:36.2028055Z >>> ddp = DDP(model, device_ids=[rank]) 2025-07-17T07:11:36.2028174Z >>> opt = ZeroRedundancyOptimizer( 2025-07-17T07:11:36.2028268Z >>> ddp.parameters(), 2025-07-17T07:11:36.2028385Z >>> optimizer_class=torch.optim.Adam, 2025-07-17T07:11:36.2028474Z >>> lr=0.01 2025-07-17T07:11:36.2028553Z >>> ) 2025-07-17T07:11:36.2028657Z >>> ddp(inputs).sum().backward() 2025-07-17T07:11:36.2028749Z >>> opt.step() 2025-07-17T07:11:36.2028825Z 2025-07-17T07:11:36.2028904Z .. warning:: 2025-07-17T07:11:36.2029118Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-07-17T07:11:36.2029261Z passed-in parameters are the same dense type. 2025-07-17T07:11:36.2029339Z 2025-07-17T07:11:36.2029419Z .. warning:: 2025-07-17T07:11:36.2029628Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-07-17T07:11:36.2029831Z the way that overlapping :class:`DistributedDataParallel` with 2025-07-17T07:11:36.2030061Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-07-17T07:11:36.2030270Z two or three training iterations do not perform parameter updates in 2025-07-17T07:11:36.2030462Z the optimizer step, depending on if ``static_graph=False`` or 2025-07-17T07:11:36.2030639Z ``static_graph=True``, respectively. This is because it needs 2025-07-17T07:11:36.2030827Z information about the gradient bucketing strategy used by 2025-07-17T07:11:36.2031042Z :class:`DistributedDataParallel`, which is not finalized until the 2025-07-17T07:11:36.2031278Z second forward pass if ``static_graph=False`` or until the third 2025-07-17T07:11:36.2031487Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-07-17T07:11:36.2031594Z is to prepend dummy inputs. 2025-07-17T07:11:36.2031669Z 2025-07-17T07:11:36.2031932Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-07-17T07:11:36.2032008Z 2025-07-17T07:11:36.2032253Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2032337Z 2025-07-17T07:11:36.2032428Z warnings.warn(msg) 2025-07-17T07:11:36.2032508Z 2025-07-17T07:11:36.2032693Z --- Parse Warning: 83 / 136 --- 2025-07-17T07:11:36.2033818Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=29. 2025-07-17T07:11:36.2034133Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2034208Z 2025-07-17T07:11:36.2034478Z Custom reducer class that can be used to specify a custom operation that 2025-07-17T07:11:36.2034655Z reduces losses of multiple microbatches into one value. 2025-07-17T07:11:36.2034732Z 2025-07-17T07:11:36.2034818Z Example: 2025-07-17T07:11:36.2034909Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2035015Z >>> sum_reducer = _CustomReducer( 2025-07-17T07:11:36.2035117Z >>> torch.tensor(0.0), 2025-07-17T07:11:36.2035209Z >>> lambda a, b: a + b 2025-07-17T07:11:36.2035288Z >>> ) 2025-07-17T07:11:36.2035370Z 2025-07-17T07:11:36.2035615Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2035687Z 2025-07-17T07:11:36.2035783Z warnings.warn(msg) 2025-07-17T07:11:36.2035856Z 2025-07-17T07:11:36.2036044Z --- Parse Warning: 84 / 136 --- 2025-07-17T07:11:36.2036990Z /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. 2025-07-17T07:11:36.2037246Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2037328Z 2025-07-17T07:11:36.2037562Z A decorator for a function indicating that the return value of the function 2025-07-17T07:11:36.2037762Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-07-17T07:11:36.2038010Z function can run asynchronously on the RPC callee. More specifically, the 2025-07-17T07:11:36.2038240Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-07-17T07:11:36.2038474Z function and installs subsequent processing steps as a callback to that 2025-07-17T07:11:36.2038702Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-07-17T07:11:36.2038899Z from the :class:`~torch.futures.Future` when completed and send the 2025-07-17T07:11:36.2039083Z value back as the RPC response. That also means the returned 2025-07-17T07:11:36.2039309Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-07-17T07:11:36.2039524Z sent through RPC. This decorator is useful when the wrapped function's 2025-07-17T07:11:36.2039722Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-07-17T07:11:36.2039941Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-07-17T07:11:36.2040023Z 2025-07-17T07:11:36.2040230Z .. note:: To enable asynchronous execution, applications must pass the 2025-07-17T07:11:36.2040455Z function object returned by this decorator to RPC APIs. If RPC detected 2025-07-17T07:11:36.2040675Z attributes installed by this decorator, it knows that this function 2025-07-17T07:11:36.2040903Z returns a ``Future`` object and will handle that accordingly. 2025-07-17T07:11:36.2041108Z However, this does not mean this decorator has to be outmost one when 2025-07-17T07:11:36.2041338Z defining a function. For example, when combined with ``@staticmethod`` 2025-07-17T07:11:36.2041543Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-07-17T07:11:36.2041764Z inner decorator to allow the target function be recognized as a static 2025-07-17T07:11:36.2041988Z or class function. This target function can still execute asynchronously 2025-07-17T07:11:36.2042205Z because, when accessed, the static or class method preserves attributes 2025-07-17T07:11:36.2042361Z installed by ``@rpc.functions.async_execution``. 2025-07-17T07:11:36.2042436Z 2025-07-17T07:11:36.2042511Z 2025-07-17T07:11:36.2042604Z Example:: 2025-07-17T07:11:36.2042803Z The returned :class:`~torch.futures.Future` object can come from 2025-07-17T07:11:36.2042957Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-07-17T07:11:36.2043183Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-07-17T07:11:36.2043400Z constructor. The example below shows directly using the 2025-07-17T07:11:36.2043538Z :class:`~torch.futures.Future` returned by 2025-07-17T07:11:36.2043657Z :meth:`~torch.futures.Future.then`. 2025-07-17T07:11:36.2043731Z 2025-07-17T07:11:36.2043856Z >>> from torch.distributed import rpc 2025-07-17T07:11:36.2043934Z >>> 2025-07-17T07:11:36.2044043Z >>> # omitting setup and shutdown RPC 2025-07-17T07:11:36.2044130Z >>> 2025-07-17T07:11:36.2044220Z >>> # On all workers 2025-07-17T07:11:36.2044330Z >>> @rpc.functions.async_execution 2025-07-17T07:11:36.2044452Z >>> def async_add_chained(to, x, y, z): 2025-07-17T07:11:36.2044638Z >>> # This function runs on "worker1" and returns immediately when 2025-07-17T07:11:36.2044838Z >>> # the callback is installed through the `then(cb)` API. In the 2025-07-17T07:11:36.2045017Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-07-17T07:11:36.2045173Z >>> # When the return value of that `rpc_async` arrives at 2025-07-17T07:11:36.2045363Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-07-17T07:11:36.2045544Z >>> # and set the value for the previously returned `Future`, which 2025-07-17T07:11:36.2045721Z >>> # will then trigger RPC to send the result back to "worker0". 2025-07-17T07:11:36.2045894Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T07:11:36.2049572Z >>> lambda fut: fut.wait() + z 2025-07-17T07:11:36.2049673Z >>> ) 2025-07-17T07:11:36.2049755Z >>> 2025-07-17T07:11:36.2049851Z >>> # On worker0 2025-07-17T07:11:36.2049956Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2050051Z >>> ret = rpc.rpc_sync( 2025-07-17T07:11:36.2050148Z >>> "worker1", 2025-07-17T07:11:36.2050244Z >>> async_add_chained, 2025-07-17T07:11:36.2050367Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-07-17T07:11:36.2050453Z >>> ) 2025-07-17T07:11:36.2050569Z >>> print(ret) # prints tensor([3., 3.]) 2025-07-17T07:11:36.2050653Z 2025-07-17T07:11:36.2050883Z When combined with TorchScript decorators, this decorator must be the 2025-07-17T07:11:36.2050964Z outmost one. 2025-07-17T07:11:36.2051048Z 2025-07-17T07:11:36.2051150Z >>> from torch import Tensor 2025-07-17T07:11:36.2051261Z >>> from torch.futures import Future 2025-07-17T07:11:36.2051386Z >>> from torch.distributed import rpc 2025-07-17T07:11:36.2051466Z >>> 2025-07-17T07:11:36.2051572Z >>> # omitting setup and shutdown RPC 2025-07-17T07:11:36.2051663Z >>> 2025-07-17T07:11:36.2051752Z >>> # On all workers 2025-07-17T07:11:36.2051917Z >>> @torch.jit.script 2025-07-17T07:11:36.2052071Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-07-17T07:11:36.2052158Z >>> return x + y 2025-07-17T07:11:36.2052245Z >>> 2025-07-17T07:11:36.2052359Z >>> @rpc.functions.async_execution 2025-07-17T07:11:36.2052449Z >>> @torch.jit.script 2025-07-17T07:11:36.2052644Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-07-17T07:11:36.2052781Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-07-17T07:11:36.2052855Z >>> 2025-07-17T07:11:36.2052945Z >>> # On worker0 2025-07-17T07:11:36.2053034Z >>> ret = rpc.rpc_sync( 2025-07-17T07:11:36.2053115Z >>> "worker1", 2025-07-17T07:11:36.2053206Z >>> async_add, 2025-07-17T07:11:36.2053315Z >>> args=("worker2", torch.ones(2), 1) 2025-07-17T07:11:36.2053389Z >>> ) 2025-07-17T07:11:36.2053539Z >>> print(ret) # prints tensor([2., 2.]) 2025-07-17T07:11:36.2053613Z 2025-07-17T07:11:36.2053840Z When combined with static or class method, this decorator must be the 2025-07-17T07:11:36.2053920Z inner one. 2025-07-17T07:11:36.2054073Z 2025-07-17T07:11:36.2054200Z >>> from torch.distributed import rpc 2025-07-17T07:11:36.2054275Z >>> 2025-07-17T07:11:36.2054385Z >>> # omitting setup and shutdown RPC 2025-07-17T07:11:36.2054467Z >>> 2025-07-17T07:11:36.2054553Z >>> # On all workers 2025-07-17T07:11:36.2054659Z >>> class AsyncExecutionClass: 2025-07-17T07:11:36.2054742Z >>> 2025-07-17T07:11:36.2054829Z >>> @staticmethod 2025-07-17T07:11:36.2054941Z >>> @rpc.functions.async_execution 2025-07-17T07:11:36.2055059Z >>> def static_async_add(to, x, y, z): 2025-07-17T07:11:36.2055309Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T07:11:36.2055429Z >>> lambda fut: fut.wait() + z 2025-07-17T07:11:36.2055510Z >>> ) 2025-07-17T07:11:36.2055586Z >>> 2025-07-17T07:11:36.2055683Z >>> @classmethod 2025-07-17T07:11:36.2055798Z >>> @rpc.functions.async_execution 2025-07-17T07:11:36.2055919Z >>> def class_async_add(cls, to, x, y, z): 2025-07-17T07:11:36.2056046Z >>> ret_fut = torch.futures.Future() 2025-07-17T07:11:36.2056193Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T07:11:36.2056342Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-07-17T07:11:36.2056434Z >>> ) 2025-07-17T07:11:36.2056525Z >>> return ret_fut 2025-07-17T07:11:36.2056612Z >>> 2025-07-17T07:11:36.2056723Z >>> @rpc.functions.async_execution 2025-07-17T07:11:36.2056845Z >>> def bound_async_add(self, to, x, y, z): 2025-07-17T07:11:36.2057017Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-07-17T07:11:36.2057127Z >>> lambda fut: fut.wait() + z 2025-07-17T07:11:36.2057207Z >>> ) 2025-07-17T07:11:36.2057291Z >>> 2025-07-17T07:11:36.2057377Z >>> # On worker0 2025-07-17T07:11:36.2057472Z >>> ret = rpc.rpc_sync( 2025-07-17T07:11:36.2057563Z >>> "worker1", 2025-07-17T07:11:36.2057694Z >>> AsyncExecutionClass.static_async_add, 2025-07-17T07:11:36.2057816Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-07-17T07:11:36.2057892Z >>> ) 2025-07-17T07:11:36.2058004Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:36.2058082Z >>> 2025-07-17T07:11:36.2058173Z >>> ret = rpc.rpc_sync( 2025-07-17T07:11:36.2058257Z >>> "worker1", 2025-07-17T07:11:36.2058391Z >>> AsyncExecutionClass.class_async_add, 2025-07-17T07:11:36.2058501Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-07-17T07:11:36.2058580Z >>> ) 2025-07-17T07:11:36.2058731Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:36.2058805Z 2025-07-17T07:11:36.2058959Z This decorator also works with RRef helpers, i.e., . 2025-07-17T07:11:36.2059105Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-07-17T07:11:36.2059255Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-07-17T07:11:36.2059391Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-07-17T07:11:36.2059462Z 2025-07-17T07:11:36.2059577Z >>> from torch.distributed import rpc 2025-07-17T07:11:36.2059659Z >>> 2025-07-17T07:11:36.2059787Z >>> # reuse the AsyncExecutionClass class above 2025-07-17T07:11:36.2059932Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T07:11:36.2060143Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-07-17T07:11:36.2060255Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:36.2060356Z >>> 2025-07-17T07:11:36.2060510Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T07:11:36.2060739Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-07-17T07:11:36.2060921Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:36.2060995Z >>> 2025-07-17T07:11:36.2061140Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-07-17T07:11:36.2061372Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-07-17T07:11:36.2061486Z >>> print(ret) # prints tensor([4., 4.]) 2025-07-17T07:11:36.2061562Z 2025-07-17T07:11:36.2061820Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2061896Z 2025-07-17T07:11:36.2061998Z warnings.warn(msg) 2025-07-17T07:11:36.2062077Z 2025-07-17T07:11:36.2062305Z --- Parse Warning: 85 / 136 --- 2025-07-17T07:11:36.2063414Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=113. 2025-07-17T07:11:36.2063684Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2063760Z 2025-07-17T07:11:36.2063975Z Set device mapping between each RPC caller and callee pair. This 2025-07-17T07:11:36.2064155Z function can be called multiple times to incrementally add 2025-07-17T07:11:36.2064273Z device placement configurations. 2025-07-17T07:11:36.2064350Z 2025-07-17T07:11:36.2064430Z Args: 2025-07-17T07:11:36.2064534Z to (str): Callee name. 2025-07-17T07:11:36.2064727Z device_map (Dict of int, str, or torch.device): Device placement 2025-07-17T07:11:36.2064902Z mappings from this worker to the callee. This map must be 2025-07-17T07:11:36.2064996Z invertible. 2025-07-17T07:11:36.2065074Z 2025-07-17T07:11:36.2065156Z Example: 2025-07-17T07:11:36.2065274Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:36.2065361Z >>> # both workers 2025-07-17T07:11:36.2065448Z >>> def add(x, y): 2025-07-17T07:11:36.2065593Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-07-17T07:11:36.2065693Z >>> return x + y, (x + y).to(2) 2025-07-17T07:11:36.2065777Z >>> 2025-07-17T07:11:36.2065863Z >>> # on worker 0 2025-07-17T07:11:36.2065995Z >>> options = TensorPipeRpcBackendOptions( 2025-07-17T07:11:36.2066101Z >>> num_worker_threads=8, 2025-07-17T07:11:36.2066206Z >>> device_maps={"worker1": {0: 1}} 2025-07-17T07:11:36.2066327Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-07-17T07:11:36.2066410Z >>> ) 2025-07-17T07:11:36.2066536Z >>> options.set_device_map("worker1", {1: 2}) 2025-07-17T07:11:36.2066656Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-07-17T07:11:36.2066771Z >>> 2025-07-17T07:11:36.2066860Z >>> rpc.init_rpc( 2025-07-17T07:11:36.2066939Z >>> "worker0", 2025-07-17T07:11:36.2067028Z >>> rank=0, 2025-07-17T07:11:36.2067117Z >>> world_size=2, 2025-07-17T07:11:36.2067247Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-07-17T07:11:36.2067354Z >>> rpc_backend_options=options 2025-07-17T07:11:36.2067430Z >>> ) 2025-07-17T07:11:36.2067514Z >>> 2025-07-17T07:11:36.2067604Z >>> x = torch.ones(2) 2025-07-17T07:11:36.2067757Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-07-17T07:11:36.2067943Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-07-17T07:11:36.2068121Z >>> # sending the return value back, it will follow the invert of 2025-07-17T07:11:36.2068293Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-07-17T07:11:36.2068406Z >>> # cuda:1 on worker0 2025-07-17T07:11:36.2068562Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-07-17T07:11:36.2068702Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-07-17T07:11:36.2068780Z 2025-07-17T07:11:36.2069093Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2069171Z 2025-07-17T07:11:36.2069275Z warnings.warn(msg) 2025-07-17T07:11:36.2069353Z 2025-07-17T07:11:36.2069540Z --- Parse Warning: 86 / 136 --- 2025-07-17T07:11:36.2070668Z /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. 2025-07-17T07:11:36.2070929Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2071006Z 2025-07-17T07:11:36.2071212Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-07-17T07:11:36.2071490Z except that it enables profiling on all threads running RPC server request callbacks. 2025-07-17T07:11:36.2071573Z 2025-07-17T07:11:36.2072125Z Context manager that manages autograd profiler state and holds a summary of results. 2025-07-17T07:11:36.2072357Z Under the hood it just records events of functions being executed in C++ and 2025-07-17T07:11:36.2072595Z exposes those events to Python. You can wrap any code into it and it will 2025-07-17T07:11:36.2072718Z only report runtime of PyTorch functions. 2025-07-17T07:11:36.2072980Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-07-17T07:11:36.2073068Z 2025-07-17T07:11:36.2073147Z Args: 2025-07-17T07:11:36.2073422Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-07-17T07:11:36.2073520Z Default: ``True``. 2025-07-17T07:11:36.2073599Z 2025-07-17T07:11:36.2073889Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-07-17T07:11:36.2074084Z Adds approximately 4us of overhead to each tensor operation. 2025-07-17T07:11:36.2074179Z Default: ``False`` 2025-07-17T07:11:36.2074262Z 2025-07-17T07:11:36.2074485Z record_shapes (bool, optional): If shapes recording is set, information 2025-07-17T07:11:36.2074708Z about input dimensions will be collected. This allows one to see which 2025-07-17T07:11:36.2074926Z dimensions have been used under the hood and further group by them 2025-07-17T07:11:36.2075141Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-07-17T07:11:36.2075369Z shape recording might skew your profiling data. It is recommended to 2025-07-17T07:11:36.2075606Z use separate runs with and without shape recording to validate the timing. 2025-07-17T07:11:36.2075831Z Most likely the skew will be negligible for bottom most events (in a case 2025-07-17T07:11:36.2076127Z of nested function calls). But for higher level functions the total 2025-07-17T07:11:36.2076335Z self cpu time might be artificially increased because of the shape 2025-07-17T07:11:36.2076426Z collection. 2025-07-17T07:11:36.2076512Z 2025-07-17T07:11:36.2076778Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-07-17T07:11:36.2076865Z 2025-07-17T07:11:36.2076960Z .. warning:: 2025-07-17T07:11:36.2077161Z Enabling memory profiling incurs additional profiler overhead 2025-07-17T07:11:36.2077243Z 2025-07-17T07:11:36.2077328Z .. warning:: 2025-07-17T07:11:36.2077614Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-07-17T07:11:36.2077815Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-07-17T07:11:36.2078084Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-07-17T07:11:36.2078244Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-07-17T07:11:36.2078331Z 2025-07-17T07:11:36.2078473Z Example: 2025-07-17T07:11:36.2078576Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2078664Z >>> # On worker 0: 2025-07-17T07:11:36.2078754Z >>> import torch 2025-07-17T07:11:36.2078884Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:36.2079018Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-07-17T07:11:36.2079138Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-07-17T07:11:36.2079257Z >>> outer_profile_rref = rpc.remote( 2025-07-17T07:11:36.2079417Z ... dst_worker_name, rpc._server_process_global_profile 2025-07-17T07:11:36.2079496Z ... ) 2025-07-17T07:11:36.2079630Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-07-17T07:11:36.2079777Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-07-17T07:11:36.2079900Z >>> inner_profile_rref = rpc.remote( 2025-07-17T07:11:36.2080057Z ... dst_worker_name, rpc._server_process_global_profile 2025-07-17T07:11:36.2080138Z ... ) 2025-07-17T07:11:36.2080272Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-07-17T07:11:36.2080415Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-07-17T07:11:36.2080586Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-07-17T07:11:36.2080761Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-07-17T07:11:36.2080916Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-07-17T07:11:36.2081161Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:36.2081473Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-07-17T07:11:36.2081704Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:36.2081902Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-07-17T07:11:36.2082090Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-07-17T07:11:36.2082317Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:36.2082432Z Self CPU time total: 89.667us 2025-07-17T07:11:36.2082589Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-07-17T07:11:36.2082819Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:36.2083124Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-07-17T07:11:36.2083374Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:36.2083565Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-07-17T07:11:36.2083751Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-07-17T07:11:36.2083936Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-07-17T07:11:36.2084160Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-07-17T07:11:36.2084265Z Self CPU time total: 213.926us 2025-07-17T07:11:36.2084367Z >>> rpc.shutdown() 2025-07-17T07:11:36.2084442Z 2025-07-17T07:11:36.2084529Z >>> # On worker 1: 2025-07-17T07:11:36.2084662Z >>> import torch.distributed.rpc as rpc 2025-07-17T07:11:36.2084824Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-07-17T07:11:36.2084993Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-07-17T07:11:36.2085082Z >>> rpc.shutdown() 2025-07-17T07:11:36.2085159Z 2025-07-17T07:11:36.2085462Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2085538Z 2025-07-17T07:11:36.2085632Z warnings.warn(msg) 2025-07-17T07:11:36.2085710Z 2025-07-17T07:11:36.2085909Z --- Parse Warning: 87 / 136 --- 2025-07-17T07:11:36.2086928Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=35. 2025-07-17T07:11:36.2087200Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2087277Z 2025-07-17T07:11:36.2087553Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-07-17T07:11:36.2087827Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-07-17T07:11:36.2088092Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-07-17T07:11:36.2088259Z :class:`DTensor` according to the ``out_placements``. 2025-07-17T07:11:36.2088334Z 2025-07-17T07:11:36.2088415Z Args: 2025-07-17T07:11:36.2088626Z func (Callable): the function to be applied on each local shard of 2025-07-17T07:11:36.2088721Z :class:`DTensor` s. 2025-07-17T07:11:36.2088957Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-07-17T07:11:36.2089208Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-07-17T07:11:36.2089444Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-07-17T07:11:36.2089697Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-07-17T07:11:36.2089943Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-07-17T07:11:36.2090064Z mapping to the flattened ``output``. 2025-07-17T07:11:36.2090273Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-07-17T07:11:36.2090541Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-07-17T07:11:36.2090644Z should be `None`. 2025-07-17T07:11:36.2090873Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-07-17T07:11:36.2091089Z in. In this case, even if `out_placements` is not `None`, the result function 2025-07-17T07:11:36.2091351Z should ignore the desired placements because the function is not running with 2025-07-17T07:11:36.2091444Z :class:`DTensor` s. 2025-07-17T07:11:36.2091605Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-07-17T07:11:36.2091918Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-07-17T07:11:36.2092152Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-07-17T07:11:36.2092386Z placements of each :class:`DTensor` argument is the same as the required 2025-07-17T07:11:36.2092563Z placements or not. If the placements are not the same and 2025-07-17T07:11:36.2092801Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-07-17T07:11:36.2093050Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-07-17T07:11:36.2093303Z the required sharding placements before passing its local tensor to ``func``. 2025-07-17T07:11:36.2093532Z The only exception is when required placements are not ``None`` and the 2025-07-17T07:11:36.2093799Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-07-17T07:11:36.2094012Z will be skipped and the argument will be directly passed to ``func``. 2025-07-17T07:11:36.2094301Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-07-17T07:11:36.2094394Z Default: None 2025-07-17T07:11:36.2094572Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-07-17T07:11:36.2094784Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-07-17T07:11:36.2094989Z to the flattened input DTensor. This argument is the hint that user 2025-07-17T07:11:36.2095254Z can give to :meth:`to_local` in case the gradient layout of the 2025-07-17T07:11:36.2095472Z local tensor input does not match its :class:`DTensor` input layout. 2025-07-17T07:11:36.2095666Z If not specified, we will assume the gradient layout of the local 2025-07-17T07:11:36.2095894Z tensor input remains the same as the original :class:`DTensor` input 2025-07-17T07:11:36.2096061Z and use that for gradient computation. Default: None. 2025-07-17T07:11:36.2096198Z device_mesh (:class:`DeviceMesh`, optional): 2025-07-17T07:11:36.2096431Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-07-17T07:11:36.2096685Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-07-17T07:11:36.2096792Z mesh. Default: None. 2025-07-17T07:11:36.2096870Z 2025-07-17T07:11:36.2096954Z Keyword Args: 2025-07-17T07:11:36.2097081Z redistribute_inputs (bool, optional): 2025-07-17T07:11:36.2097331Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-07-17T07:11:36.2097572Z their placements are different from the required input placements. If this 2025-07-17T07:11:36.2097803Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-07-17T07:11:36.2097943Z an exception will be raised. Default: False. 2025-07-17T07:11:36.2098028Z 2025-07-17T07:11:36.2098108Z Returns: 2025-07-17T07:11:36.2098363Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-07-17T07:11:36.2098611Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-07-17T07:11:36.2098687Z 2025-07-17T07:11:36.2098760Z Raises: 2025-07-17T07:11:36.2099004Z AssertionError: For any non-DTensor output, we require its corresponding 2025-07-17T07:11:36.2099260Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-07-17T07:11:36.2099356Z if this is not the case. 2025-07-17T07:11:36.2099443Z 2025-07-17T07:11:36.2099702Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-07-17T07:11:36.2099864Z a redistribution according to ``in_placements``. 2025-07-17T07:11:36.2099972Z 2025-07-17T07:11:36.2100050Z Example: 2025-07-17T07:11:36.2100170Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:36.2100307Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-07-17T07:11:36.2100422Z >>> partial_sum_tensor = torch.mm(W, X) 2025-07-17T07:11:36.2100669Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-07-17T07:11:36.2100769Z >>> return reduced_tensor 2025-07-17T07:11:36.2100845Z >>> 2025-07-17T07:11:36.2100977Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-07-17T07:11:36.2101094Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-07-17T07:11:36.2101192Z >>> Y = torch.mm(W, X) 2025-07-17T07:11:36.2101375Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-07-17T07:11:36.2101555Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-07-17T07:11:36.2101670Z >>> 2025-07-17T07:11:36.2101939Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion 2025-07-17T07:11:36.2102059Z >>> local_mm_allreduce_forward = local_map( 2025-07-17T07:11:36.2102213Z >>> mm_allreduce_forward, 2025-07-17T07:11:36.2102325Z >>> out_placements=[Replicate()], 2025-07-17T07:11:36.2102440Z >>> in_placements=[col_wise, row_wise], 2025-07-17T07:11:36.2102548Z >>> device_mesh=device_mesh, 2025-07-17T07:11:36.2102621Z >>> ) 2025-07-17T07:11:36.2102702Z >>> 2025-07-17T07:11:36.2102800Z >>> W_dt = distribute_tensor( 2025-07-17T07:11:36.2102896Z ... W, device_mesh, (col_wise) 2025-07-17T07:11:36.2103009Z ... ) # col-wisely sharded W tensor 2025-07-17T07:11:36.2103109Z >>> X_dt = distribute_tensor( 2025-07-17T07:11:36.2103204Z ... X, device_mesh, (row_wise) 2025-07-17T07:11:36.2103323Z ... ) # row-wisely sharded X tensor 2025-07-17T07:11:36.2103434Z >>> Y_dt = local_mm_allreduce_forward( 2025-07-17T07:11:36.2103529Z ... device_mesh, W_dt, X_dt 2025-07-17T07:11:36.2103684Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-07-17T07:11:36.2103758Z 2025-07-17T07:11:36.2103964Z .. note:: This API is currently experimental and subject to change 2025-07-17T07:11:36.2104042Z 2025-07-17T07:11:36.2104289Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2104374Z 2025-07-17T07:11:36.2104469Z warnings.warn(msg) 2025-07-17T07:11:36.2104544Z 2025-07-17T07:11:36.2104760Z --- Parse Warning: 88 / 136 --- 2025-07-17T07:11:36.2105838Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2025-07-17T07:11:36.2106108Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2106183Z 2025-07-17T07:11:36.2106461Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-07-17T07:11:36.2106703Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-07-17T07:11:36.2106949Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-07-17T07:11:36.2107185Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-07-17T07:11:36.2107468Z when users would like to overwrite default sharding strategies of existing operators. 2025-07-17T07:11:36.2107546Z 2025-07-17T07:11:36.2107631Z Args: 2025-07-17T07:11:36.2107759Z op (Union[OpOverload, List[OpOverload]]): 2025-07-17T07:11:36.2107952Z An op or a list of ops to register the customized sharding function. 2025-07-17T07:11:36.2108072Z 2025-07-17T07:11:36.2108151Z Returns: 2025-07-17T07:11:36.2108413Z A function decorator which can be used to wrap a function that defines the sharding 2025-07-17T07:11:36.2108691Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-07-17T07:11:36.2108962Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-07-17T07:11:36.2109264Z already implemented the operator. The customized sharding function takes the same inputs 2025-07-17T07:11:36.2109509Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-07-17T07:11:36.2109773Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-07-17T07:11:36.2110048Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-07-17T07:11:36.2110165Z corresponding intput placements. 2025-07-17T07:11:36.2110267Z 2025-07-17T07:11:36.2110355Z Example: 2025-07-17T07:11:36.2110467Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:36.2110596Z >>> @register_sharding(aten._softmax.default) 2025-07-17T07:11:36.2110800Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-07-17T07:11:36.2110938Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-07-17T07:11:36.2111048Z >>> acceptable_shardings = [] 2025-07-17T07:11:36.2111125Z >>> 2025-07-17T07:11:36.2111296Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-07-17T07:11:36.2111435Z >>> acceptable_shardings.append(all_replicate) 2025-07-17T07:11:36.2111512Z >>> 2025-07-17T07:11:36.2111623Z >>> for sharding_dim in range(x.ndim): 2025-07-17T07:11:36.2111738Z >>> if sharding_dim != softmax_dim: 2025-07-17T07:11:36.2111831Z >>> all_sharded = ( 2025-07-17T07:11:36.2111935Z >>> [Shard(sharding_dim)], 2025-07-17T07:11:36.2112066Z >>> [Shard(sharding_dim), None, None], 2025-07-17T07:11:36.2112145Z >>> ) 2025-07-17T07:11:36.2112283Z >>> acceptable_shardings.append(all_sharded) 2025-07-17T07:11:36.2112362Z >>> 2025-07-17T07:11:36.2112468Z >>> return acceptable_shardings 2025-07-17T07:11:36.2112549Z 2025-07-17T07:11:36.2112741Z .. note:: This API is currently experimental and subject to change 2025-07-17T07:11:36.2112816Z 2025-07-17T07:11:36.2113066Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2113139Z 2025-07-17T07:11:36.2113230Z warnings.warn(msg) 2025-07-17T07:11:36.2113311Z 2025-07-17T07:11:36.2113493Z --- Parse Warning: 89 / 136 --- 2025-07-17T07:11:36.2114533Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=428. 2025-07-17T07:11:36.2114792Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2114865Z 2025-07-17T07:11:36.2115254Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-07-17T07:11:36.2115572Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-07-17T07:11:36.2115646Z 2025-07-17T07:11:36.2115735Z Keyword Args: 2025-07-17T07:11:36.2115933Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T07:11:36.2116271Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-07-17T07:11:36.2116629Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-07-17T07:11:36.2116745Z as a placeholder. default: None. 2025-07-17T07:11:36.2117010Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T07:11:36.2117388Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T07:11:36.2117780Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-07-17T07:11:36.2117916Z input_kwarg_layouts (Dict[str, Placement]): 2025-07-17T07:11:36.2118288Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-07-17T07:11:36.2118384Z default: None 2025-07-17T07:11:36.2118541Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-07-17T07:11:36.2118908Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T07:11:36.2119084Z have the desired DTensor layouts. default: None. 2025-07-17T07:11:36.2119200Z use_local_output (bool, optional): 2025-07-17T07:11:36.2119615Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-07-17T07:11:36.2119695Z Returns: 2025-07-17T07:11:36.2120007Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-07-17T07:11:36.2120089Z 2025-07-17T07:11:36.2120171Z Example:: 2025-07-17T07:11:36.2120275Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:36.2120590Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-07-17T07:11:36.2120778Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T07:11:36.2120858Z >>> ... 2025-07-17T07:11:36.2121165Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T07:11:36.2121291Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T07:11:36.2121377Z >>> 2025-07-17T07:11:36.2121715Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-07-17T07:11:36.2121858Z >>> # and then redistributed to Replicated DTensor. 2025-07-17T07:11:36.2121964Z >>> parallelize_module( 2025-07-17T07:11:36.2122093Z >>> block, # this can be a submodule or module 2025-07-17T07:11:36.2122178Z >>> tp_mesh, 2025-07-17T07:11:36.2122283Z >>> parallelize_plan={ 2025-07-17T07:11:36.2122399Z >>> "attn": PrepareModuleInput( 2025-07-17T07:11:36.2122536Z >>> input_layouts=(Shard(0), None, None, ...), 2025-07-17T07:11:36.2122698Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-07-17T07:11:36.2122779Z >>> ), 2025-07-17T07:11:36.2122863Z >>> } 2025-07-17T07:11:36.2122942Z >>> ) 2025-07-17T07:11:36.2123019Z 2025-07-17T07:11:36.2123273Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2123348Z 2025-07-17T07:11:36.2123440Z warnings.warn(msg) 2025-07-17T07:11:36.2123523Z 2025-07-17T07:11:36.2123703Z --- Parse Warning: 90 / 136 --- 2025-07-17T07:11:36.2124741Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=597. 2025-07-17T07:11:36.2125004Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2125080Z 2025-07-17T07:11:36.2125478Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-07-17T07:11:36.2125807Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-07-17T07:11:36.2125912Z 2025-07-17T07:11:36.2126004Z Keyword Args: 2025-07-17T07:11:36.2126171Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T07:11:36.2126517Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-07-17T07:11:36.2126892Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-07-17T07:11:36.2127031Z ``None`` need to be specified as a placeholder. 2025-07-17T07:11:36.2127231Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T07:11:36.2127618Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-07-17T07:11:36.2127731Z have the desired DTensor layouts. 2025-07-17T07:11:36.2127851Z use_local_output (bool, optional): 2025-07-17T07:11:36.2128249Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-07-17T07:11:36.2128340Z Returns: 2025-07-17T07:11:36.2128671Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-07-17T07:11:36.2128748Z 2025-07-17T07:11:36.2128840Z Example:: 2025-07-17T07:11:36.2128938Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:36.2129247Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-07-17T07:11:36.2129444Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T07:11:36.2129521Z >>> ... 2025-07-17T07:11:36.2129825Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T07:11:36.2129955Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T07:11:36.2130035Z >>> 2025-07-17T07:11:36.2130451Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-07-17T07:11:36.2130583Z >>> # and then redistributed to Sharded DTensor. 2025-07-17T07:11:36.2130685Z >>> parallelize_module( 2025-07-17T07:11:36.2130819Z >>> block, # this can be a submodule or module 2025-07-17T07:11:36.2130907Z >>> tp_mesh, 2025-07-17T07:11:36.2131040Z >>> parallelize_plan = PrepareModuleOutput( 2025-07-17T07:11:36.2131159Z >>> output_layouts=Replicate(), 2025-07-17T07:11:36.2131269Z >>> desired_output_layouts=Shard(0) 2025-07-17T07:11:36.2131354Z >>> ) 2025-07-17T07:11:36.2131431Z >>> ) 2025-07-17T07:11:36.2131502Z 2025-07-17T07:11:36.2131757Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2131832Z 2025-07-17T07:11:36.2131923Z warnings.warn(msg) 2025-07-17T07:11:36.2132004Z 2025-07-17T07:11:36.2132183Z --- Parse Warning: 91 / 136 --- 2025-07-17T07:11:36.2133256Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-07-17T07:11:36.2133551Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2133669Z 2025-07-17T07:11:36.2134171Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-07-17T07:11:36.2134592Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-07-17T07:11:36.2134966Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-07-17T07:11:36.2135250Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-07-17T07:11:36.2135379Z 2025-07-17T07:11:36.2135465Z Keyword Args: 2025-07-17T07:11:36.2135671Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T07:11:36.2136001Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-07-17T07:11:36.2136368Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-07-17T07:11:36.2136482Z as a placeholder. default: None. 2025-07-17T07:11:36.2136708Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-07-17T07:11:36.2137095Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T07:11:36.2137489Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-07-17T07:11:36.2137654Z input_kwarg_layouts (Dict[str, Placement]): 2025-07-17T07:11:36.2138030Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-07-17T07:11:36.2138169Z default: None 2025-07-17T07:11:36.2138337Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-07-17T07:11:36.2138703Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-07-17T07:11:36.2138842Z have the desired DTensor layouts. default: None. 2025-07-17T07:11:36.2138959Z use_local_input (bool, optional): 2025-07-17T07:11:36.2139316Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-07-17T07:11:36.2139485Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T07:11:36.2139823Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-07-17T07:11:36.2140200Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-07-17T07:11:36.2140349Z ``None`` need to be specified as a placeholder. 2025-07-17T07:11:36.2140541Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-07-17T07:11:36.2140930Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-07-17T07:11:36.2141041Z have the desired DTensor layouts. 2025-07-17T07:11:36.2141151Z use_local_output (bool, optional): 2025-07-17T07:11:36.2141513Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-07-17T07:11:36.2141595Z Returns: 2025-07-17T07:11:36.2141957Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-07-17T07:11:36.2142045Z 2025-07-17T07:11:36.2142132Z Example:: 2025-07-17T07:11:36.2142231Z >>> # xdoctest: +SKIP(failing) 2025-07-17T07:11:36.2142578Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-07-17T07:11:36.2142763Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-07-17T07:11:36.2142847Z >>> ... 2025-07-17T07:11:36.2143148Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-07-17T07:11:36.2143269Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-07-17T07:11:36.2143351Z >>> 2025-07-17T07:11:36.2143681Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-07-17T07:11:36.2144039Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-07-17T07:11:36.2144247Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-07-17T07:11:36.2144373Z >>> parallelize_module( 2025-07-17T07:11:36.2144509Z >>> block, # this can be a submodule or module 2025-07-17T07:11:36.2144595Z >>> tp_mesh, 2025-07-17T07:11:36.2144698Z >>> parallelize_plan={ 2025-07-17T07:11:36.2144832Z >>> "attn": PrepareModuleInputOutput( 2025-07-17T07:11:36.2144963Z >>> input_layouts=(Shard(0), None, None, ...), 2025-07-17T07:11:36.2145126Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-07-17T07:11:36.2145246Z >>> output_layouts=Replicate(), 2025-07-17T07:11:36.2145364Z >>> desired_output_layouts=Shard(0), 2025-07-17T07:11:36.2145448Z >>> ), 2025-07-17T07:11:36.2145526Z >>> } 2025-07-17T07:11:36.2145602Z >>> ) 2025-07-17T07:11:36.2145682Z 2025-07-17T07:11:36.2145931Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2146032Z 2025-07-17T07:11:36.2146131Z warnings.warn(msg) 2025-07-17T07:11:36.2146209Z 2025-07-17T07:11:36.2146407Z --- Parse Warning: 92 / 136 --- 2025-07-17T07:11:36.2147574Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-07-17T07:11:36.2147836Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2147917Z 2025-07-17T07:11:36.2148224Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-07-17T07:11:36.2148406Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-07-17T07:11:36.2148487Z 2025-07-17T07:11:36.2148658Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-07-17T07:11:36.2148734Z 2025-07-17T07:11:36.2148822Z Example: 2025-07-17T07:11:36.2148964Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-07-17T07:11:36.2149096Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:36.2149221Z >>> m = LowRankMultivariateNormal( 2025-07-17T07:11:36.2149397Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-07-17T07:11:36.2149484Z ... ) 2025-07-17T07:11:36.2149775Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-07-17T07:11:36.2149867Z tensor([-0.2102, -0.5429]) 2025-07-17T07:11:36.2149951Z 2025-07-17T07:11:36.2150031Z Args: 2025-07-17T07:11:36.2150262Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-07-17T07:11:36.2150527Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-07-17T07:11:36.2150645Z `batch_shape + event_shape + (rank,)` 2025-07-17T07:11:36.2150908Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-07-17T07:11:36.2151012Z `batch_shape + event_shape` 2025-07-17T07:11:36.2151085Z 2025-07-17T07:11:36.2151171Z Note: 2025-07-17T07:11:36.2151436Z The computation for determinant and inverse of covariance matrix is avoided when 2025-07-17T07:11:36.2151669Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-07-17T07:11:36.2151884Z `_ and 2025-07-17T07:11:36.2152172Z `matrix determinant lemma `_. 2025-07-17T07:11:36.2152424Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-07-17T07:11:36.2152542Z the small size "capacitance" matrix:: 2025-07-17T07:11:36.2152618Z 2025-07-17T07:11:36.2152800Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-07-17T07:11:36.2152904Z 2025-07-17T07:11:36.2153150Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2153231Z 2025-07-17T07:11:36.2153327Z warnings.warn(msg) 2025-07-17T07:11:36.2153400Z 2025-07-17T07:11:36.2153588Z --- Parse Warning: 93 / 136 --- 2025-07-17T07:11:36.2154610Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=15. 2025-07-17T07:11:36.2154874Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2154949Z 2025-07-17T07:11:36.2155170Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-07-17T07:11:36.2155413Z distribution where all component are from different parameterizations of 2025-07-17T07:11:36.2155652Z the same distribution type. It is parameterized by a `Categorical` 2025-07-17T07:11:36.2155847Z "selecting distribution" (over `k` component) and a component 2025-07-17T07:11:36.2156097Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-07-17T07:11:36.2156252Z (equal to `[k]`) which indexes each (batch of) component. 2025-07-17T07:11:36.2156333Z 2025-07-17T07:11:36.2156417Z Examples:: 2025-07-17T07:11:36.2156491Z 2025-07-17T07:11:36.2156610Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.2156809Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-07-17T07:11:36.2156917Z >>> # weighted normal distributions 2025-07-17T07:11:36.2157035Z >>> mix = D.Categorical(torch.ones(5,)) 2025-07-17T07:11:36.2157173Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-07-17T07:11:36.2157286Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T07:11:36.2157370Z 2025-07-17T07:11:36.2157570Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-07-17T07:11:36.2157701Z >>> # weighted bivariate normal distributions 2025-07-17T07:11:36.2157817Z >>> mix = D.Categorical(torch.ones(5,)) 2025-07-17T07:11:36.2157925Z >>> comp = D.Independent(D.Normal( 2025-07-17T07:11:36.2158054Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-07-17T07:11:36.2158170Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T07:11:36.2158244Z 2025-07-17T07:11:36.2158426Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-07-17T07:11:36.2158623Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-07-17T07:11:36.2158736Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-07-17T07:11:36.2158847Z >>> comp = D.Independent(D.Normal( 2025-07-17T07:11:36.2158974Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-07-17T07:11:36.2159093Z >>> gmm = MixtureSameFamily(mix, comp) 2025-07-17T07:11:36.2159170Z 2025-07-17T07:11:36.2159247Z Args: 2025-07-17T07:11:36.2159452Z mixture_distribution: `torch.distributions.Categorical`-like 2025-07-17T07:11:36.2159640Z instance. Manages the probability of selecting component. 2025-07-17T07:11:36.2159799Z The number of categories must match the rightmost batch 2025-07-17T07:11:36.2159991Z dimension of the `component_distribution`. Must have either 2025-07-17T07:11:36.2160130Z scalar `batch_shape` or `batch_shape` matching 2025-07-17T07:11:36.2160259Z `component_distribution.batch_shape[:-1]` 2025-07-17T07:11:36.2160481Z component_distribution: `torch.distributions.Distribution`-like 2025-07-17T07:11:36.2160654Z instance. Right-most batch dimension indexes component. 2025-07-17T07:11:36.2160734Z 2025-07-17T07:11:36.2160984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2161100Z 2025-07-17T07:11:36.2161202Z warnings.warn(msg) 2025-07-17T07:11:36.2161277Z 2025-07-17T07:11:36.2161453Z --- Parse Warning: 94 / 136 --- 2025-07-17T07:11:36.2162474Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=120. 2025-07-17T07:11:36.2162734Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2162816Z 2025-07-17T07:11:36.2162995Z Creates a RelaxedBernoulli distribution, parametrized by 2025-07-17T07:11:36.2163180Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-07-17T07:11:36.2163397Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-07-17T07:11:36.2163573Z so the values are in (0, 1), and has reparametrizable samples. 2025-07-17T07:11:36.2163678Z 2025-07-17T07:11:36.2163771Z Example:: 2025-07-17T07:11:36.2163845Z 2025-07-17T07:11:36.2163978Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:36.2164157Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-07-17T07:11:36.2164280Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-07-17T07:11:36.2164372Z >>> m.sample() 2025-07-17T07:11:36.2164485Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-07-17T07:11:36.2164558Z 2025-07-17T07:11:36.2164644Z Args: 2025-07-17T07:11:36.2164778Z temperature (Tensor): relaxation temperature 2025-07-17T07:11:36.2164942Z probs (Number, Tensor): the probability of sampling `1` 2025-07-17T07:11:36.2165106Z logits (Number, Tensor): the log-odds of sampling `1` 2025-07-17T07:11:36.2165181Z 2025-07-17T07:11:36.2165427Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2165513Z 2025-07-17T07:11:36.2165608Z warnings.warn(msg) 2025-07-17T07:11:36.2165684Z 2025-07-17T07:11:36.2165864Z --- Parse Warning: 95 / 136 --- 2025-07-17T07:11:36.2166914Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=109. 2025-07-17T07:11:36.2167180Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2167255Z 2025-07-17T07:11:36.2167460Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-07-17T07:11:36.2167657Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-07-17T07:11:36.2167889Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-07-17T07:11:36.2168052Z its samples are on simplex, and are reparametrizable. 2025-07-17T07:11:36.2168127Z 2025-07-17T07:11:36.2168210Z Example:: 2025-07-17T07:11:36.2168290Z 2025-07-17T07:11:36.2168423Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:36.2168575Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-07-17T07:11:36.2168711Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-07-17T07:11:36.2168796Z >>> m.sample() 2025-07-17T07:11:36.2168906Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-07-17T07:11:36.2168989Z 2025-07-17T07:11:36.2169066Z Args: 2025-07-17T07:11:36.2169208Z temperature (Tensor): relaxation temperature 2025-07-17T07:11:36.2169322Z probs (Tensor): event probabilities 2025-07-17T07:11:36.2169503Z logits (Tensor): unnormalized log probability for each event 2025-07-17T07:11:36.2169583Z 2025-07-17T07:11:36.2169829Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2169906Z 2025-07-17T07:11:36.2170007Z warnings.warn(msg) 2025-07-17T07:11:36.2170106Z 2025-07-17T07:11:36.2170279Z --- Parse Warning: 96 / 136 --- 2025-07-17T07:11:36.2171313Z /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. 2025-07-17T07:11:36.2171568Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2171916Z Return a new dict with new, potentially nested, key value pair 2025-07-17T07:11:36.2171991Z 2025-07-17T07:11:36.2172077Z >>> purchase = { 2025-07-17T07:11:36.2172180Z ... "name": "Alice", 2025-07-17T07:11:36.2172347Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T07:11:36.2172459Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T07:11:36.2172603Z ... } 2025-07-17T07:11:36.2172805Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-07-17T07:11:36.2172910Z {'credit card': '5555-1234-1234-1234', 2025-07-17T07:11:36.2173009Z 'name': 'Alice', 2025-07-17T07:11:36.2173233Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-07-17T07:11:36.2173319Z 2025-07-17T07:11:36.2173567Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2173645Z 2025-07-17T07:11:36.2173746Z warnings.warn(msg) 2025-07-17T07:11:36.2173820Z 2025-07-17T07:11:36.2174003Z --- Parse Warning: 97 / 136 --- 2025-07-17T07:11:36.2175042Z /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. 2025-07-17T07:11:36.2175412Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2175574Z Update value in a (potentially) nested dictionary 2025-07-17T07:11:36.2175649Z 2025-07-17T07:11:36.2175727Z inputs: 2025-07-17T07:11:36.2175848Z d - dictionary on which to operate 2025-07-17T07:11:36.2176062Z keys - list or tuple giving the location of the value to be changed in d 2025-07-17T07:11:36.2176184Z func - function to operate on that value 2025-07-17T07:11:36.2176264Z 2025-07-17T07:11:36.2176449Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-07-17T07:11:36.2176677Z original dictionary with v replaced by func(v), but does not mutate the 2025-07-17T07:11:36.2176781Z original dictionary. 2025-07-17T07:11:36.2176856Z 2025-07-17T07:11:36.2177062Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-07-17T07:11:36.2177272Z specified by the keys, with the innermost value set to func(default). 2025-07-17T07:11:36.2177352Z 2025-07-17T07:11:36.2177451Z >>> inc = lambda x: x + 1 2025-07-17T07:11:36.2177553Z >>> update_in({"a": 0}, ["a"], inc) 2025-07-17T07:11:36.2177638Z {'a': 1} 2025-07-17T07:11:36.2177722Z 2025-07-17T07:11:36.2177817Z >>> transaction = { 2025-07-17T07:11:36.2177912Z ... "name": "Alice", 2025-07-17T07:11:36.2178110Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T07:11:36.2178222Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T07:11:36.2178308Z ... } 2025-07-17T07:11:36.2178522Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-07-17T07:11:36.2178629Z {'credit card': '5555-1234-1234-1234', 2025-07-17T07:11:36.2178722Z 'name': 'Alice', 2025-07-17T07:11:36.2178883Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-07-17T07:11:36.2178962Z 2025-07-17T07:11:36.2179094Z >>> # updating a value when k0 is not in d 2025-07-17T07:11:36.2179265Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-07-17T07:11:36.2179347Z {1: {2: {3: 'bar'}}} 2025-07-17T07:11:36.2179475Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-07-17T07:11:36.2179568Z {1: 'foo', 2: {3: {4: 1}}} 2025-07-17T07:11:36.2179654Z 2025-07-17T07:11:36.2179905Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2179984Z 2025-07-17T07:11:36.2180087Z warnings.warn(msg) 2025-07-17T07:11:36.2180162Z 2025-07-17T07:11:36.2180346Z --- Parse Warning: 98 / 136 --- 2025-07-17T07:11:36.2181370Z /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. 2025-07-17T07:11:36.2181661Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2181834Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-07-17T07:11:36.2181913Z 2025-07-17T07:11:36.2182140Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-07-17T07:11:36.2182355Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-07-17T07:11:36.2182434Z 2025-07-17T07:11:36.2182635Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-07-17T07:11:36.2182772Z structures such as dictionaries and lists. 2025-07-17T07:11:36.2182851Z 2025-07-17T07:11:36.2182942Z >>> transaction = { 2025-07-17T07:11:36.2183040Z ... "name": "Alice", 2025-07-17T07:11:36.2183226Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-07-17T07:11:36.2183349Z ... "credit card": "5555-1234-1234-1234", 2025-07-17T07:11:36.2183433Z ... } 2025-07-17T07:11:36.2183563Z >>> get_in(["purchase", "items", 0], transaction) 2025-07-17T07:11:36.2183655Z 'Apple' 2025-07-17T07:11:36.2183759Z >>> get_in(["name"], transaction) 2025-07-17T07:11:36.2183833Z 'Alice' 2025-07-17T07:11:36.2183966Z >>> get_in(["purchase", "total"], transaction) 2025-07-17T07:11:36.2184110Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-07-17T07:11:36.2184238Z >>> get_in(["purchase", "items", 10], transaction) 2025-07-17T07:11:36.2184377Z >>> get_in(["purchase", "total"], transaction, 0) 2025-07-17T07:11:36.2184450Z 0 2025-07-17T07:11:36.2184553Z >>> get_in(["y"], {}, no_default=True) 2025-07-17T07:11:36.2184672Z Traceback (most recent call last): 2025-07-17T07:11:36.2184749Z ... 2025-07-17T07:11:36.2184843Z KeyError: 'y' 2025-07-17T07:11:36.2184920Z 2025-07-17T07:11:36.2184998Z See Also: 2025-07-17T07:11:36.2185101Z itertoolz.get 2025-07-17T07:11:36.2185195Z operator.getitem 2025-07-17T07:11:36.2185269Z 2025-07-17T07:11:36.2185526Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2185597Z 2025-07-17T07:11:36.2185692Z warnings.warn(msg) 2025-07-17T07:11:36.2185777Z 2025-07-17T07:11:36.2185952Z --- Parse Warning: 99 / 136 --- 2025-07-17T07:11:36.2186991Z /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. 2025-07-17T07:11:36.2187254Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2187361Z Group a collection by a key function 2025-07-17T07:11:36.2187446Z 2025-07-17T07:11:36.2187607Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-07-17T07:11:36.2187725Z >>> groupby(len, names) # doctest: +SKIP 2025-07-17T07:11:36.2187922Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-07-17T07:11:36.2187994Z 2025-07-17T07:11:36.2188096Z >>> iseven = lambda x: x % 2 == 0 2025-07-17T07:11:36.2188272Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-07-17T07:11:36.2188383Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-07-17T07:11:36.2188469Z 2025-07-17T07:11:36.2188603Z Non-callable keys imply grouping on a member. 2025-07-17T07:11:36.2188678Z 2025-07-17T07:11:36.2188771Z >>> groupby( 2025-07-17T07:11:36.2188853Z ... "gender", 2025-07-17T07:11:36.2188930Z ... [ 2025-07-17T07:11:36.2189054Z ... {"name": "Alice", "gender": "F"}, 2025-07-17T07:11:36.2189162Z ... {"name": "Bob", "gender": "M"}, 2025-07-17T07:11:36.2189280Z ... {"name": "Charlie", "gender": "M"}, 2025-07-17T07:11:36.2189414Z ... ], 2025-07-17T07:11:36.2189508Z ... ) # doctest:+SKIP 2025-07-17T07:11:36.2189617Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-07-17T07:11:36.2189726Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-07-17T07:11:36.2189892Z {'gender': 'M', 'name': 'Charlie'}]} 2025-07-17T07:11:36.2189978Z 2025-07-17T07:11:36.2190115Z Not to be confused with ``itertools.groupby`` 2025-07-17T07:11:36.2190191Z 2025-07-17T07:11:36.2190278Z See Also: 2025-07-17T07:11:36.2190355Z countby 2025-07-17T07:11:36.2190431Z 2025-07-17T07:11:36.2190688Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2190763Z 2025-07-17T07:11:36.2190857Z warnings.warn(msg) 2025-07-17T07:11:36.2190940Z 2025-07-17T07:11:36.2191124Z --- Parse Warning: 100 / 136 --- 2025-07-17T07:11:36.2191992Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py line=142. 2025-07-17T07:11:36.2192252Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2192484Z Return the recommended gain value for the given nonlinearity function. 2025-07-17T07:11:36.2192572Z 2025-07-17T07:11:36.2192670Z The values are as follows: 2025-07-17T07:11:36.2192745Z 2025-07-17T07:11:36.2192866Z ================= ==================================================== 2025-07-17T07:11:36.2192963Z nonlinearity gain 2025-07-17T07:11:36.2193072Z ================= ==================================================== 2025-07-17T07:11:36.2193180Z Linear / Identity :math:`1` 2025-07-17T07:11:36.2193272Z Conv{1,2,3}D :math:`1` 2025-07-17T07:11:36.2193372Z Sigmoid :math:`1` 2025-07-17T07:11:36.2193475Z Tanh :math:`\frac{5}{3}` 2025-07-17T07:11:36.2193574Z ReLU :math:`\sqrt{2}` 2025-07-17T07:11:36.2193773Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-07-17T07:11:36.2193875Z SELU :math:`\frac{3}{4}` 2025-07-17T07:11:36.2193987Z ================= ==================================================== 2025-07-17T07:11:36.2194067Z 2025-07-17T07:11:36.2194159Z .. warning:: 2025-07-17T07:11:36.2194341Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-07-17T07:11:36.2194584Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-07-17T07:11:36.2194748Z This gives the initial weights a variance of ``1 / N``, 2025-07-17T07:11:36.2194978Z which is necessary to induce a stable fixed point in the forward pass. 2025-07-17T07:11:36.2195193Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-07-17T07:11:36.2195375Z effect for more stable gradient flow in rectangular layers. 2025-07-17T07:11:36.2195484Z 2025-07-17T07:11:36.2195562Z Args: 2025-07-17T07:11:36.2195755Z nonlinearity: the non-linear function (`nn.functional` name) 2025-07-17T07:11:36.2195931Z param: optional parameter for the non-linear function 2025-07-17T07:11:36.2196011Z 2025-07-17T07:11:36.2196098Z Examples: 2025-07-17T07:11:36.2196207Z >>> gain = nn.init.calculate_gain( 2025-07-17T07:11:36.2196305Z ... "leaky_relu", 0.2 2025-07-17T07:11:36.2196435Z ... ) # leaky_relu with negative_slope=0.2 2025-07-17T07:11:36.2196512Z 2025-07-17T07:11:36.2196992Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-07-17T07:11:36.2197077Z 2025-07-17T07:11:36.2197326Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2197428Z 2025-07-17T07:11:36.2197530Z warnings.warn(msg) 2025-07-17T07:11:36.2197604Z 2025-07-17T07:11:36.2197793Z --- Parse Warning: 101 / 136 --- 2025-07-17T07:11:36.2199110Z /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. 2025-07-17T07:11:36.2199370Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2199554Z Applies Batch Normalization over a N-Dimensional input. 2025-07-17T07:11:36.2199631Z 2025-07-17T07:11:36.2199973Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-07-17T07:11:36.2200207Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-07-17T07:11:36.2200409Z Internal Covariate Shift `__ . 2025-07-17T07:11:36.2200499Z 2025-07-17T07:11:36.2200585Z .. math:: 2025-07-17T07:11:36.2200658Z 2025-07-17T07:11:36.2200882Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-07-17T07:11:36.2200960Z 2025-07-17T07:11:36.2201188Z The mean and standard-deviation are calculated per-dimension over all 2025-07-17T07:11:36.2201423Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-07-17T07:11:36.2201663Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-07-17T07:11:36.2201844Z By default, the elements of :math:`\gamma` are sampled from 2025-07-17T07:11:36.2202040Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-07-17T07:11:36.2202300Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-07-17T07:11:36.2202424Z `torch.var(input, unbiased=False)`. 2025-07-17T07:11:36.2202501Z 2025-07-17T07:11:36.2202736Z Also by default, during training this layer keeps running estimates of its 2025-07-17T07:11:36.2202973Z computed mean and variance, which are then used for normalization during 2025-07-17T07:11:36.2203215Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-07-17T07:11:36.2203294Z of 0.1. 2025-07-17T07:11:36.2203380Z 2025-07-17T07:11:36.2203600Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-07-17T07:11:36.2203821Z keep running estimates, and batch statistics are instead used during 2025-07-17T07:11:36.2203920Z evaluation time as well. 2025-07-17T07:11:36.2203993Z 2025-07-17T07:11:36.2204087Z .. note:: 2025-07-17T07:11:36.2204306Z This :attr:`momentum` argument is different from one used in optimizer 2025-07-17T07:11:36.2204526Z classes and the conventional notion of momentum. Mathematically, the 2025-07-17T07:11:36.2204666Z update rule for running statistics here is 2025-07-17T07:11:36.2204957Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-07-17T07:11:36.2205172Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-07-17T07:11:36.2205267Z new observed value. 2025-07-17T07:11:36.2205343Z 2025-07-17T07:11:36.2205648Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-07-17T07:11:36.2205894Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-07-17T07:11:36.2206068Z Normalization or Spatio-temporal Batch Normalization. 2025-07-17T07:11:36.2206149Z 2025-07-17T07:11:36.2206290Z Currently :class:`SyncBatchNorm` only supports 2025-07-17T07:11:36.2206569Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-07-17T07:11:36.2206809Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-07-17T07:11:36.2207014Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-07-17T07:11:36.2207114Z Network with DDP. 2025-07-17T07:11:36.2207189Z 2025-07-17T07:11:36.2207314Z Args: 2025-07-17T07:11:36.2207483Z num_features: :math:`C` from an expected input of size 2025-07-17T07:11:36.2207575Z :math:`(N, C, +)` 2025-07-17T07:11:36.2207761Z eps: a value added to the denominator for numerical stability. 2025-07-17T07:11:36.2207862Z Default: ``1e-5`` 2025-07-17T07:11:36.2208053Z momentum: the value used for the running_mean and running_var 2025-07-17T07:11:36.2208268Z computation. Can be set to ``None`` for cumulative moving average 2025-07-17T07:11:36.2208385Z (i.e. simple average). Default: 0.1 2025-07-17T07:11:36.2208585Z affine: a boolean value that when set to ``True``, this module has 2025-07-17T07:11:36.2208745Z learnable affine parameters. Default: ``True`` 2025-07-17T07:11:36.2208955Z track_running_stats: a boolean value that when set to ``True``, this 2025-07-17T07:11:36.2209186Z module tracks the running mean and variance, and when set to ``False``, 2025-07-17T07:11:36.2209417Z this module does not track such statistics, and initializes statistics 2025-07-17T07:11:36.2209613Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-07-17T07:11:36.2209846Z When these buffers are ``None``, this module always uses batch statistics. 2025-07-17T07:11:36.2209996Z in both training and eval modes. Default: ``True`` 2025-07-17T07:11:36.2210242Z process_group: synchronization of stats happen within each process group 2025-07-17T07:11:36.2210479Z individually. Default behavior is synchronization across the whole 2025-07-17T07:11:36.2210565Z world 2025-07-17T07:11:36.2210644Z 2025-07-17T07:11:36.2210738Z Shape: 2025-07-17T07:11:36.2210845Z - Input: :math:`(N, C, +)` 2025-07-17T07:11:36.2210988Z - Output: :math:`(N, C, +)` (same shape as input) 2025-07-17T07:11:36.2211075Z 2025-07-17T07:11:36.2211160Z .. note:: 2025-07-17T07:11:36.2211414Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-07-17T07:11:36.2211613Z synchronization is disabled when ``model.eval()`` is set or if 2025-07-17T07:11:36.2211738Z ``self.training`` is otherwise ``False``. 2025-07-17T07:11:36.2211822Z 2025-07-17T07:11:36.2211907Z Examples:: 2025-07-17T07:11:36.2211988Z 2025-07-17T07:11:36.2212101Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2212209Z >>> # With Learnable Parameters 2025-07-17T07:11:36.2212317Z >>> m = nn.SyncBatchNorm(100) 2025-07-17T07:11:36.2212444Z >>> # creating process group (optional) 2025-07-17T07:11:36.2212608Z >>> # ranks is a list of int identifying rank ids. 2025-07-17T07:11:36.2212717Z >>> ranks = list(range(8)) 2025-07-17T07:11:36.2212816Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-07-17T07:11:36.2212962Z >>> # Note: every rank calls into new_group for every 2025-07-17T07:11:36.2213116Z >>> # process group created, even if that rank is not 2025-07-17T07:11:36.2213216Z >>> # part of the group. 2025-07-17T07:11:36.2213459Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-07-17T07:11:36.2213664Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-07-17T07:11:36.2213782Z >>> # Without Learnable Parameters 2025-07-17T07:11:36.2213998Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-07-17T07:11:36.2214119Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-07-17T07:11:36.2214240Z >>> output = m(input) 2025-07-17T07:11:36.2214327Z 2025-07-17T07:11:36.2214437Z >>> # network is nn.BatchNorm layer 2025-07-17T07:11:36.2214754Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-07-17T07:11:36.2214923Z >>> # only single gpu per process is currently supported 2025-07-17T07:11:36.2215136Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-07-17T07:11:36.2215328Z >>> sync_bn_network, 2025-07-17T07:11:36.2215458Z >>> device_ids=[args.local_rank], 2025-07-17T07:11:36.2215584Z >>> output_device=args.local_rank) 2025-07-17T07:11:36.2215669Z 2025-07-17T07:11:36.2215917Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2215993Z 2025-07-17T07:11:36.2216091Z warnings.warn(msg) 2025-07-17T07:11:36.2216170Z 2025-07-17T07:11:36.2216372Z --- Parse Warning: 102 / 136 --- 2025-07-17T07:11:36.2217412Z /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. 2025-07-17T07:11:36.2217676Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2217993Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-07-17T07:11:36.2218069Z 2025-07-17T07:11:36.2218149Z Args: 2025-07-17T07:11:36.2218399Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-07-17T07:11:36.2218611Z process_group (optional): process group to scope synchronization, 2025-07-17T07:11:36.2218720Z default is the whole world 2025-07-17T07:11:36.2218805Z 2025-07-17T07:11:36.2218890Z Returns: 2025-07-17T07:11:36.2219145Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-07-17T07:11:36.2219358Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-07-17T07:11:36.2219564Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-07-17T07:11:36.2219656Z instead. 2025-07-17T07:11:36.2219731Z 2025-07-17T07:11:36.2219821Z Example:: 2025-07-17T07:11:36.2219905Z 2025-07-17T07:11:36.2220023Z >>> # Network with nn.BatchNorm layer 2025-07-17T07:11:36.2220160Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:36.2220283Z >>> module = torch.nn.Sequential( 2025-07-17T07:11:36.2220397Z >>> torch.nn.Linear(20, 100), 2025-07-17T07:11:36.2220519Z >>> torch.nn.BatchNorm1d(100), 2025-07-17T07:11:36.2220640Z >>> ).cuda() 2025-07-17T07:11:36.2220760Z >>> # creating process group (optional) 2025-07-17T07:11:36.2220905Z >>> # ranks is a list of int identifying rank ids. 2025-07-17T07:11:36.2221009Z >>> ranks = list(range(8)) 2025-07-17T07:11:36.2221113Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-07-17T07:11:36.2221263Z >>> # Note: every rank calls into new_group for every 2025-07-17T07:11:36.2221409Z >>> # process group created, even if that rank is not 2025-07-17T07:11:36.2221505Z >>> # part of the group. 2025-07-17T07:11:36.2221628Z >>> # xdoctest: +SKIP("distributed") 2025-07-17T07:11:36.2221876Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-07-17T07:11:36.2222083Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-07-17T07:11:36.2222401Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-07-17T07:11:36.2222481Z 2025-07-17T07:11:36.2222565Z 2025-07-17T07:11:36.2222871Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2222948Z 2025-07-17T07:11:36.2223051Z warnings.warn(msg) 2025-07-17T07:11:36.2223124Z 2025-07-17T07:11:36.2223310Z --- Parse Warning: 103 / 136 --- 2025-07-17T07:11:36.2224204Z /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. 2025-07-17T07:11:36.2224462Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2224547Z 2025-07-17T07:11:36.2224854Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-07-17T07:11:36.2224934Z 2025-07-17T07:11:36.2225209Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-07-17T07:11:36.2225428Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-07-17T07:11:36.2225517Z 2025-07-17T07:11:36.2225825Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-07-17T07:11:36.2226070Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-07-17T07:11:36.2226241Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-07-17T07:11:36.2226311Z 2025-07-17T07:11:36.2226390Z Shape: 2025-07-17T07:11:36.2226607Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-07-17T07:11:36.2226850Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-07-17T07:11:36.2227073Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-07-17T07:11:36.2227197Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-07-17T07:11:36.2227268Z 2025-07-17T07:11:36.2227357Z Args: 2025-07-17T07:11:36.2227505Z dim (Union[int, str]): Dimension to be unflattened 2025-07-17T07:11:36.2227850Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-07-17T07:11:36.2227934Z 2025-07-17T07:11:36.2228013Z Examples: 2025-07-17T07:11:36.2228116Z >>> input = torch.randn(2, 50) 2025-07-17T07:11:36.2228218Z >>> # With tuple of ints 2025-07-17T07:11:36.2228310Z >>> m = nn.Sequential( 2025-07-17T07:11:36.2228400Z >>> nn.Linear(50, 50), 2025-07-17T07:11:36.2228514Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-07-17T07:11:36.2228589Z >>> ) 2025-07-17T07:11:36.2228691Z >>> output = m(input) 2025-07-17T07:11:36.2228778Z >>> output.size() 2025-07-17T07:11:36.2228875Z torch.Size([2, 2, 5, 5]) 2025-07-17T07:11:36.2228999Z >>> # With torch.Size 2025-07-17T07:11:36.2229092Z >>> m = nn.Sequential( 2025-07-17T07:11:36.2229181Z >>> nn.Linear(50, 50), 2025-07-17T07:11:36.2229311Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-07-17T07:11:36.2229391Z >>> ) 2025-07-17T07:11:36.2229483Z >>> output = m(input) 2025-07-17T07:11:36.2229583Z >>> output.size() 2025-07-17T07:11:36.2229675Z torch.Size([2, 2, 5, 5]) 2025-07-17T07:11:36.2229793Z >>> # With namedshape (tuple of tuples) 2025-07-17T07:11:36.2229945Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-07-17T07:11:36.2230144Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-07-17T07:11:36.2230254Z >>> output = unflatten(input) 2025-07-17T07:11:36.2230343Z >>> output.size() 2025-07-17T07:11:36.2230434Z torch.Size([2, 2, 5, 5]) 2025-07-17T07:11:36.2230546Z 2025-07-17T07:11:36.2230801Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2230874Z 2025-07-17T07:11:36.2230974Z warnings.warn(msg) 2025-07-17T07:11:36.2231048Z 2025-07-17T07:11:36.2231279Z --- Parse Warning: 104 / 136 --- 2025-07-17T07:11:36.2232272Z /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=1718. 2025-07-17T07:11:36.2232527Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2232730Z Creates a criterion that measures the triplet loss given input 2025-07-17T07:11:36.2232920Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-07-17T07:11:36.2233128Z positive, and negative examples, respectively), and a nonnegative, 2025-07-17T07:11:36.2233380Z real-valued function ("distance function") used to compute the relationship 2025-07-17T07:11:36.2233597Z between the anchor and positive example ("positive distance") and the 2025-07-17T07:11:36.2233757Z anchor and negative example ("negative distance"). 2025-07-17T07:11:36.2233829Z 2025-07-17T07:11:36.2234030Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-07-17T07:11:36.2234132Z can be described as: 2025-07-17T07:11:36.2234207Z 2025-07-17T07:11:36.2234290Z .. math:: 2025-07-17T07:11:36.2234434Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-07-17T07:11:36.2234582Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-07-17T07:11:36.2234659Z 2025-07-17T07:11:36.2234911Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-07-17T07:11:36.2235199Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-07-17T07:11:36.2235445Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-07-17T07:11:36.2235685Z between the positive and negative distances that is required for the loss to 2025-07-17T07:11:36.2235910Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-07-17T07:11:36.2236035Z that the distance function can handle. 2025-07-17T07:11:36.2236108Z 2025-07-17T07:11:36.2236216Z If :attr:`reduction` is not ``'none'`` 2025-07-17T07:11:36.2236319Z (default ``'mean'``), then: 2025-07-17T07:11:36.2236392Z 2025-07-17T07:11:36.2236471Z .. math:: 2025-07-17T07:11:36.2236562Z \ell(x, y) = 2025-07-17T07:11:36.2236648Z \begin{cases} 2025-07-17T07:11:36.2236853Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-07-17T07:11:36.2237036Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-07-17T07:11:36.2237147Z \end{cases} 2025-07-17T07:11:36.2237228Z 2025-07-17T07:11:36.2237461Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-07-17T07:11:36.2237713Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-07-17T07:11:36.2237800Z 2025-07-17T07:11:36.2237882Z Args: 2025-07-17T07:11:36.2238155Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-07-17T07:11:36.2238356Z quantifies the closeness of two tensors. If not specified, 2025-07-17T07:11:36.2238526Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-07-17T07:11:36.2238805Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-07-17T07:11:36.2239075Z between the positive and negative distances required for the loss to be 0. Larger 2025-07-17T07:11:36.2239357Z margins penalize cases where the negative examples are not distant enough from the 2025-07-17T07:11:36.2239570Z anchors, relative to the positives. Default: :math:`1`. 2025-07-17T07:11:36.2239861Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-07-17T07:11:36.2240132Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-07-17T07:11:36.2240364Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-07-17T07:11:36.2240628Z negative example than the anchor is, swaps the positive example and the anchor in 2025-07-17T07:11:36.2240770Z the loss computation. Default: ``False``. 2025-07-17T07:11:36.2241045Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-07-17T07:11:36.2241232Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-07-17T07:11:36.2241425Z ``'mean'``: the sum of the output will be divided by the number of 2025-07-17T07:11:36.2241662Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-07-17T07:11:36.2241755Z 2025-07-17T07:11:36.2241836Z 2025-07-17T07:11:36.2241918Z Shape: 2025-07-17T07:11:36.2242170Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-07-17T07:11:36.2242296Z as supported by the distance function. 2025-07-17T07:11:36.2242544Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-07-17T07:11:36.2242643Z otherwise. 2025-07-17T07:11:36.2242720Z 2025-07-17T07:11:36.2242804Z Examples: 2025-07-17T07:11:36.2242893Z 2025-07-17T07:11:36.2242993Z >>> # Initialize embeddings 2025-07-17T07:11:36.2243122Z >>> embedding = nn.Embedding(1000, 128) 2025-07-17T07:11:36.2243249Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-07-17T07:11:36.2243382Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-07-17T07:11:36.2243521Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-07-17T07:11:36.2243635Z >>> anchor = embedding(anchor_ids) 2025-07-17T07:11:36.2243754Z >>> positive = embedding(positive_ids) 2025-07-17T07:11:36.2243880Z >>> negative = embedding(negative_ids) 2025-07-17T07:11:36.2243958Z >>> 2025-07-17T07:11:36.2244060Z >>> # Built-in Distance Function 2025-07-17T07:11:36.2244166Z >>> triplet_loss = \ 2025-07-17T07:11:36.2244444Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-07-17T07:11:36.2244604Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T07:11:36.2244700Z >>> output.backward() 2025-07-17T07:11:36.2244780Z >>> 2025-07-17T07:11:36.2244892Z >>> # Custom Distance Function 2025-07-17T07:11:36.2244993Z >>> def l_infinity(x1, x2): 2025-07-17T07:11:36.2245170Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-07-17T07:11:36.2245259Z >>> 2025-07-17T07:11:36.2245445Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-07-17T07:11:36.2245543Z >>> triplet_loss = ( 2025-07-17T07:11:36.2245829Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-07-17T07:11:36.2245977Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T07:11:36.2246083Z >>> output.backward() 2025-07-17T07:11:36.2246160Z >>> 2025-07-17T07:11:36.2246276Z >>> # Custom Distance Function (Lambda) 2025-07-17T07:11:36.2246383Z >>> triplet_loss = ( 2025-07-17T07:11:36.2246511Z >>> nn.TripletMarginWithDistanceLoss( 2025-07-17T07:11:36.2246723Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-07-17T07:11:36.2246882Z >>> output = triplet_loss(anchor, positive, negative) 2025-07-17T07:11:36.2247008Z >>> output.backward() 2025-07-17T07:11:36.2247085Z 2025-07-17T07:11:36.2247176Z Reference: 2025-07-17T07:11:36.2247529Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-07-17T07:11:36.2247760Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-07-17T07:11:36.2247837Z 2025-07-17T07:11:36.2248091Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-07-17T07:11:36.2248178Z 2025-07-17T07:11:36.2248270Z warnings.warn(msg) 2025-07-17T07:11:36.2248346Z 2025-07-17T07:11:36.2248547Z --- Parse Warning: 105 / 136 --- 2025-07-17T07:11:36.2249417Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1852. 2025-07-17T07:11:36.2249687Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2249831Z The Connectionist Temporal Classification loss. 2025-07-17T07:11:36.2249908Z 2025-07-17T07:11:36.2250303Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-07-17T07:11:36.2250654Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-07-17T07:11:36.2250990Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-07-17T07:11:36.2251300Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-07-17T07:11:36.2251376Z 2025-07-17T07:11:36.2251466Z Args: 2025-07-17T07:11:36.2251629Z blank (int, optional): blank label. Default :math:`0`. 2025-07-17T07:11:36.2251865Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-07-17T07:11:36.2252061Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-07-17T07:11:36.2252267Z ``'mean'``: the output losses will be divided by the target lengths and 2025-07-17T07:11:36.2252512Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-07-17T07:11:36.2252611Z Default: ``'mean'`` 2025-07-17T07:11:36.2252720Z zero_infinity (bool, optional): 2025-07-17T07:11:36.2252921Z Whether to zero infinite losses and the associated gradients. 2025-07-17T07:11:36.2253010Z Default: ``False`` 2025-07-17T07:11:36.2253192Z Infinite losses mainly occur when the inputs are too short 2025-07-17T07:11:36.2253310Z to be aligned to the targets. 2025-07-17T07:11:36.2253384Z 2025-07-17T07:11:36.2253466Z Shape: 2025-07-17T07:11:36.2253665Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-07-17T07:11:36.2253815Z where :math:`T = \text{input length}`, 2025-07-17T07:11:36.2253927Z :math:`N = \text{batch size}`, and 2025-07-17T07:11:36.2254094Z :math:`C = \text{number of classes (including blank)}`. 2025-07-17T07:11:36.2254317Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-07-17T07:11:36.2254459Z :func:`torch.nn.functional.log_softmax`). 2025-07-17T07:11:36.2254584Z - Targets: Tensor of size :math:`(N, S)` or 2025-07-17T07:11:36.2254738Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-07-17T07:11:36.2254872Z where :math:`N = \text{batch size}` and 2025-07-17T07:11:36.2255029Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-07-17T07:11:36.2255313Z It represents the target sequences. Each element in the target 2025-07-17T07:11:36.2255611Z sequence is a class index. And the target index cannot be blank (default=0). 2025-07-17T07:11:36.2255764Z In the :math:`(N, S)` form, targets are padded to the 2025-07-17T07:11:36.2255911Z length of the longest sequence, and stacked. 2025-07-17T07:11:36.2256193Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-07-17T07:11:36.2256326Z the targets are assumed to be un-padded and 2025-07-17T07:11:36.2256457Z concatenated within 1 dimension. 2025-07-17T07:11:36.2256660Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-07-17T07:11:36.2256860Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-07-17T07:11:36.2257070Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-07-17T07:11:36.2257299Z for each sequence to achieve masking under the assumption that sequences 2025-07-17T07:11:36.2257426Z are padded to equal lengths. 2025-07-17T07:11:36.2257633Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-07-17T07:11:36.2257842Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-07-17T07:11:36.2258078Z Lengths are specified for each sequence to achieve masking under the 2025-07-17T07:11:36.2258321Z assumption that sequences are padded to equal lengths. If target shape is 2025-07-17T07:11:36.2258498Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-07-17T07:11:36.2258746Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-07-17T07:11:36.2258920Z each target in a batch. Lengths must each be :math:`\leq S` 2025-07-17T07:11:36.2259171Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-07-17T07:11:36.2259402Z targets, the target_lengths must add up to the total length of the tensor. 2025-07-17T07:11:36.2259590Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-07-17T07:11:36.2259834Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-07-17T07:11:36.2260026Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-07-17T07:11:36.2260112Z 2025-07-17T07:11:36.2260194Z Examples: 2025-07-17T07:11:36.2260269Z 2025-07-17T07:11:36.2260385Z >>> # Target are to be padded 2025-07-17T07:11:36.2260496Z >>> T = 50 # Input sequence length 2025-07-17T07:11:36.2260627Z >>> C = 20 # Number of classes (including blank) 2025-07-17T07:11:36.2260730Z >>> N = 16 # Batch size 2025-07-17T07:11:36.2260954Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-07-17T07:11:36.2261135Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-07-17T07:11:36.2261255Z >>> 2025-07-17T07:11:36.2261450Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-07-17T07:11:36.2261667Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-07-17T07:11:36.2261746Z >>> 2025-07-17T07:11:36.2261935Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T07:11:36.2262155Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-07-17T07:11:36.2262235Z >>> 2025-07-17T07:11:36.2262448Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-07-17T07:11:36.2262573Z >>> target_lengths = torch.randint( 2025-07-17T07:11:36.2262662Z ... low=S_min, 2025-07-17T07:11:36.2262757Z ... high=S, 2025-07-17T07:11:36.2262844Z ... size=(N,), 2025-07-17T07:11:36.2262943Z ... dtype=torch.long, 2025-07-17T07:11:36.2263061Z ... ) 2025-07-17T07:11:36.2263163Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T07:11:36.2263341Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T07:11:36.2263491Z >>> loss.backward() 2025-07-17T07:11:36.2263568Z >>> 2025-07-17T07:11:36.2263645Z >>> 2025-07-17T07:11:36.2263770Z >>> # Target are to be un-padded 2025-07-17T07:11:36.2263874Z >>> T = 50 # Input sequence length 2025-07-17T07:11:36.2264005Z >>> C = 20 # Number of classes (including blank) 2025-07-17T07:11:36.2264110Z >>> N = 16 # Batch size 2025-07-17T07:11:36.2264187Z >>> 2025-07-17T07:11:36.2264398Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-07-17T07:11:36.2264602Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-07-17T07:11:36.2264815Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-07-17T07:11:36.2264909Z >>> 2025-07-17T07:11:36.2265098Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T07:11:36.2265334Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-07-17T07:11:36.2265446Z >>> target = torch.randint( 2025-07-17T07:11:36.2265528Z ... low=1, 2025-07-17T07:11:36.2265623Z ... high=C, 2025-07-17T07:11:36.2265734Z ... size=(sum(target_lengths),), 2025-07-17T07:11:36.2265830Z ... dtype=torch.long, 2025-07-17T07:11:36.2265919Z ... ) 2025-07-17T07:11:36.2266016Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T07:11:36.2266193Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T07:11:36.2266294Z >>> loss.backward() 2025-07-17T07:11:36.2266373Z >>> 2025-07-17T07:11:36.2266453Z >>> 2025-07-17T07:11:36.2266647Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-07-17T07:11:36.2266754Z >>> T = 50 # Input sequence length 2025-07-17T07:11:36.2266896Z >>> C = 20 # Number of classes (including blank) 2025-07-17T07:11:36.2266977Z >>> 2025-07-17T07:11:36.2267163Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-07-17T07:11:36.2267301Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-07-17T07:11:36.2267499Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-07-17T07:11:36.2267646Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-07-17T07:11:36.2267737Z >>> 2025-07-17T07:11:36.2267924Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-07-17T07:11:36.2268153Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-07-17T07:11:36.2268269Z >>> target = torch.randint( 2025-07-17T07:11:36.2268384Z ... low=1, 2025-07-17T07:11:36.2268481Z ... high=C, 2025-07-17T07:11:36.2268591Z ... size=(target_lengths,), 2025-07-17T07:11:36.2268696Z ... dtype=torch.long, 2025-07-17T07:11:36.2268791Z ... ) 2025-07-17T07:11:36.2268891Z >>> ctc_loss = nn.CTCLoss() 2025-07-17T07:11:36.2269070Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-07-17T07:11:36.2269179Z >>> loss.backward() 2025-07-17T07:11:36.2269256Z 2025-07-17T07:11:36.2269342Z Reference: 2025-07-17T07:11:36.2269529Z A. Graves et al.: Connectionist Temporal Classification: 2025-07-17T07:11:36.2269756Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-07-17T07:11:36.2269930Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-07-17T07:11:36.2270010Z 2025-07-17T07:11:36.2270118Z Note: 2025-07-17T07:11:36.2270377Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-07-17T07:11:36.2270632Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-07-17T07:11:36.2270895Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-07-17T07:11:36.2271018Z dtype :attr:`torch.int32`. 2025-07-17T07:11:36.2271094Z 2025-07-17T07:11:36.2271361Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-07-17T07:11:36.2271451Z 2025-07-17T07:11:36.2271528Z 2025-07-17T07:11:36.2271621Z Note: 2025-07-17T07:11:36.2272007Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-07-17T07:11:36.2272262Z may select a nondeterministic algorithm to increase performance. If this is 2025-07-17T07:11:36.2272518Z undesirable, you can try to make the operation deterministic (potentially at 2025-07-17T07:11:36.2272757Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-07-17T07:11:36.2272840Z True``. 2025-07-17T07:11:36.2273054Z Please see the notes on :doc:`/notes/randomness` for background. 2025-07-17T07:11:36.2273135Z 2025-07-17T07:11:36.2273398Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2273476Z 2025-07-17T07:11:36.2273569Z warnings.warn(msg) 2025-07-17T07:11:36.2273659Z 2025-07-17T07:11:36.2273870Z --- Parse Warning: 106 / 136 --- 2025-07-17T07:11:36.2274771Z /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=406. 2025-07-17T07:11:36.2275043Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2275198Z Computes a partial inverse of :class:`MaxPool2d`. 2025-07-17T07:11:36.2275277Z 2025-07-17T07:11:36.2275546Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-07-17T07:11:36.2275619Z 2025-07-17T07:11:36.2275855Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-07-17T07:11:36.2276097Z including the indices of the maximal values and computes a partial inverse 2025-07-17T07:11:36.2276237Z in which all non-maximal values are set to zero. 2025-07-17T07:11:36.2276325Z 2025-07-17T07:11:36.2276404Z Note: 2025-07-17T07:11:36.2276714Z This operation may behave nondeterministically when the input indices has repeat values. 2025-07-17T07:11:36.2277103Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-07-17T07:11:36.2277178Z 2025-07-17T07:11:36.2277418Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-07-17T07:11:36.2277652Z sizes. Hence, the inversion process can get ambiguous. 2025-07-17T07:11:36.2277837Z To accommodate this, you can provide the needed output size 2025-07-17T07:11:36.2278060Z as an additional argument :attr:`output_size` in the forward call. 2025-07-17T07:11:36.2278179Z See the Inputs and Example below. 2025-07-17T07:11:36.2278256Z 2025-07-17T07:11:36.2278347Z Args: 2025-07-17T07:11:36.2278528Z kernel_size (int or tuple): Size of the max pooling window. 2025-07-17T07:11:36.2278714Z stride (int or tuple): Stride of the max pooling window. 2025-07-17T07:11:36.2278849Z It is set to :attr:`kernel_size` by default. 2025-07-17T07:11:36.2279026Z padding (int or tuple): Padding that was added to the input 2025-07-17T07:11:36.2279115Z 2025-07-17T07:11:36.2279198Z Inputs: 2025-07-17T07:11:36.2279352Z - `input`: the input Tensor to invert 2025-07-17T07:11:36.2279570Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-07-17T07:11:36.2279723Z - `output_size` (optional): the targeted output size 2025-07-17T07:11:36.2279856Z 2025-07-17T07:11:36.2279949Z Shape: 2025-07-17T07:11:36.2280137Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-07-17T07:11:36.2280357Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-07-17T07:11:36.2280433Z 2025-07-17T07:11:36.2280524Z .. math:: 2025-07-17T07:11:36.2280807Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-07-17T07:11:36.2280885Z 2025-07-17T07:11:36.2280973Z .. math:: 2025-07-17T07:11:36.2281244Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-07-17T07:11:36.2281325Z 2025-07-17T07:11:36.2281486Z or as given by :attr:`output_size` in the call operator 2025-07-17T07:11:36.2281574Z 2025-07-17T07:11:36.2281659Z Example:: 2025-07-17T07:11:36.2281745Z 2025-07-17T07:11:36.2281908Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-07-17T07:11:36.2282035Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-07-17T07:11:36.2282172Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-07-17T07:11:36.2282277Z [ 5., 6., 7., 8.], 2025-07-17T07:11:36.2282379Z [ 9., 10., 11., 12.], 2025-07-17T07:11:36.2282495Z [13., 14., 15., 16.]]]]) 2025-07-17T07:11:36.2282609Z >>> output, indices = pool(input) 2025-07-17T07:11:36.2282711Z >>> unpool(output, indices) 2025-07-17T07:11:36.2282824Z tensor([[[[ 0., 0., 0., 0.], 2025-07-17T07:11:36.2282923Z [ 0., 6., 0., 8.], 2025-07-17T07:11:36.2285624Z [ 0., 0., 0., 0.], 2025-07-17T07:11:36.2285763Z [ 0., 14., 0., 16.]]]]) 2025-07-17T07:11:36.2285986Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-07-17T07:11:36.2286139Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-07-17T07:11:36.2286250Z [ 6., 7., 8., 9., 10.], 2025-07-17T07:11:36.2286360Z [11., 12., 13., 14., 15.], 2025-07-17T07:11:36.2286479Z [16., 17., 18., 19., 20.]]]]) 2025-07-17T07:11:36.2286597Z >>> output, indices = pool(input) 2025-07-17T07:11:36.2286764Z >>> # This call will not work without specifying output_size 2025-07-17T07:11:36.2286932Z >>> unpool(output, indices, output_size=input.size()) 2025-07-17T07:11:36.2287037Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-07-17T07:11:36.2287179Z [ 0., 7., 0., 9., 0.], 2025-07-17T07:11:36.2287315Z [ 0., 0., 0., 0., 0.], 2025-07-17T07:11:36.2287417Z [ 0., 17., 0., 19., 0.]]]]) 2025-07-17T07:11:36.2287496Z 2025-07-17T07:11:36.2287588Z 2025-07-17T07:11:36.2287669Z 2025-07-17T07:11:36.2287933Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2288008Z 2025-07-17T07:11:36.2288100Z warnings.warn(msg) 2025-07-17T07:11:36.2288186Z 2025-07-17T07:11:36.2288416Z --- Parse Warning: 107 / 136 --- 2025-07-17T07:11:36.2289321Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=272. 2025-07-17T07:11:36.2289627Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2289948Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-07-17T07:11:36.2290037Z 2025-07-17T07:11:36.2290388Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-07-17T07:11:36.2290496Z and with 2D inputs, this class 2025-07-17T07:11:36.2290581Z 2025-07-17T07:11:36.2290885Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-07-17T07:11:36.2291196Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-07-17T07:11:36.2291512Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-07-17T07:11:36.2291588Z 2025-07-17T07:11:36.2291948Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-07-17T07:11:36.2292040Z operations. 2025-07-17T07:11:36.2292117Z 2025-07-17T07:11:36.2292383Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-07-17T07:11:36.2292621Z pass. This scales the output of the Embedding before performing a weighted 2025-07-17T07:11:36.2292868Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-07-17T07:11:36.2293114Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-07-17T07:11:36.2293219Z :attr:`per_sample_weights`. 2025-07-17T07:11:36.2293292Z 2025-07-17T07:11:36.2293383Z Args: 2025-07-17T07:11:36.2293563Z num_embeddings (int): size of the dictionary of embeddings 2025-07-17T07:11:36.2293736Z embedding_dim (int): the size of each embedding vector 2025-07-17T07:11:36.2294047Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-07-17T07:11:36.2294269Z is renormalized to have norm :attr:`max_norm`. 2025-07-17T07:11:36.2294622Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-07-17T07:11:36.2294949Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-07-17T07:11:36.2295123Z the words in the mini-batch. Default ``False``. 2025-07-17T07:11:36.2295400Z Note: this option is not supported when ``mode="max"``. 2025-07-17T07:11:36.2295652Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-07-17T07:11:36.2295884Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-07-17T07:11:36.2296105Z into consideration. ``"mean"`` computes the average of the values 2025-07-17T07:11:36.2296315Z in the bag, ``"max"`` computes the max value over each bag. 2025-07-17T07:11:36.2296442Z Default: ``"mean"`` 2025-07-17T07:11:36.2296764Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-07-17T07:11:36.2297028Z Notes for more details regarding sparse gradients. Note: this option is not 2025-07-17T07:11:36.2297158Z supported when ``mode="max"``. 2025-07-17T07:11:36.2297532Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-07-17T07:11:36.2297762Z is equivalent to the size of `indices`. This matches the CSR format. 2025-07-17T07:11:36.2298123Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-07-17T07:11:36.2298433Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-07-17T07:11:36.2298682Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-07-17T07:11:36.2298954Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-07-17T07:11:36.2299205Z zeros, but can be updated to another value to be used as the padding vector. 2025-07-17T07:11:36.2299452Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-07-17T07:11:36.2299570Z reduction. 2025-07-17T07:11:36.2299647Z 2025-07-17T07:11:36.2299738Z Attributes: 2025-07-17T07:11:36.2300069Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-07-17T07:11:36.2300214Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-07-17T07:11:36.2300293Z 2025-07-17T07:11:36.2300405Z Examples:: 2025-07-17T07:11:36.2300481Z 2025-07-17T07:11:36.2300647Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-07-17T07:11:36.2300811Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-07-17T07:11:36.2300934Z >>> # a batch of 2 samples of 4 indices each 2025-07-17T07:11:36.2301127Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-07-17T07:11:36.2301270Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-07-17T07:11:36.2301408Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-07-17T07:11:36.2301531Z >>> embedding_sum(input, offsets) 2025-07-17T07:11:36.2301638Z tensor([[-0.8861, -5.4350, -0.0523], 2025-07-17T07:11:36.2301737Z [ 1.1306, -2.5798, -1.0044]]) 2025-07-17T07:11:36.2301865Z 2025-07-17T07:11:36.2301977Z >>> # Example with padding_idx 2025-07-17T07:11:36.2302194Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-07-17T07:11:36.2302375Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-07-17T07:11:36.2302517Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-07-17T07:11:36.2302647Z >>> embedding_sum(input, offsets) 2025-07-17T07:11:36.2302746Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-07-17T07:11:36.2302844Z [-0.7082, 3.2145, -2.6251]]) 2025-07-17T07:11:36.2302929Z 2025-07-17T07:11:36.2303097Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-07-17T07:11:36.2303241Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-07-17T07:11:36.2303431Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-07-17T07:11:36.2303533Z embedding.weight, 2025-07-17T07:11:36.2303670Z padding_idx=embedding.padding_idx, 2025-07-17T07:11:36.2303761Z mode='sum') 2025-07-17T07:11:36.2303839Z 2025-07-17T07:11:36.2304100Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2304174Z 2025-07-17T07:11:36.2304268Z warnings.warn(msg) 2025-07-17T07:11:36.2304351Z 2025-07-17T07:11:36.2304555Z --- Parse Warning: 108 / 136 --- 2025-07-17T07:11:36.2305520Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py line=186. 2025-07-17T07:11:36.2305787Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2305965Z Take in and process masked source/target sequences. 2025-07-17T07:11:36.2306050Z 2025-07-17T07:11:36.2306135Z .. note:: 2025-07-17T07:11:36.2306210Z 2025-07-17T07:11:36.2306632Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-07-17T07:11:36.2306778Z not allowed to participate in the attention, 2025-07-17T07:11:36.2306968Z which is the opposite of the definition for :attr:`attn_mask` 2025-07-17T07:11:36.2307162Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-07-17T07:11:36.2307237Z 2025-07-17T07:11:36.2307331Z Args: 2025-07-17T07:11:36.2307465Z src: the sequence to the encoder (required). 2025-07-17T07:11:36.2307598Z tgt: the sequence to the decoder (required). 2025-07-17T07:11:36.2307786Z src_mask: the additive mask for the src sequence (optional). 2025-07-17T07:11:36.2307968Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-07-17T07:11:36.2308172Z memory_mask: the additive mask for the encoder output (optional). 2025-07-17T07:11:36.2308403Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-07-17T07:11:36.2308621Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-07-17T07:11:36.2308884Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-07-17T07:11:36.2309088Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-07-17T07:11:36.2309233Z Default: ``None``; try to detect a causal mask. 2025-07-17T07:11:36.2309335Z Warning: 2025-07-17T07:11:36.2309488Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-07-17T07:11:36.2309662Z the causal mask. Providing incorrect hints can result in 2025-07-17T07:11:36.2309840Z incorrect execution, including forward and backward 2025-07-17T07:11:36.2309968Z compatibility. 2025-07-17T07:11:36.2310186Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-07-17T07:11:36.2310327Z Default: ``None``; try to detect a causal mask. 2025-07-17T07:11:36.2310413Z Warning: 2025-07-17T07:11:36.2310576Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-07-17T07:11:36.2310744Z the causal mask. Providing incorrect hints can result in 2025-07-17T07:11:36.2310906Z incorrect execution, including forward and backward 2025-07-17T07:11:36.2311007Z compatibility. 2025-07-17T07:11:36.2311177Z memory_is_causal: If specified, applies a causal mask as 2025-07-17T07:11:36.2311279Z ``memory_mask``. 2025-07-17T07:11:36.2311398Z Default: ``False``. 2025-07-17T07:11:36.2311482Z Warning: 2025-07-17T07:11:36.2311623Z ``memory_is_causal`` provides a hint that 2025-07-17T07:11:36.2311784Z ``memory_mask`` is the causal mask. Providing incorrect 2025-07-17T07:11:36.2311939Z hints can result in incorrect execution, including 2025-07-17T07:11:36.2312070Z forward and backward compatibility. 2025-07-17T07:11:36.2312146Z 2025-07-17T07:11:36.2312227Z Shape: 2025-07-17T07:11:36.2312479Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-07-17T07:11:36.2312592Z `(N, S, E)` if `batch_first=True`. 2025-07-17T07:11:36.2312838Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-07-17T07:11:36.2312946Z `(N, T, E)` if `batch_first=True`. 2025-07-17T07:11:36.2313166Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-07-17T07:11:36.2313360Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-07-17T07:11:36.2313505Z - memory_mask: :math:`(T, S)`. 2025-07-17T07:11:36.2313749Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-07-17T07:11:36.2313995Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-07-17T07:11:36.2314247Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-07-17T07:11:36.2314337Z 2025-07-17T07:11:36.2314639Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-07-17T07:11:36.2314846Z positions. If a BoolTensor is provided, positions with ``True`` 2025-07-17T07:11:36.2315117Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-07-17T07:11:36.2315283Z is provided, it will be added to the attention weight. 2025-07-17T07:11:36.2315598Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-07-17T07:11:36.2315808Z the attention. If a BoolTensor is provided, the positions with the 2025-07-17T07:11:36.2316120Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-07-17T07:11:36.2316208Z 2025-07-17T07:11:36.2316461Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-07-17T07:11:36.2316574Z `(N, T, E)` if `batch_first=True`. 2025-07-17T07:11:36.2316658Z 2025-07-17T07:11:36.2316896Z Note: Due to the multi-head attention architecture in the transformer model, 2025-07-17T07:11:36.2317139Z the output sequence length of a transformer is same as the input sequence 2025-07-17T07:11:36.2317259Z (i.e. target) length of the decoder. 2025-07-17T07:11:36.2317383Z 2025-07-17T07:11:36.2317716Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-07-17T07:11:36.2317847Z batch size, :math:`E` is the feature number 2025-07-17T07:11:36.2317921Z 2025-07-17T07:11:36.2318013Z Examples: 2025-07-17T07:11:36.2318109Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2318220Z >>> output = transformer_model( 2025-07-17T07:11:36.2318374Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-07-17T07:11:36.2318452Z ... ) 2025-07-17T07:11:36.2318540Z 2025-07-17T07:11:36.2318792Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2318865Z 2025-07-17T07:11:36.2318970Z warnings.warn(msg) 2025-07-17T07:11:36.2319070Z 2025-07-17T07:11:36.2319268Z --- Parse Warning: 109 / 136 --- 2025-07-17T07:11:36.2320318Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1766. 2025-07-17T07:11:36.2320573Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2320660Z 2025-07-17T07:11:36.2320894Z Context manager for training with uneven inputs across processes in DDP. 2025-07-17T07:11:36.2320967Z 2025-07-17T07:11:36.2321198Z This context manager will keep track of already-joined DDP processes, 2025-07-17T07:11:36.2321402Z and "shadow" the forward and backward passes by inserting collective 2025-07-17T07:11:36.2321628Z communication operations to match with the ones created by non-joined 2025-07-17T07:11:36.2321895Z DDP processes. This will ensure each collective call has a corresponding 2025-07-17T07:11:36.2322112Z call by already-joined DDP processes, preventing hangs or errors that 2025-07-17T07:11:36.2322333Z would otherwise happen when training with uneven inputs across 2025-07-17T07:11:36.2322576Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-07-17T07:11:36.2322776Z specified to be ``True``, all trainers will throw an error once one rank 2025-07-17T07:11:36.2322977Z runs out of inputs, allowing these errors to be caught and handled 2025-07-17T07:11:36.2323087Z according to application logic. 2025-07-17T07:11:36.2323162Z 2025-07-17T07:11:36.2323383Z Once all DDP processes have joined, the context manager will broadcast 2025-07-17T07:11:36.2323605Z the model corresponding to the last joined process to all processes to 2025-07-17T07:11:36.2323744Z ensure the model is the same across all processes 2025-07-17T07:11:36.2323856Z (which is guaranteed by DDP). 2025-07-17T07:11:36.2323934Z 2025-07-17T07:11:36.2324139Z To use this to enable training with uneven inputs across processes, 2025-07-17T07:11:36.2324355Z simply wrap this context manager around your training loop. No further 2025-07-17T07:11:36.2324522Z modifications to the model or data loading is required. 2025-07-17T07:11:36.2324607Z 2025-07-17T07:11:36.2324692Z .. warning:: 2025-07-17T07:11:36.2324890Z If the model or training loop this context manager is wrapped around 2025-07-17T07:11:36.2325076Z has additional distributed collective operations, such as 2025-07-17T07:11:36.2325257Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-07-17T07:11:36.2325456Z ``throw_on_early_termination`` must be enabled. This is because this 2025-07-17T07:11:36.2325667Z context manager is not aware of non-DDP collective communication. 2025-07-17T07:11:36.2325830Z This flag will cause all ranks to throw when any one rank 2025-07-17T07:11:36.2326049Z exhausts inputs, allowing these errors to be caught and recovered 2025-07-17T07:11:36.2326175Z from across all ranks. 2025-07-17T07:11:36.2326251Z 2025-07-17T07:11:36.2326341Z Args: 2025-07-17T07:11:36.2326517Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-07-17T07:11:36.2326718Z gradients by the initial ``world_size`` DDP training was launched 2025-07-17T07:11:36.2326888Z with. If ``False``, will compute the effective world size 2025-07-17T07:11:36.2327067Z (number of ranks that have not depleted their inputs yet) and 2025-07-17T07:11:36.2327217Z divide gradients by that during allreduce. Set 2025-07-17T07:11:36.2327391Z ``divide_by_initial_world_size=True`` to ensure every input 2025-07-17T07:11:36.2327590Z sample including the uneven inputs have equal weight in terms of 2025-07-17T07:11:36.2327767Z how much they contribute to the global gradient. This is 2025-07-17T07:11:36.2327959Z achieved by always dividing the gradient by the initial 2025-07-17T07:11:36.2328139Z ``world_size`` even when we encounter uneven inputs. If you set 2025-07-17T07:11:36.2328316Z this to ``False``, we divide the gradient by the remaining 2025-07-17T07:11:36.2328502Z number of nodes. This ensures parity with training on a smaller 2025-07-17T07:11:36.2328689Z ``world_size`` although it also means the uneven inputs would 2025-07-17T07:11:36.2328879Z contribute more towards the global gradient. Typically, you 2025-07-17T07:11:36.2329059Z would want to set this to ``True`` for cases where the last few 2025-07-17T07:11:36.2329261Z inputs of your training job are uneven. In extreme cases, where 2025-07-17T07:11:36.2329441Z there is a large discrepancy in the number of inputs, setting 2025-07-17T07:11:36.2329576Z this to ``False`` might provide better results. 2025-07-17T07:11:36.2329820Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-07-17T07:11:36.2329983Z in ``enable=False`` to disable in cases where you know that 2025-07-17T07:11:36.2330199Z inputs are even across participating processes. Default is 2025-07-17T07:11:36.2330286Z ``True``. 2025-07-17T07:11:36.2330462Z throw_on_early_termination (bool): Whether to throw an error 2025-07-17T07:11:36.2330646Z or continue training when at least one rank has exhausted 2025-07-17T07:11:36.2330826Z inputs. If ``True``, will throw upon the first rank reaching end 2025-07-17T07:11:36.2330989Z of data. If ``False``, will continue training with a smaller 2025-07-17T07:11:36.2331187Z effective world size until all ranks are joined. Note that if 2025-07-17T07:11:36.2331308Z this flag is specified, then the flag 2025-07-17T07:11:36.2331485Z ``divide_by_initial_world_size`` would be ignored. Default 2025-07-17T07:11:36.2331575Z is ``False``. 2025-07-17T07:11:36.2331649Z 2025-07-17T07:11:36.2331735Z 2025-07-17T07:11:36.2331819Z Example:: 2025-07-17T07:11:36.2331893Z 2025-07-17T07:11:36.2332016Z >>> # xdoctest: +SKIP("Distributed") 2025-07-17T07:11:36.2332101Z >>> import torch 2025-07-17T07:11:36.2332214Z >>> import torch.distributed as dist 2025-07-17T07:11:36.2332305Z >>> import os 2025-07-17T07:11:36.2332422Z >>> import torch.multiprocessing as mp 2025-07-17T07:11:36.2336333Z >>> import torch.nn as nn 2025-07-17T07:11:36.2336471Z >>> # On each spawned worker 2025-07-17T07:11:36.2336578Z >>> def worker(rank): 2025-07-17T07:11:36.2336764Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-07-17T07:11:36.2336872Z >>> torch.cuda.set_device(rank) 2025-07-17T07:11:36.2337014Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-07-17T07:11:36.2337197Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-07-17T07:11:36.2337407Z >>> model, device_ids=[rank], output_device=rank 2025-07-17T07:11:36.2337501Z >>> ) 2025-07-17T07:11:36.2337627Z >>> # Rank 1 gets one more input than rank 0. 2025-07-17T07:11:36.2337820Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-07-17T07:11:36.2337920Z >>> with model.join(): 2025-07-17T07:11:36.2338013Z >>> for _ in range(5): 2025-07-17T07:11:36.2338122Z >>> for inp in inputs: 2025-07-17T07:11:36.2338233Z >>> loss = model(inp).sum() 2025-07-17T07:11:36.2338330Z >>> loss.backward() 2025-07-17T07:11:36.2338528Z >>> # Without the join() API, the below synchronization will hang 2025-07-17T07:11:36.2338667Z >>> # blocking for rank 1's allreduce to complete. 2025-07-17T07:11:36.2338789Z >>> torch.cuda.synchronize(device=rank) 2025-07-17T07:11:36.2338905Z 2025-07-17T07:11:36.2339162Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2339249Z 2025-07-17T07:11:36.2339345Z warnings.warn(msg) 2025-07-17T07:11:36.2339418Z 2025-07-17T07:11:36.2339654Z --- Parse Warning: 110 / 136 --- 2025-07-17T07:11:36.2340765Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2057. 2025-07-17T07:11:36.2341023Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2341107Z 2025-07-17T07:11:36.2341412Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-07-17T07:11:36.2341496Z 2025-07-17T07:11:36.2341735Z Registers an optimizer with DDP such that the optimization for a 2025-07-17T07:11:36.2341941Z parameter will run immediately when that parameter's gradient is 2025-07-17T07:11:36.2342144Z finished with reduction, instead of waiting for all parameters' 2025-07-17T07:11:36.2342383Z gradients to finish reduction. This can result in a training speedup 2025-07-17T07:11:36.2342597Z depending on your workload since the optimizer can run while gradient 2025-07-17T07:11:36.2342831Z reduction for other parameters are still ongoing. In addition, this has 2025-07-17T07:11:36.2343051Z the potential to reduce peak memory consumption during training, as it 2025-07-17T07:11:36.2343250Z only needs to load the per-parameter optimizer states of a single 2025-07-17T07:11:36.2343457Z parameter at a time, instead of loading all per-parameter optimizer 2025-07-17T07:11:36.2343546Z states at once. 2025-07-17T07:11:36.2343628Z 2025-07-17T07:11:36.2343704Z Args: 2025-07-17T07:11:36.2343898Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-07-17T07:11:36.2344002Z as a fused optimizer. 2025-07-17T07:11:36.2344165Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-07-17T07:11:36.2344375Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-07-17T07:11:36.2344596Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-07-17T07:11:36.2344795Z Optimizers. If this is omitted, all DDP model parameters will be 2025-07-17T07:11:36.2344890Z optimized. 2025-07-17T07:11:36.2345083Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-07-17T07:11:36.2345161Z 2025-07-17T07:11:36.2345259Z .. warning :: 2025-07-17T07:11:36.2345470Z _register_fused_optim should only be called once on a DDP instance, 2025-07-17T07:11:36.2345676Z and registering multiple fused optimizers for the same DDP model 2025-07-17T07:11:36.2345810Z is not currently supported. Please ping 2025-07-17T07:11:36.2346040Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T07:11:36.2346162Z for your use case. 2025-07-17T07:11:36.2346246Z 2025-07-17T07:11:36.2346334Z .. warning :: 2025-07-17T07:11:36.2346529Z _register_fused_optim and register_comm_hook currently do not 2025-07-17T07:11:36.2346743Z compose together, meaning that custom DDP communication hooks are 2025-07-17T07:11:36.2346912Z not supported with overlapped optimizers. Please ping 2025-07-17T07:11:36.2347143Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T07:11:36.2347235Z for your use case. 2025-07-17T07:11:36.2347314Z 2025-07-17T07:11:36.2347398Z .. warning :: 2025-07-17T07:11:36.2347618Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-07-17T07:11:36.2347742Z with overlapped optimizer. Please ping 2025-07-17T07:11:36.2347971Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-07-17T07:11:36.2348089Z for your use case. 2025-07-17T07:11:36.2348173Z 2025-07-17T07:11:36.2348254Z Example:: 2025-07-17T07:11:36.2348329Z 2025-07-17T07:11:36.2348465Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-07-17T07:11:36.2348759Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-07-17T07:11:36.2348952Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-07-17T07:11:36.2349039Z >>> lr = 1e-2 2025-07-17T07:11:36.2349128Z >>> betas = (0.9, 0.99) 2025-07-17T07:11:36.2349210Z >>> eps = 1e-6 2025-07-17T07:11:36.2349432Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-07-17T07:11:36.2349547Z >>> # Example with subset of parameters 2025-07-17T07:11:36.2349685Z >>> params_to_opt = [list(net.parameters())[0]] 2025-07-17T07:11:36.2349827Z >>> net._register_fused_optim( 2025-07-17T07:11:36.2350052Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-07-17T07:11:36.2350137Z ... ) 2025-07-17T07:11:36.2350214Z 2025-07-17T07:11:36.2350488Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2350570Z 2025-07-17T07:11:36.2350663Z warnings.warn(msg) 2025-07-17T07:11:36.2350739Z 2025-07-17T07:11:36.2350942Z --- Parse Warning: 111 / 136 --- 2025-07-17T07:11:36.2351951Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=14. 2025-07-17T07:11:36.2352222Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2352436Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-07-17T07:11:36.2352518Z 2025-07-17T07:11:36.2352794Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-07-17T07:11:36.2353066Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-07-17T07:11:36.2353332Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-07-17T07:11:36.2353634Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-07-17T07:11:36.2353712Z 2025-07-17T07:11:36.2353802Z .. note:: 2025-07-17T07:11:36.2354032Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-07-17T07:11:36.2354252Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-07-17T07:11:36.2354467Z layer with 4d weight will be affected by ``model.to``, which does not 2025-07-17T07:11:36.2354690Z necessarily benefit from conversion to specified ``memory_format``. 2025-07-17T07:11:36.2354950Z One place we are confident in is that NHWC(channels_last) conversion for 2025-07-17T07:11:36.2355169Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-07-17T07:11:36.2355366Z even in cases where we have to apply permutation to input tensors. 2025-07-17T07:11:36.2355454Z 2025-07-17T07:11:36.2355673Z Hence our strategy here is to convert only the weight of convolution to 2025-07-17T07:11:36.2355786Z channels_last. This ensures that; 2025-07-17T07:11:36.2356005Z 1. Fast convolution kernels will be used, the benefit of which could 2025-07-17T07:11:36.2356236Z outweigh overhead of permutation (if input is not in the same format). 2025-07-17T07:11:36.2356471Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-07-17T07:11:36.2356578Z from memory_format conversion. 2025-07-17T07:11:36.2356657Z 2025-07-17T07:11:36.2356916Z The optimal case is that, layers between convolution layers are channels 2025-07-17T07:11:36.2357147Z last compatible. Input tensor would be permuted to channels last when it 2025-07-17T07:11:36.2357375Z encounters the first convolution layer and stay in that memory format. 2025-07-17T07:11:36.2357615Z Hence following convolutions will not need to permute its input tensor. 2025-07-17T07:11:36.2357689Z 2025-07-17T07:11:36.2357906Z In case where a channels last incompatible layer is between convolution 2025-07-17T07:11:36.2358120Z layers, we need to permute the input tensor back to contiguous format 2025-07-17T07:11:36.2358337Z for that layer. The input tensor will go through the remaining layers in 2025-07-17T07:11:36.2358566Z contiguous format and be permuted to channels last when it encounters 2025-07-17T07:11:36.2358770Z another convolution layer. There's no point in propagating that 2025-07-17T07:11:36.2359010Z permutation to an earlier layer, as most layers are quite agnostic to 2025-07-17T07:11:36.2359110Z ``memory_format``. 2025-07-17T07:11:36.2359188Z 2025-07-17T07:11:36.2359436Z This claim might change when PyTorch supports fusion of permutation, as 2025-07-17T07:11:36.2359662Z there might have been a better spot to fuse the permutation other than 2025-07-17T07:11:36.2359779Z immediately before a convolution. 2025-07-17T07:11:36.2359867Z 2025-07-17T07:11:36.2359946Z Args: 2025-07-17T07:11:36.2360157Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-07-17T07:11:36.2360266Z ``nn.Module`` 2025-07-17T07:11:36.2360409Z memory_format: user specified ``memory_format``, 2025-07-17T07:11:36.2360586Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-07-17T07:11:36.2360676Z 2025-07-17T07:11:36.2360758Z Returns: 2025-07-17T07:11:36.2360898Z The original module with updated ``nn.Conv2d`` 2025-07-17T07:11:36.2360984Z 2025-07-17T07:11:36.2361064Z Example: 2025-07-17T07:11:36.2361209Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:36.2361357Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-07-17T07:11:36.2361457Z >>> input = torch.randint( 2025-07-17T07:11:36.2361612Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-07-17T07:11:36.2361686Z ... ) 2025-07-17T07:11:36.2361785Z >>> model = nn.Sequential( 2025-07-17T07:11:36.2361904Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-07-17T07:11:36.2361999Z >>> # This is identical to: 2025-07-17T07:11:36.2362235Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-07-17T07:11:36.2362404Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-07-17T07:11:36.2362523Z ... model, torch.channels_last 2025-07-17T07:11:36.2362629Z ... ) 2025-07-17T07:11:36.2362725Z >>> out = model(input) 2025-07-17T07:11:36.2362813Z 2025-07-17T07:11:36.2363061Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2363148Z 2025-07-17T07:11:36.2363238Z warnings.warn(msg) 2025-07-17T07:11:36.2363313Z 2025-07-17T07:11:36.2363514Z --- Parse Warning: 112 / 136 --- 2025-07-17T07:11:36.2364516Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=93. 2025-07-17T07:11:36.2364774Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2364994Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-07-17T07:11:36.2365298Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-07-17T07:11:36.2365580Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-07-17T07:11:36.2365826Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-07-17T07:11:36.2366127Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-07-17T07:11:36.2366213Z 2025-07-17T07:11:36.2366294Z .. note:: 2025-07-17T07:11:36.2366535Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-07-17T07:11:36.2366766Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-07-17T07:11:36.2366971Z layer with 4d weight will be affected by ``model.to``, which does not 2025-07-17T07:11:36.2367226Z necessarily benefit from conversion to specified ``memory_format``. 2025-07-17T07:11:36.2367460Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-07-17T07:11:36.2367700Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-07-17T07:11:36.2367908Z even in cases where we have to apply permutation to input tensors. 2025-07-17T07:11:36.2367982Z 2025-07-17T07:11:36.2368201Z Hence our strategy here is to convert only the weight of convolution to 2025-07-17T07:11:36.2368324Z channels_last_3d. This ensures that; 2025-07-17T07:11:36.2368533Z 1. Fast convolution kernels will be used, the benefit of which could 2025-07-17T07:11:36.2368769Z outweigh overhead of permutation (if input is not in the same format). 2025-07-17T07:11:36.2368997Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-07-17T07:11:36.2369104Z from memory_format conversion. 2025-07-17T07:11:36.2369193Z 2025-07-17T07:11:36.2369413Z The optimal case is that, layers between convolution layers are channels 2025-07-17T07:11:36.2369646Z last compatible. Input tensor would be permuted to channels last when it 2025-07-17T07:11:36.2369882Z encounters the first convolution layer and stay in that memory format. 2025-07-17T07:11:36.2370114Z Hence following convolutions will not need to permute its input tensor. 2025-07-17T07:11:36.2370199Z 2025-07-17T07:11:36.2370414Z In case where a channels last incompatible layer is between convolution 2025-07-17T07:11:36.2370619Z layers, we need to permute the input tensor back to contiguous format 2025-07-17T07:11:36.2370847Z for that layer. The input tensor will go through the remaining layers in 2025-07-17T07:11:36.2371066Z contiguous format and be permuted to channels last when it encounters 2025-07-17T07:11:36.2371271Z another convolution layer. There's no point in propagating that 2025-07-17T07:11:36.2371523Z permutation to an earlier layer, as most layers are quite agnostic to 2025-07-17T07:11:36.2371618Z ``memory_format``. 2025-07-17T07:11:36.2371704Z 2025-07-17T07:11:36.2372075Z This claim might change when PyTorch supports fusion of permutation, as 2025-07-17T07:11:36.2372291Z there might have been a better spot to fuse the permutation other than 2025-07-17T07:11:36.2372419Z immediately before a convolution. 2025-07-17T07:11:36.2372495Z 2025-07-17T07:11:36.2372572Z Args: 2025-07-17T07:11:36.2372789Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-07-17T07:11:36.2372886Z ``nn.Module`` 2025-07-17T07:11:36.2373044Z memory_format: user specified ``memory_format``, 2025-07-17T07:11:36.2373221Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-07-17T07:11:36.2373356Z 2025-07-17T07:11:36.2373445Z Returns: 2025-07-17T07:11:36.2373586Z The original module with updated ``nn.Conv3d`` 2025-07-17T07:11:36.2373660Z 2025-07-17T07:11:36.2373755Z Example: 2025-07-17T07:11:36.2373890Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-07-17T07:11:36.2374039Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-07-17T07:11:36.2374148Z >>> input = torch.randint( 2025-07-17T07:11:36.2374290Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-07-17T07:11:36.2374369Z ... ) 2025-07-17T07:11:36.2374481Z >>> model = nn.Sequential( 2025-07-17T07:11:36.2374590Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-07-17T07:11:36.2374700Z >>> # This is identical to: 2025-07-17T07:11:36.2374952Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-07-17T07:11:36.2375145Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-07-17T07:11:36.2375358Z ... model, torch.channels_last_3d 2025-07-17T07:11:36.2375438Z ... ) 2025-07-17T07:11:36.2375532Z >>> out = model(input) 2025-07-17T07:11:36.2375657Z 2025-07-17T07:11:36.2375905Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2375992Z 2025-07-17T07:11:36.2376081Z warnings.warn(msg) 2025-07-17T07:11:36.2376158Z 2025-07-17T07:11:36.2376377Z --- Parse Warning: 113 / 136 --- 2025-07-17T07:11:36.2377349Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py line=424. 2025-07-17T07:11:36.2377608Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2377773Z Register a parametrization to a tensor in a module. 2025-07-17T07:11:36.2377848Z 2025-07-17T07:11:36.2378123Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-07-17T07:11:36.2378410Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-07-17T07:11:36.2378670Z If the original tensor requires a gradient, the backward pass will differentiate 2025-07-17T07:11:36.2378971Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-07-17T07:11:36.2379043Z 2025-07-17T07:11:36.2379352Z The first time that a module registers a parametrization, this function will add an attribute 2025-07-17T07:11:36.2379599Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-07-17T07:11:36.2379670Z 2025-07-17T07:11:36.2379920Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-07-17T07:11:36.2380054Z ``module.parametrizations.weight``. 2025-07-17T07:11:36.2380129Z 2025-07-17T07:11:36.2380316Z The original tensor will be accessible under 2025-07-17T07:11:36.2380461Z ``module.parametrizations.weight.original``. 2025-07-17T07:11:36.2380538Z 2025-07-17T07:11:36.2380813Z Parametrizations may be concatenated by registering several parametrizations 2025-07-17T07:11:36.2380903Z on the same attribute. 2025-07-17T07:11:36.2380978Z 2025-07-17T07:11:36.2381224Z The training mode of a registered parametrization is updated on registration 2025-07-17T07:11:36.2381353Z to match the training mode of the host module 2025-07-17T07:11:36.2381431Z 2025-07-17T07:11:36.2381741Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-07-17T07:11:36.2381861Z using the context manager :func:`cached`. 2025-07-17T07:11:36.2381947Z 2025-07-17T07:11:36.2382185Z A :attr:`parametrization` may optionally implement a method with signature 2025-07-17T07:11:36.2382304Z 2025-07-17T07:11:36.2382421Z .. code-block:: python 2025-07-17T07:11:36.2382498Z 2025-07-17T07:11:36.2382712Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-07-17T07:11:36.2382798Z 2025-07-17T07:11:36.2383057Z This method is called on the unparametrized tensor when the first parametrization 2025-07-17T07:11:36.2383261Z is registered to compute the initial value of the original tensor. 2025-07-17T07:11:36.2383577Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-07-17T07:11:36.2383652Z 2025-07-17T07:11:36.2383967Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-07-17T07:11:36.2384253Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-07-17T07:11:36.2384328Z 2025-07-17T07:11:36.2384587Z It is possible for the first parametrization to depend on several inputs. 2025-07-17T07:11:36.2384830Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-07-17T07:11:36.2385085Z (see the example implementation of a ``RankOne`` parametrization below). 2025-07-17T07:11:36.2385169Z 2025-07-17T07:11:36.2385503Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-07-17T07:11:36.2385632Z with names ``original0``, ``original1``,... 2025-07-17T07:11:36.2385705Z 2025-07-17T07:11:36.2385788Z .. note:: 2025-07-17T07:11:36.2385869Z 2025-07-17T07:11:36.2386133Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-07-17T07:11:36.2386277Z once to perform a number of consistency checks. 2025-07-17T07:11:36.2386550Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-07-17T07:11:36.2386672Z and nothing will be called otherwise. 2025-07-17T07:11:36.2386761Z 2025-07-17T07:11:36.2386841Z .. note:: 2025-07-17T07:11:36.2386918Z 2025-07-17T07:11:36.2387128Z In most situations, ``right_inverse`` will be a function such that 2025-07-17T07:11:36.2387244Z ``forward(right_inverse(X)) == X`` (see 2025-07-17T07:11:36.2387524Z `right inverse `_). 2025-07-17T07:11:36.2387780Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-07-17T07:11:36.2387872Z to relax this. 2025-07-17T07:11:36.2387947Z 2025-07-17T07:11:36.2388035Z .. warning:: 2025-07-17T07:11:36.2388112Z 2025-07-17T07:11:36.2388384Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-07-17T07:11:36.2388646Z will register a number of new parameters. If such parametrization is registered 2025-07-17T07:11:36.2388918Z after the optimizer is created, these new parameters will need to be added manually 2025-07-17T07:11:36.2389147Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-07-17T07:11:36.2389225Z 2025-07-17T07:11:36.2389306Z Args: 2025-07-17T07:11:36.2389521Z module (nn.Module): module on which to register the parametrization 2025-07-17T07:11:36.2389732Z tensor_name (str): name of the parameter or buffer on which to register 2025-07-17T07:11:36.2389831Z the parametrization 2025-07-17T07:11:36.2390036Z parametrization (nn.Module): the parametrization to register 2025-07-17T07:11:36.2390124Z Keyword args: 2025-07-17T07:11:36.2390340Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-07-17T07:11:36.2390528Z may change the dtype and shape of the tensor. Default: `False` 2025-07-17T07:11:36.2390795Z Warning: the parametrization is not checked for consistency upon registration. 2025-07-17T07:11:36.2390942Z Enable this flag at your own risk. 2025-07-17T07:11:36.2391020Z 2025-07-17T07:11:36.2391100Z Raises: 2025-07-17T07:11:36.2391394Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-07-17T07:11:36.2391471Z 2025-07-17T07:11:36.2391560Z Examples: 2025-07-17T07:11:36.2391705Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-07-17T07:11:36.2391798Z >>> import torch 2025-07-17T07:11:36.2391906Z >>> import torch.nn as nn 2025-07-17T07:11:36.2392039Z >>> import torch.nn.utils.parametrize as P 2025-07-17T07:11:36.2392117Z >>> 2025-07-17T07:11:36.2392229Z >>> class Symmetric(nn.Module): 2025-07-17T07:11:36.2392327Z >>> def forward(self, X): 2025-07-17T07:11:36.2392501Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-07-17T07:11:36.2392615Z >>> 2025-07-17T07:11:36.2392724Z >>> def right_inverse(self, A): 2025-07-17T07:11:36.2392819Z >>> return A.triu() 2025-07-17T07:11:36.2392906Z >>> 2025-07-17T07:11:36.2393026Z >>> m = nn.Linear(5, 5) 2025-07-17T07:11:36.2393204Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-07-17T07:11:36.2393442Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-07-17T07:11:36.2393519Z True 2025-07-17T07:11:36.2393620Z >>> A = torch.rand(5, 5) 2025-07-17T07:11:36.2393727Z >>> A = A + A.T # A is now symmetric 2025-07-17T07:11:36.2393920Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-07-17T07:11:36.2394041Z >>> print(torch.allclose(m.weight, A)) 2025-07-17T07:11:36.2394119Z True 2025-07-17T07:11:36.2394193Z 2025-07-17T07:11:36.2394300Z >>> class RankOne(nn.Module): 2025-07-17T07:11:36.2394405Z >>> def forward(self, x, y): 2025-07-17T07:11:36.2394545Z >>> # Form a rank 1 matrix multiplying two vectors 2025-07-17T07:11:36.2394674Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-07-17T07:11:36.2394749Z >>> 2025-07-17T07:11:36.2394860Z >>> def right_inverse(self, Z): 2025-07-17T07:11:36.2394974Z >>> # Project Z onto the rank 1 matrices 2025-07-17T07:11:36.2395122Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-07-17T07:11:36.2395242Z >>> # Return rescaled singular vectors 2025-07-17T07:11:36.2395357Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-07-17T07:11:36.2395506Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-07-17T07:11:36.2395586Z >>> 2025-07-17T07:11:36.2395729Z >>> linear_rank_one = P.register_parametrization( 2025-07-17T07:11:36.2395858Z ... nn.Linear(4, 4), "weight", RankOne() 2025-07-17T07:11:36.2395935Z ... ) 2025-07-17T07:11:36.2396161Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-07-17T07:11:36.2396248Z 1 2025-07-17T07:11:36.2396324Z 2025-07-17T07:11:36.2396403Z 2025-07-17T07:11:36.2396661Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2396738Z 2025-07-17T07:11:36.2396832Z warnings.warn(msg) 2025-07-17T07:11:36.2396913Z 2025-07-17T07:11:36.2397112Z --- Parse Warning: 114 / 136 --- 2025-07-17T07:11:36.2397995Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=979. 2025-07-17T07:11:36.2398253Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2398591Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-07-17T07:11:36.2398677Z 2025-07-17T07:11:36.2398907Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-07-17T07:11:36.2399117Z by removing the specified ``amount`` of (currently unpruned) channels 2025-07-17T07:11:36.2399297Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-07-17T07:11:36.2399487Z Modifies module in place (and also return the modified module) 2025-07-17T07:11:36.2399574Z by: 2025-07-17T07:11:36.2399650Z 2025-07-17T07:11:36.2399854Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T07:11:36.2400081Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T07:11:36.2400284Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T07:11:36.2400514Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T07:11:36.2400618Z ``name+'_orig'``. 2025-07-17T07:11:36.2400695Z 2025-07-17T07:11:36.2400771Z Args: 2025-07-17T07:11:36.2400978Z module (nn.Module): module containing the tensor to prune 2025-07-17T07:11:36.2401158Z name (str): parameter name within ``module`` on which pruning 2025-07-17T07:11:36.2401254Z will act. 2025-07-17T07:11:36.2401418Z amount (int or float): quantity of parameters to prune. 2025-07-17T07:11:36.2401588Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-07-17T07:11:36.2401793Z fraction of parameters to prune. If ``int``, it represents the 2025-07-17T07:11:36.2401917Z absolute number of parameters to prune. 2025-07-17T07:11:36.2402104Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-07-17T07:11:36.2402266Z entries for argument ``p`` in :func:`torch.norm`. 2025-07-17T07:11:36.2402461Z dim (int): index of the dim along which we define channels to prune. 2025-07-17T07:11:36.2402701Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-07-17T07:11:36.2402883Z shape as module parameter) used to compute mask for pruning. 2025-07-17T07:11:36.2403099Z The values in this tensor indicate the importance of the corresponding 2025-07-17T07:11:36.2403233Z elements in the parameter being pruned. 2025-07-17T07:11:36.2403455Z If unspecified or None, the module parameter will be used in its place. 2025-07-17T07:11:36.2403528Z 2025-07-17T07:11:36.2403615Z Returns: 2025-07-17T07:11:36.2403825Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-07-17T07:11:36.2403907Z 2025-07-17T07:11:36.2403989Z Examples: 2025-07-17T07:11:36.2404104Z >>> from torch.nn.utils import prune 2025-07-17T07:11:36.2404216Z >>> m = prune.ln_structured( 2025-07-17T07:11:36.2404420Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-07-17T07:11:36.2404500Z ... ) 2025-07-17T07:11:36.2404587Z 2025-07-17T07:11:36.2404832Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2404908Z 2025-07-17T07:11:36.2405012Z warnings.warn(msg) 2025-07-17T07:11:36.2405086Z 2025-07-17T07:11:36.2405270Z --- Parse Warning: 115 / 136 --- 2025-07-17T07:11:36.2406200Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1026. 2025-07-17T07:11:36.2406457Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2406541Z 2025-07-17T07:11:36.2406972Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-07-17T07:11:36.2407076Z 2025-07-17T07:11:36.2407188Z Modifies modules in place by: 2025-07-17T07:11:36.2407260Z 2025-07-17T07:11:36.2407464Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T07:11:36.2407685Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T07:11:36.2407885Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T07:11:36.2408095Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T07:11:36.2408182Z ``name+'_orig'``. 2025-07-17T07:11:36.2408258Z 2025-07-17T07:11:36.2408343Z Args: 2025-07-17T07:11:36.2408538Z parameters (Iterable of (module, name) tuples): parameters of 2025-07-17T07:11:36.2408722Z the model to prune in a global fashion, i.e. by aggregating all 2025-07-17T07:11:36.2408958Z weights prior to deciding which ones to prune. module must be of 2025-07-17T07:11:36.2409107Z type :class:`nn.Module`, and name must be a string. 2025-07-17T07:11:36.2409352Z pruning_method (function): a valid pruning function from this module, 2025-07-17T07:11:36.2409523Z or a custom one implemented by the user that satisfies the 2025-07-17T07:11:36.2409747Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-07-17T07:11:36.2409973Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-07-17T07:11:36.2410190Z the corresponding parameter's importance scores tensor. The tensor 2025-07-17T07:11:36.2410388Z should be the same shape as the parameter, and is used for computing 2025-07-17T07:11:36.2410487Z mask for pruning. 2025-07-17T07:11:36.2410686Z If unspecified or None, the parameter will be used in place of its 2025-07-17T07:11:36.2410788Z importance scores. 2025-07-17T07:11:36.2410908Z kwargs: other keyword arguments such as: 2025-07-17T07:11:36.2411099Z amount (int or float): quantity of parameters to prune across the 2025-07-17T07:11:36.2411204Z specified parameters. 2025-07-17T07:11:36.2411372Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-07-17T07:11:36.2411562Z fraction of parameters to prune. If ``int``, it represents the 2025-07-17T07:11:36.2411691Z absolute number of parameters to prune. 2025-07-17T07:11:36.2411764Z 2025-07-17T07:11:36.2411837Z Raises: 2025-07-17T07:11:36.2411986Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-07-17T07:11:36.2412061Z 2025-07-17T07:11:36.2412142Z Note: 2025-07-17T07:11:36.2412350Z Since global structured pruning doesn't make much sense unless the 2025-07-17T07:11:36.2412539Z norm is normalized by the size of the parameter, we now limit the 2025-07-17T07:11:36.2412692Z scope of global pruning to unstructured methods. 2025-07-17T07:11:36.2412767Z 2025-07-17T07:11:36.2412845Z Examples: 2025-07-17T07:11:36.2413000Z >>> from torch.nn.utils import prune 2025-07-17T07:11:36.2413118Z >>> from collections import OrderedDict 2025-07-17T07:11:36.2413210Z >>> net = nn.Sequential( 2025-07-17T07:11:36.2413305Z ... OrderedDict( 2025-07-17T07:11:36.2413380Z ... [ 2025-07-17T07:11:36.2413501Z ... ("first", nn.Linear(10, 4)), 2025-07-17T07:11:36.2413612Z ... ("second", nn.Linear(4, 1)), 2025-07-17T07:11:36.2413688Z ... ] 2025-07-17T07:11:36.2413767Z ... ) 2025-07-17T07:11:36.2413840Z ... ) 2025-07-17T07:11:36.2413938Z >>> parameters_to_prune = ( 2025-07-17T07:11:36.2414039Z ... (net.first, "weight"), 2025-07-17T07:11:36.2414136Z ... (net.second, "weight"), 2025-07-17T07:11:36.2414212Z ... ) 2025-07-17T07:11:36.2414323Z >>> prune.global_unstructured( 2025-07-17T07:11:36.2414445Z ... parameters_to_prune, 2025-07-17T07:11:36.2414574Z ... pruning_method=prune.L1Unstructured, 2025-07-17T07:11:36.2414666Z ... amount=10, 2025-07-17T07:11:36.2414740Z ... ) 2025-07-17T07:11:36.2414960Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-07-17T07:11:36.2415041Z tensor(10) 2025-07-17T07:11:36.2415113Z 2025-07-17T07:11:36.2415287Z 2025-07-17T07:11:36.2415538Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2415612Z 2025-07-17T07:11:36.2415710Z warnings.warn(msg) 2025-07-17T07:11:36.2415782Z 2025-07-17T07:11:36.2415975Z --- Parse Warning: 116 / 136 --- 2025-07-17T07:11:36.2416875Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1149. 2025-07-17T07:11:36.2417168Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2417596Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-07-17T07:11:36.2417670Z 2025-07-17T07:11:36.2417879Z Modifies module in place (and also return the modified module) by: 2025-07-17T07:11:36.2417961Z 2025-07-17T07:11:36.2418164Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-07-17T07:11:36.2418377Z binary mask applied to the parameter ``name`` by the pruning method. 2025-07-17T07:11:36.2418590Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-07-17T07:11:36.2418792Z original (unpruned) parameter is stored in a new parameter named 2025-07-17T07:11:36.2418887Z ``name+'_orig'``. 2025-07-17T07:11:36.2418960Z 2025-07-17T07:11:36.2419042Z Args: 2025-07-17T07:11:36.2419220Z module (nn.Module): module containing the tensor to prune 2025-07-17T07:11:36.2419401Z name (str): parameter name within ``module`` on which pruning 2025-07-17T07:11:36.2419489Z will act. 2025-07-17T07:11:36.2419664Z mask (Tensor): binary mask to be applied to the parameter. 2025-07-17T07:11:36.2419739Z 2025-07-17T07:11:36.2419821Z Returns: 2025-07-17T07:11:36.2420041Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-07-17T07:11:36.2420116Z 2025-07-17T07:11:36.2420198Z Examples: 2025-07-17T07:11:36.2420319Z >>> from torch.nn.utils import prune 2025-07-17T07:11:36.2420425Z >>> m = prune.custom_from_mask( 2025-07-17T07:11:36.2420598Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-07-17T07:11:36.2420674Z ... ) 2025-07-17T07:11:36.2420768Z >>> print(m.bias_mask) 2025-07-17T07:11:36.2420863Z tensor([0., 1., 0.]) 2025-07-17T07:11:36.2420938Z 2025-07-17T07:11:36.2421045Z 2025-07-17T07:11:36.2421298Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2421375Z 2025-07-17T07:11:36.2421467Z warnings.warn(msg) 2025-07-17T07:11:36.2421548Z 2025-07-17T07:11:36.2421730Z --- Parse Warning: 117 / 136 --- 2025-07-17T07:11:36.2422631Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py line=342. 2025-07-17T07:11:36.2422887Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2423026Z Pad a packed batch of variable length sequences. 2025-07-17T07:11:36.2423108Z 2025-07-17T07:11:36.2423278Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-07-17T07:11:36.2423380Z 2025-07-17T07:11:36.2423669Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-07-17T07:11:36.2423904Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-07-17T07:11:36.2424024Z sequence and ``B`` is the batch size. 2025-07-17T07:11:36.2424098Z 2025-07-17T07:11:36.2424176Z Example: 2025-07-17T07:11:36.2424414Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-07-17T07:11:36.2424553Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-07-17T07:11:36.2424640Z >>> lens = [2, 1, 3] 2025-07-17T07:11:36.2424759Z >>> packed = pack_padded_sequence( 2025-07-17T07:11:36.2424909Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-07-17T07:11:36.2424987Z ... ) 2025-07-17T07:11:36.2425101Z >>> packed 2025-07-17T07:11:36.2425341Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-07-17T07:11:36.2425562Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-07-17T07:11:36.2425829Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-07-17T07:11:36.2425920Z >>> seq_unpacked 2025-07-17T07:11:36.2426016Z tensor([[1, 2, 0], 2025-07-17T07:11:36.2426096Z [3, 0, 0], 2025-07-17T07:11:36.2426177Z [4, 5, 6]]) 2025-07-17T07:11:36.2426268Z >>> lens_unpacked 2025-07-17T07:11:36.2426351Z tensor([2, 1, 3]) 2025-07-17T07:11:36.2426423Z 2025-07-17T07:11:36.2426512Z .. note:: 2025-07-17T07:11:36.2426651Z :attr:`total_length` is useful to implement the 2025-07-17T07:11:36.2426875Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-07-17T07:11:36.2427086Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-07-17T07:11:36.2427317Z See :ref:`this FAQ section ` for 2025-07-17T07:11:36.2427402Z details. 2025-07-17T07:11:36.2427476Z 2025-07-17T07:11:36.2427551Z Args: 2025-07-17T07:11:36.2427683Z sequence (PackedSequence): batch to pad 2025-07-17T07:11:36.2427909Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-07-17T07:11:36.2428014Z format, ``T x B x *`` otherwise. 2025-07-17T07:11:36.2428204Z padding_value (float, optional): values for padded elements. 2025-07-17T07:11:36.2428427Z total_length (int, optional): if not ``None``, the output will be padded to 2025-07-17T07:11:36.2428662Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-07-17T07:11:36.2428844Z if :attr:`total_length` is less than the max sequence length in 2025-07-17T07:11:36.2428938Z :attr:`sequence`. 2025-07-17T07:11:36.2429019Z 2025-07-17T07:11:36.2429138Z Returns: 2025-07-17T07:11:36.2429324Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-07-17T07:11:36.2429520Z containing the list of lengths of each sequence in the batch. 2025-07-17T07:11:36.2429744Z Batch elements will be re-ordered as they were ordered originally when 2025-07-17T07:11:36.2429965Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-07-17T07:11:36.2430042Z 2025-07-17T07:11:36.2430288Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2430374Z 2025-07-17T07:11:36.2430465Z warnings.warn(msg) 2025-07-17T07:11:36.2430539Z 2025-07-17T07:11:36.2430726Z --- Parse Warning: 118 / 136 --- 2025-07-17T07:11:36.2431635Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=808. 2025-07-17T07:11:36.2431929Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2432254Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-07-17T07:11:36.2432333Z 2025-07-17T07:11:36.2432707Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-07-17T07:11:36.2432918Z intervals by which each scheduler should be called at a given epoch. 2025-07-17T07:11:36.2432990Z 2025-07-17T07:11:36.2433075Z Args: 2025-07-17T07:11:36.2433207Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:36.2433346Z schedulers (list): List of chained schedulers. 2025-07-17T07:11:36.2433592Z milestones (list): List of integers that reflects milestone points. 2025-07-17T07:11:36.2433752Z last_epoch (int): The index of last epoch. Default: -1. 2025-07-17T07:11:36.2433834Z 2025-07-17T07:11:36.2433914Z Example: 2025-07-17T07:11:36.2434032Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2434186Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-07-17T07:11:36.2434286Z >>> # lr = 0.005 if epoch == 0 2025-07-17T07:11:36.2434382Z >>> # lr = 0.005 if epoch == 1 2025-07-17T07:11:36.2434485Z >>> # lr = 0.005 if epoch == 2 2025-07-17T07:11:36.2434562Z >>> # ... 2025-07-17T07:11:36.2434661Z >>> # lr = 0.05 if epoch == 20 2025-07-17T07:11:36.2434765Z >>> # lr = 0.045 if epoch == 21 2025-07-17T07:11:36.2434860Z >>> # lr = 0.0405 if epoch == 22 2025-07-17T07:11:36.2435067Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-07-17T07:11:36.2435220Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-07-17T07:11:36.2435328Z >>> scheduler = SequentialLR( 2025-07-17T07:11:36.2435421Z ... optimizer, 2025-07-17T07:11:36.2435547Z ... schedulers=[scheduler1, scheduler2], 2025-07-17T07:11:36.2435641Z ... milestones=[20], 2025-07-17T07:11:36.2435724Z ... ) 2025-07-17T07:11:36.2435826Z >>> for epoch in range(100): 2025-07-17T07:11:36.2435910Z >>> train(...) 2025-07-17T07:11:36.2436004Z >>> validate(...) 2025-07-17T07:11:36.2436100Z >>> scheduler.step() 2025-07-17T07:11:36.2436179Z 2025-07-17T07:11:36.2436350Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-07-17T07:11:36.2436424Z 2025-07-17T07:11:36.2436679Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2436753Z 2025-07-17T07:11:36.2436846Z warnings.warn(msg) 2025-07-17T07:11:36.2436928Z 2025-07-17T07:11:36.2437106Z --- Parse Warning: 119 / 136 --- 2025-07-17T07:11:36.2438070Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1233. 2025-07-17T07:11:36.2438314Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-07-17T07:11:36.2438490Z Reduce learning rate when a metric has stopped improving. 2025-07-17T07:11:36.2438566Z 2025-07-17T07:11:36.2438761Z Models often benefit from reducing the learning rate by a factor 2025-07-17T07:11:36.2438943Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-07-17T07:11:36.2439134Z quantity and if no improvement is seen for a 'patience' number 2025-07-17T07:11:36.2439248Z of epochs, the learning rate is reduced. 2025-07-17T07:11:36.2439323Z 2025-07-17T07:11:36.2439427Z Args: 2025-07-17T07:11:36.2439555Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:36.2439709Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-07-17T07:11:36.2439862Z be reduced when the quantity monitored has stopped 2025-07-17T07:11:36.2440017Z decreasing; in `max` mode it will be reduced when the 2025-07-17T07:11:36.2440212Z quantity monitored has stopped increasing. Default: 'min'. 2025-07-17T07:11:36.2440377Z factor (float): Factor by which the learning rate will be 2025-07-17T07:11:36.2440509Z reduced. new_lr = lr * factor. Default: 0.1. 2025-07-17T07:11:36.2440725Z patience (int): The number of allowed epochs with no improvement after 2025-07-17T07:11:36.2440847Z which the learning rate will be reduced. 2025-07-17T07:11:36.2441076Z For example, consider the case of having no patience (`patience = 0`). 2025-07-17T07:11:36.2441469Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-07-17T07:11:36.2441710Z In the second epoch, if the performance is worse than the baseline, 2025-07-17T07:11:36.2441867Z we have what is considered an intolerable epoch. 2025-07-17T07:11:36.2442123Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-07-17T07:11:36.2442287Z the learning rate is reduced at the end of this epoch. 2025-07-17T07:11:36.2442610Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-07-17T07:11:36.2442924Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-07-17T07:11:36.2443045Z the learning rate is not adjusted. 2025-07-17T07:11:36.2443136Z Default: 10. 2025-07-17T07:11:36.2443323Z threshold (float): Threshold for measuring the new optimum, 2025-07-17T07:11:36.2443484Z to only focus on significant changes. Default: 1e-4. 2025-07-17T07:11:36.2443651Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-07-17T07:11:36.2443813Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-07-17T07:11:36.2443950Z mode or best * ( 1 - threshold ) in `min` mode. 2025-07-17T07:11:36.2444100Z In `abs` mode, dynamic_threshold = best + threshold in 2025-07-17T07:11:36.2444277Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-07-17T07:11:36.2444440Z cooldown (int): Number of epochs to wait before resuming 2025-07-17T07:11:36.2444611Z normal operation after lr has been reduced. Default: 0. 2025-07-17T07:11:36.2444774Z min_lr (float or list): A scalar or a list of scalars. A 2025-07-17T07:11:36.2444930Z lower bound on the learning rate of all param groups 2025-07-17T07:11:36.2445095Z or each group respectively. Default: 0. 2025-07-17T07:11:36.2445265Z eps (float): Minimal decay applied to lr. If the difference 2025-07-17T07:11:36.2445439Z between new and old lr is smaller than eps, the update is 2025-07-17T07:11:36.2445545Z ignored. Default: 1e-8. 2025-07-17T07:11:36.2445618Z 2025-07-17T07:11:36.2445700Z Example: 2025-07-17T07:11:36.2445799Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2446022Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-07-17T07:11:36.2446173Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-07-17T07:11:36.2446277Z >>> for epoch in range(10): 2025-07-17T07:11:36.2446365Z >>> train(...) 2025-07-17T07:11:36.2446475Z >>> val_loss = validate(...) 2025-07-17T07:11:36.2446621Z >>> # Note that step should be called after validate() 2025-07-17T07:11:36.2446754Z >>> scheduler.step(val_loss) 2025-07-17T07:11:36.2446837Z 2025-07-17T07:11:36.2447026Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-07-17T07:11:36.2447105Z 2025-07-17T07:11:36.2447530Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-07-17T07:11:36.2447604Z 2025-07-17T07:11:36.2447702Z scheduler.step(val_loss) 2025-07-17T07:11:36.2447785Z ^ 2025-07-17T07:11:36.2447874Z warnings.warn(msg) 2025-07-17T07:11:36.2447944Z 2025-07-17T07:11:36.2448132Z --- Parse Warning: 120 / 136 --- 2025-07-17T07:11:36.2449029Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1430. 2025-07-17T07:11:36.2449321Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2449642Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-07-17T07:11:36.2449718Z 2025-07-17T07:11:36.2450049Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-07-17T07:11:36.2450305Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-07-17T07:11:36.2450539Z The distance between the two boundaries can be scaled on a per-iteration 2025-07-17T07:11:36.2450633Z or per-cycle basis. 2025-07-17T07:11:36.2450706Z 2025-07-17T07:11:36.2450951Z Cyclical learning rate policy changes the learning rate after every batch. 2025-07-17T07:11:36.2451141Z `step` should be called after a batch has been used for training. 2025-07-17T07:11:36.2451217Z 2025-07-17T07:11:36.2451425Z This class has three built-in policies, as put forth in the paper: 2025-07-17T07:11:36.2451501Z 2025-07-17T07:11:36.2451699Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-07-17T07:11:36.2451999Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-07-17T07:11:36.2452319Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-07-17T07:11:36.2452427Z at each cycle iteration. 2025-07-17T07:11:36.2452502Z 2025-07-17T07:11:36.2452736Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-07-17T07:11:36.2452817Z 2025-07-17T07:11:36.2452893Z Args: 2025-07-17T07:11:36.2453027Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:36.2453205Z base_lr (float or list): Initial learning rate which is the 2025-07-17T07:11:36.2453368Z lower boundary in the cycle for each parameter group. 2025-07-17T07:11:36.2453571Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-07-17T07:11:36.2453724Z for each parameter group. Functionally, 2025-07-17T07:11:36.2453878Z it defines the cycle amplitude (max_lr - base_lr). 2025-07-17T07:11:36.2454004Z The lr at any cycle is the sum of base_lr 2025-07-17T07:11:36.2454136Z and some scaling of the amplitude; therefore 2025-07-17T07:11:36.2454276Z max_lr may not actually be reached depending on 2025-07-17T07:11:36.2454374Z scaling function. 2025-07-17T07:11:36.2454536Z step_size_up (int): Number of training iterations in the 2025-07-17T07:11:36.2454667Z increasing half of a cycle. Default: 2000 2025-07-17T07:11:36.2454835Z step_size_down (int): Number of training iterations in the 2025-07-17T07:11:36.2454995Z decreasing half of a cycle. If step_size_down is None, 2025-07-17T07:11:36.2455126Z it is set to step_size_up. Default: None 2025-07-17T07:11:36.2455416Z mode (str): One of {triangular, triangular2, exp_range}. 2025-07-17T07:11:36.2455559Z Values correspond to policies detailed above. 2025-07-17T07:11:36.2455711Z If scale_fn is not None, this argument is ignored. 2025-07-17T07:11:36.2455814Z Default: 'triangular' 2025-07-17T07:11:36.2455973Z gamma (float): Constant in 'exp_range' scaling function: 2025-07-17T07:11:36.2456078Z gamma**(cycle iterations) 2025-07-17T07:11:36.2456166Z Default: 1.0 2025-07-17T07:11:36.2456356Z scale_fn (function): Custom scaling policy defined by a single 2025-07-17T07:11:36.2456470Z argument lambda function, where 2025-07-17T07:11:36.2456579Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-07-17T07:11:36.2456699Z If specified, then 'mode' is ignored. 2025-07-17T07:11:36.2456816Z Default: None 2025-07-17T07:11:36.2456941Z scale_mode (str): {'cycle', 'iterations'}. 2025-07-17T07:11:36.2457072Z Defines whether scale_fn is evaluated on 2025-07-17T07:11:36.2457224Z cycle number or cycle iterations (training 2025-07-17T07:11:36.2457346Z iterations since start of cycle). 2025-07-17T07:11:36.2457437Z Default: 'cycle' 2025-07-17T07:11:36.2457628Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-07-17T07:11:36.2457812Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-07-17T07:11:36.2457899Z Default: True 2025-07-17T07:11:36.2458109Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-07-17T07:11:36.2458313Z for each parameter group. Note that momentum is cycled inversely 2025-07-17T07:11:36.2458470Z to learning rate; at the peak of a cycle, momentum is 2025-07-17T07:11:36.2458609Z 'base_momentum' and learning rate is 'max_lr'. 2025-07-17T07:11:36.2458702Z Default: 0.8 2025-07-17T07:11:36.2458909Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-07-17T07:11:36.2459042Z for each parameter group. Functionally, 2025-07-17T07:11:36.2459230Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-07-17T07:11:36.2459406Z The momentum at any cycle is the difference of max_momentum 2025-07-17T07:11:36.2459542Z and some scaling of the amplitude; therefore 2025-07-17T07:11:36.2459707Z base_momentum may not actually be reached depending on 2025-07-17T07:11:36.2459882Z scaling function. Note that momentum is cycled inversely 2025-07-17T07:11:36.2460101Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-07-17T07:11:36.2460211Z and learning rate is 'base_lr' 2025-07-17T07:11:36.2460313Z Default: 0.9 2025-07-17T07:11:36.2460553Z last_epoch (int): The index of the last batch. This parameter is used when 2025-07-17T07:11:36.2460770Z resuming a training job. Since `step()` should be invoked after each 2025-07-17T07:11:36.2460989Z batch instead of after each epoch, this number represents the total 2025-07-17T07:11:36.2461204Z number of *batches* computed, not the total number of epochs computed. 2025-07-17T07:11:36.2461402Z When last_epoch=-1, the schedule is started from the beginning. 2025-07-17T07:11:36.2461494Z Default: -1 2025-07-17T07:11:36.2461569Z 2025-07-17T07:11:36.2461658Z Example: 2025-07-17T07:11:36.2461754Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2461974Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-07-17T07:11:36.2462136Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-07-17T07:11:36.2462255Z ... optimizer, 2025-07-17T07:11:36.2462342Z ... base_lr=0.01, 2025-07-17T07:11:36.2462442Z ... max_lr=0.1, 2025-07-17T07:11:36.2462534Z ... step_size_up=10, 2025-07-17T07:11:36.2462613Z ... ) 2025-07-17T07:11:36.2462772Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-07-17T07:11:36.2462869Z >>> for epoch in range(10): 2025-07-17T07:11:36.2462980Z >>> for batch in data_loader: 2025-07-17T07:11:36.2463077Z >>> train_batch(...) 2025-07-17T07:11:36.2463176Z >>> scheduler.step() 2025-07-17T07:11:36.2463261Z 2025-07-17T07:11:36.2463432Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-07-17T07:11:36.2463547Z 2025-07-17T07:11:36.2463916Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-07-17T07:11:36.2464113Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-07-17T07:11:36.2464191Z 2025-07-17T07:11:36.2464452Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2464524Z 2025-07-17T07:11:36.2464649Z warnings.warn(msg) 2025-07-17T07:11:36.2464725Z 2025-07-17T07:11:36.2464930Z --- Parse Warning: 121 / 136 --- 2025-07-17T07:11:36.2465944Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1713. 2025-07-17T07:11:36.2466204Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2466454Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-07-17T07:11:36.2466540Z 2025-07-17T07:11:36.2466712Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-07-17T07:11:36.2466961Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-07-17T07:11:36.2467095Z of epochs between two warm restarts in SGDR: 2025-07-17T07:11:36.2467167Z 2025-07-17T07:11:36.2467262Z .. math:: 2025-07-17T07:11:36.2467436Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-07-17T07:11:36.2467572Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-07-17T07:11:36.2467654Z 2025-07-17T07:11:36.2467814Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-07-17T07:11:36.2467996Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-07-17T07:11:36.2468076Z 2025-07-17T07:11:36.2468174Z It has been proposed in 2025-07-17T07:11:36.2468346Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-07-17T07:11:36.2468421Z 2025-07-17T07:11:36.2468497Z Args: 2025-07-17T07:11:36.2468638Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:36.2468794Z T_0 (int): Number of iterations until the first restart. 2025-07-17T07:11:36.2469104Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-07-17T07:11:36.2469297Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-07-17T07:11:36.2469504Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-07-17T07:11:36.2469583Z 2025-07-17T07:11:36.2469748Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-07-17T07:11:36.2469860Z https://arxiv.org/abs/1608.03983 2025-07-17T07:11:36.2469942Z 2025-07-17T07:11:36.2470024Z Example: 2025-07-17T07:11:36.2470116Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2470306Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-07-17T07:11:36.2470533Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-07-17T07:11:36.2470659Z ... optimizer, T_0=20 2025-07-17T07:11:36.2470746Z ... ) 2025-07-17T07:11:36.2470851Z >>> for epoch in range(100): 2025-07-17T07:11:36.2470940Z >>> train(...) 2025-07-17T07:11:36.2471040Z >>> validate(...) 2025-07-17T07:11:36.2471133Z >>> scheduler.step() 2025-07-17T07:11:36.2471212Z 2025-07-17T07:11:36.2471443Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-07-17T07:11:36.2471519Z 2025-07-17T07:11:36.2472022Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2472098Z 2025-07-17T07:11:36.2472190Z warnings.warn(msg) 2025-07-17T07:11:36.2472273Z 2025-07-17T07:11:36.2472464Z --- Parse Warning: 122 / 136 --- 2025-07-17T07:11:36.2473360Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1863. 2025-07-17T07:11:36.2473706Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2474036Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-07-17T07:11:36.2474123Z 2025-07-17T07:11:36.2474418Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-07-17T07:11:36.2474704Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-07-17T07:11:36.2474829Z lower than the initial learning rate. 2025-07-17T07:11:36.2475048Z This policy was initially described in the paper `Super-Convergence: 2025-07-17T07:11:36.2475261Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-07-17T07:11:36.2475338Z 2025-07-17T07:11:36.2475584Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-07-17T07:11:36.2475785Z `step` should be called after a batch has been used for training. 2025-07-17T07:11:36.2475862Z 2025-07-17T07:11:36.2475971Z This scheduler is not chainable. 2025-07-17T07:11:36.2476056Z 2025-07-17T07:11:36.2476296Z Note also that the total number of steps in the cycle can be determined in one 2025-07-17T07:11:36.2476427Z of two ways (listed in order of precedence): 2025-07-17T07:11:36.2476509Z 2025-07-17T07:11:36.2476657Z #. A value for total_steps is explicitly provided. 2025-07-17T07:11:36.2476844Z #. A number of epochs (epochs) and a number of steps per epoch 2025-07-17T07:11:36.2476955Z (steps_per_epoch) are provided. 2025-07-17T07:11:36.2477113Z In this case, the number of total steps is inferred by 2025-07-17T07:11:36.2477240Z total_steps = epochs * steps_per_epoch 2025-07-17T07:11:36.2477320Z 2025-07-17T07:11:36.2477552Z You must either provide a value for total_steps or provide a value for both 2025-07-17T07:11:36.2477701Z epochs and steps_per_epoch. 2025-07-17T07:11:36.2477778Z 2025-07-17T07:11:36.2478079Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-07-17T07:11:36.2478375Z claims that "unpublished work has shown even better results by using only two phases". To 2025-07-17T07:11:36.2478613Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-07-17T07:11:36.2478694Z 2025-07-17T07:11:36.2478773Z Args: 2025-07-17T07:11:36.2478903Z optimizer (Optimizer): Wrapped optimizer. 2025-07-17T07:11:36.2479103Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-07-17T07:11:36.2479209Z for each parameter group. 2025-07-17T07:11:36.2479407Z total_steps (int): The total number of steps in the cycle. Note that 2025-07-17T07:11:36.2479621Z if a value is not provided here, then it must be inferred by providing 2025-07-17T07:11:36.2479786Z a value for epochs and steps_per_epoch. 2025-07-17T07:11:36.2479880Z Default: None 2025-07-17T07:11:36.2480086Z epochs (int): The number of epochs to train for. This is used along 2025-07-17T07:11:36.2480327Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-07-17T07:11:36.2480465Z if a value for total_steps is not provided. 2025-07-17T07:11:36.2480556Z Default: None 2025-07-17T07:11:36.2480774Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-07-17T07:11:36.2481004Z used along with epochs in order to infer the total number of steps in the 2025-07-17T07:11:36.2481151Z cycle if a value for total_steps is not provided. 2025-07-17T07:11:36.2481246Z Default: None 2025-07-17T07:11:36.2481500Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-07-17T07:11:36.2481612Z increasing the learning rate. 2025-07-17T07:11:36.2481712Z Default: 0.3 2025-07-17T07:11:36.2481857Z anneal_strategy (str): {'cos', 'linear'} 2025-07-17T07:11:36.2482105Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-07-17T07:11:36.2482209Z linear annealing. 2025-07-17T07:11:36.2482299Z Default: 'cos' 2025-07-17T07:11:36.2482489Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-07-17T07:11:36.2482681Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-07-17T07:11:36.2482772Z Default: True 2025-07-17T07:11:36.2482987Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-07-17T07:11:36.2483187Z for each parameter group. Note that momentum is cycled inversely 2025-07-17T07:11:36.2483349Z to learning rate; at the peak of a cycle, momentum is 2025-07-17T07:11:36.2483498Z 'base_momentum' and learning rate is 'max_lr'. 2025-07-17T07:11:36.2483591Z Default: 0.85 2025-07-17T07:11:36.2483799Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-07-17T07:11:36.2483936Z for each parameter group. Functionally, 2025-07-17T07:11:36.2484129Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-07-17T07:11:36.2484258Z Note that momentum is cycled inversely 2025-07-17T07:11:36.2484461Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-07-17T07:11:36.2484571Z and learning rate is 'base_lr' 2025-07-17T07:11:36.2484672Z Default: 0.95 2025-07-17T07:11:36.2484855Z div_factor (float): Determines the initial learning rate via 2025-07-17T07:11:36.2484967Z initial_lr = max_lr/div_factor 2025-07-17T07:11:36.2485062Z Default: 25 2025-07-17T07:11:36.2485287Z final_div_factor (float): Determines the minimum learning rate via 2025-07-17T07:11:36.2485417Z min_lr = initial_lr/final_div_factor 2025-07-17T07:11:36.2485507Z Default: 1e4 2025-07-17T07:11:36.2485750Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-07-17T07:11:36.2486011Z learning rate according to 'final_div_factor' instead of modifying the second 2025-07-17T07:11:36.2486248Z phase (the first two phases will be symmetrical about the step indicated by 2025-07-17T07:11:36.2486336Z 'pct_start'). 2025-07-17T07:11:36.2486563Z last_epoch (int): The index of the last batch. This parameter is used when 2025-07-17T07:11:36.2486774Z resuming a training job. Since `step()` should be invoked after each 2025-07-17T07:11:36.2487021Z batch instead of after each epoch, this number represents the total 2025-07-17T07:11:36.2487240Z number of *batches* computed, not the total number of epochs computed. 2025-07-17T07:11:36.2487431Z When last_epoch=-1, the schedule is started from the beginning. 2025-07-17T07:11:36.2487524Z Default: -1 2025-07-17T07:11:36.2487597Z 2025-07-17T07:11:36.2487677Z Example: 2025-07-17T07:11:36.2487778Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2487925Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-07-17T07:11:36.2488153Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-07-17T07:11:36.2488313Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-07-17T07:11:36.2488521Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-07-17T07:11:36.2488630Z ... ) 2025-07-17T07:11:36.2488731Z >>> for epoch in range(10): 2025-07-17T07:11:36.2488835Z >>> for batch in data_loader: 2025-07-17T07:11:36.2488938Z >>> train_batch(...) 2025-07-17T07:11:36.2489063Z >>> optimizer.step() 2025-07-17T07:11:36.2489161Z >>> scheduler.step() 2025-07-17T07:11:36.2489241Z 2025-07-17T07:11:36.2489405Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-07-17T07:11:36.2489476Z 2025-07-17T07:11:36.2489769Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-07-17T07:11:36.2489887Z https://arxiv.org/abs/1708.07120 2025-07-17T07:11:36.2489966Z 2025-07-17T07:11:36.2490215Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2490290Z 2025-07-17T07:11:36.2490388Z warnings.warn(msg) 2025-07-17T07:11:36.2490463Z 2025-07-17T07:11:36.2490655Z --- Parse Warning: 123 / 136 --- 2025-07-17T07:11:36.2491617Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/optimizer.py line=867. 2025-07-17T07:11:36.2491878Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2491985Z Load the optimizer state. 2025-07-17T07:11:36.2492061Z 2025-07-17T07:11:36.2492142Z Args: 2025-07-17T07:11:36.2492354Z state_dict (dict): optimizer state. Should be an object returned 2025-07-17T07:11:36.2492473Z from a call to :meth:`state_dict`. 2025-07-17T07:11:36.2492550Z 2025-07-17T07:11:36.2492647Z .. warning:: 2025-07-17T07:11:36.2492985Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-07-17T07:11:36.2493194Z as calling it beforehand will overwrite the loaded learning rates. 2025-07-17T07:11:36.2493281Z 2025-07-17T07:11:36.2493390Z .. note:: 2025-07-17T07:11:36.2493693Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-07-17T07:11:36.2493868Z in :meth:`state_dict`) will not affect the loading process. 2025-07-17T07:11:36.2494191Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-07-17T07:11:36.2494355Z differ from those initialized in the optimizer), 2025-07-17T07:11:36.2494654Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-07-17T07:11:36.2494744Z accordingly. 2025-07-17T07:11:36.2495038Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-07-17T07:11:36.2495460Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-07-17T07:11:36.2495667Z the optimizer ``param_names`` will remain unchanged. 2025-07-17T07:11:36.2495747Z 2025-07-17T07:11:36.2495830Z Example: 2025-07-17T07:11:36.2495938Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2496057Z >>> model = torch.nn.Linear(10, 10) 2025-07-17T07:11:36.2496220Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-07-17T07:11:36.2496387Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-07-17T07:11:36.2496475Z ... optim, 2025-07-17T07:11:36.2496581Z ... start_factor=0.1, 2025-07-17T07:11:36.2496674Z ... end_factor=1, 2025-07-17T07:11:36.2496764Z ... total_iters=20, 2025-07-17T07:11:36.2496855Z ... ) 2025-07-17T07:11:36.2497041Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-07-17T07:11:36.2497156Z ... optim, 2025-07-17T07:11:36.2497251Z ... T_max=80, 2025-07-17T07:11:36.2497348Z ... eta_min=3e-5, 2025-07-17T07:11:36.2497425Z ... ) 2025-07-17T07:11:36.2497603Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-07-17T07:11:36.2497693Z ... optim, 2025-07-17T07:11:36.2497828Z ... schedulers=[scheduler1, scheduler2], 2025-07-17T07:11:36.2497924Z ... milestones=[20], 2025-07-17T07:11:36.2498003Z ... ) 2025-07-17T07:11:36.2498153Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-07-17T07:11:36.2498354Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-07-17T07:11:36.2498514Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-07-17T07:11:36.2498597Z 2025-07-17T07:11:36.2498680Z 2025-07-17T07:11:36.2498922Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2499008Z 2025-07-17T07:11:36.2499106Z warnings.warn(msg) 2025-07-17T07:11:36.2499185Z 2025-07-17T07:11:36.2499381Z --- Parse Warning: 124 / 136 --- 2025-07-17T07:11:36.2500276Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=120. 2025-07-17T07:11:36.2500537Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2500897Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-07-17T07:11:36.2500971Z 2025-07-17T07:11:36.2501217Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-07-17T07:11:36.2501430Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-07-17T07:11:36.2501652Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-07-17T07:11:36.2501760Z (UAI 2018). 2025-07-17T07:11:36.2501837Z 2025-07-17T07:11:36.2502062Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-07-17T07:11:36.2502302Z but using exponential weights instead of equal weights across iterations. 2025-07-17T07:11:36.2502375Z 2025-07-17T07:11:36.2502617Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-07-17T07:11:36.2502839Z on the device :attr:`device` and allows to compute running averages of the 2025-07-17T07:11:36.2502941Z parameters of the :attr:`model`. 2025-07-17T07:11:36.2503022Z 2025-07-17T07:11:36.2503100Z Args: 2025-07-17T07:11:36.2503253Z model (torch.nn.Module): model to use with SWA/EMA 2025-07-17T07:11:36.2503485Z device (torch.device, optional): if provided, the averaged model will be 2025-07-17T07:11:36.2503631Z stored on the :attr:`device` 2025-07-17T07:11:36.2503842Z avg_fn (function, optional): the averaging function used to update 2025-07-17T07:11:36.2504040Z parameters; the function must take in the current value of the 2025-07-17T07:11:36.2504257Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-07-17T07:11:36.2504457Z parameter, and the number of models already averaged; if None, 2025-07-17T07:11:36.2504613Z an equally weighted average is used (default: None) 2025-07-17T07:11:36.2504838Z multi_avg_fn (function, optional): the averaging function used to update 2025-07-17T07:11:36.2505071Z parameters inplace; the function must take in the current values of the 2025-07-17T07:11:36.2505336Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-07-17T07:11:36.2505591Z parameters as a list, and the number of models already averaged; if None, 2025-07-17T07:11:36.2505752Z an equally weighted average is used (default: None) 2025-07-17T07:11:36.2505975Z use_buffers (bool): if ``True``, it will compute running averages for 2025-07-17T07:11:36.2506203Z both the parameters and the buffers of the model. (default: ``False``) 2025-07-17T07:11:36.2506279Z 2025-07-17T07:11:36.2506363Z Example: 2025-07-17T07:11:36.2506492Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.2506616Z >>> loader, optimizer, model, loss_fn = ... 2025-07-17T07:11:36.2506789Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-07-17T07:11:36.2507009Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-07-17T07:11:36.2507115Z >>> T_max=300) 2025-07-17T07:11:36.2507211Z >>> swa_start = 160 2025-07-17T07:11:36.2507351Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-07-17T07:11:36.2507457Z >>> for i in range(300): 2025-07-17T07:11:36.2507569Z >>> for input, target in loader: 2025-07-17T07:11:36.2507679Z >>> optimizer.zero_grad() 2025-07-17T07:11:36.2507814Z >>> loss_fn(model(input), target).backward() 2025-07-17T07:11:36.2507917Z >>> optimizer.step() 2025-07-17T07:11:36.2508012Z >>> if i > swa_start: 2025-07-17T07:11:36.2508143Z >>> swa_model.update_parameters(model) 2025-07-17T07:11:36.2508249Z >>> swa_scheduler.step() 2025-07-17T07:11:36.2508331Z >>> else: 2025-07-17T07:11:36.2508435Z >>> scheduler.step() 2025-07-17T07:11:36.2508514Z >>> 2025-07-17T07:11:36.2508669Z >>> # Update bn statistics for the swa_model at the end 2025-07-17T07:11:36.2508825Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-07-17T07:11:36.2508904Z 2025-07-17T07:11:36.2509231Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-07-17T07:11:36.2509428Z If no averaging function is provided, the default is to compute 2025-07-17T07:11:36.2509570Z equally-weighted average of the weights (SWA). 2025-07-17T07:11:36.2509648Z 2025-07-17T07:11:36.2509728Z Example: 2025-07-17T07:11:36.2509848Z >>> # xdoctest: +SKIP("undefined variables") 2025-07-17T07:11:36.2510061Z >>> # Compute exponential moving averages of the weights and buffers 2025-07-17T07:11:36.2510225Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-07-17T07:11:36.2510444Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-07-17T07:11:36.2510519Z 2025-07-17T07:11:36.2510600Z .. note:: 2025-07-17T07:11:36.2510824Z When using SWA/EMA with models containing Batch Normalization you may 2025-07-17T07:11:36.2511052Z need to update the activation statistics for Batch Normalization. 2025-07-17T07:11:36.2511283Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-07-17T07:11:36.2511515Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-07-17T07:11:36.2511753Z statistics in a post-training step by passing data through the model. The 2025-07-17T07:11:36.2511998Z second does it during the parameter update phase by averaging all buffers. 2025-07-17T07:11:36.2512242Z Empirical evidence has shown that updating the statistics in normalization 2025-07-17T07:11:36.2512466Z layers increases accuracy, but you may wish to empirically test which 2025-07-17T07:11:36.2512622Z approach yields the best results in your problem. 2025-07-17T07:11:36.2512698Z 2025-07-17T07:11:36.2512806Z .. note:: 2025-07-17T07:11:36.2513066Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-07-17T07:11:36.2513146Z 2025-07-17T07:11:36.2513235Z .. note:: 2025-07-17T07:11:36.2513456Z When :meth:`update_parameters` is called for the first time (i.e. 2025-07-17T07:11:36.2513639Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-07-17T07:11:36.2513846Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-07-17T07:11:36.2514029Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-07-17T07:11:36.2514135Z to update the parameters. 2025-07-17T07:11:36.2514219Z 2025-07-17T07:11:36.2514437Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-07-17T07:11:36.2514563Z https://arxiv.org/abs/1803.05407 2025-07-17T07:11:36.2514795Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-07-17T07:11:36.2514885Z Average: 2025-07-17T07:11:36.2515002Z https://arxiv.org/abs/1806.05594 2025-07-17T07:11:36.2515199Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-07-17T07:11:36.2515313Z https://arxiv.org/abs/1904.11943 2025-07-17T07:11:36.2515538Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-07-17T07:11:36.2515632Z Generalizes Well: 2025-07-17T07:11:36.2515743Z https://arxiv.org/abs/2001.02312 2025-07-17T07:11:36.2515835Z .. _Polyak averaging: 2025-07-17T07:11:36.2515999Z https://paperswithcode.com/method/polyak-averaging 2025-07-17T07:11:36.2516079Z 2025-07-17T07:11:36.2516319Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2516392Z 2025-07-17T07:11:36.2516488Z warnings.warn(msg) 2025-07-17T07:11:36.2516561Z 2025-07-17T07:11:36.2516749Z --- Parse Warning: 125 / 136 --- 2025-07-17T07:11:36.2517642Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=375. 2025-07-17T07:11:36.2517902Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2518120Z Anneals the learning rate in each parameter group to a fixed value. 2025-07-17T07:11:36.2518193Z 2025-07-17T07:11:36.2518421Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-07-17T07:11:36.2518638Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-07-17T07:11:36.2518712Z 2025-07-17T07:11:36.2518788Z Args: 2025-07-17T07:11:36.2518963Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-07-17T07:11:36.2519165Z swa_lrs (float or list): the learning rate value for all param groups 2025-07-17T07:11:36.2519318Z together or separately for each group. 2025-07-17T07:11:36.2519525Z annealing_epochs (int): number of epochs in the annealing phase 2025-07-17T07:11:36.2519614Z (default: 10) 2025-07-17T07:11:36.2519831Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-07-17T07:11:36.2520035Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-07-17T07:11:36.2520128Z (default: "cos") 2025-07-17T07:11:36.2520309Z last_epoch (int): the index of the last epoch (default: -1) 2025-07-17T07:11:36.2520385Z 2025-07-17T07:11:36.2520560Z The :class:`SWALR` scheduler can be used together with other 2025-07-17T07:11:36.2520780Z schedulers to switch to a constant learning rate late in the training 2025-07-17T07:11:36.2520879Z as in the example below. 2025-07-17T07:11:36.2520956Z 2025-07-17T07:11:36.2521042Z Example: 2025-07-17T07:11:36.2521195Z >>> # xdoctest: +SKIP("Undefined variables") 2025-07-17T07:11:36.2521319Z >>> loader, optimizer, model = ... 2025-07-17T07:11:36.2521434Z >>> lr_lambda = lambda epoch: 0.9 2025-07-17T07:11:36.2521678Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-07-17T07:11:36.2521792Z >>> lr_lambda=lr_lambda) 2025-07-17T07:11:36.2521957Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-07-17T07:11:36.2522130Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-07-17T07:11:36.2522235Z >>> swa_start = 160 2025-07-17T07:11:36.2522330Z >>> for i in range(300): 2025-07-17T07:11:36.2522443Z >>> for input, target in loader: 2025-07-17T07:11:36.2522563Z >>> optimizer.zero_grad() 2025-07-17T07:11:36.2522693Z >>> loss_fn(model(input), target).backward() 2025-07-17T07:11:36.2522811Z >>> optimizer.step() 2025-07-17T07:11:36.2522905Z >>> if i > swa_start: 2025-07-17T07:11:36.2523014Z >>> swa_scheduler.step() 2025-07-17T07:11:36.2523105Z >>> else: 2025-07-17T07:11:36.2523203Z >>> scheduler.step() 2025-07-17T07:11:36.2523276Z 2025-07-17T07:11:36.2523504Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-07-17T07:11:36.2523617Z https://arxiv.org/abs/1803.05407 2025-07-17T07:11:36.2523693Z 2025-07-17T07:11:36.2523952Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2524026Z 2025-07-17T07:11:36.2524127Z warnings.warn(msg) 2025-07-17T07:11:36.2524200Z 2025-07-17T07:11:36.2524385Z --- Parse Warning: 126 / 136 --- 2025-07-17T07:11:36.2525301Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1331. 2025-07-17T07:11:36.2525595Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2525750Z Asserts that ``actual`` and ``expected`` are close. 2025-07-17T07:11:36.2525836Z 2025-07-17T07:11:36.2526202Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-07-17T07:11:36.2526294Z 2025-07-17T07:11:36.2526376Z .. math:: 2025-07-17T07:11:36.2526453Z 2025-07-17T07:11:36.2526829Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-07-17T07:11:36.2526903Z 2025-07-17T07:11:36.2527245Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-07-17T07:11:36.2527454Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-07-17T07:11:36.2527555Z 2025-07-17T07:11:36.2527754Z In addition, they are only considered close if they have the same 2025-07-17T07:11:36.2527835Z 2025-07-17T07:11:36.2528023Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-07-17T07:11:36.2528156Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-07-17T07:11:36.2528292Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-07-17T07:11:36.2528413Z - stride (if ``check_stride`` is ``True``). 2025-07-17T07:11:36.2528497Z 2025-07-17T07:11:36.2528792Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-07-17T07:11:36.2528869Z 2025-07-17T07:11:36.2529239Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-07-17T07:11:36.2529611Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-07-17T07:11:36.2529875Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-07-17T07:11:36.2530286Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-07-17T07:11:36.2530364Z 2025-07-17T07:11:36.2530653Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-07-17T07:11:36.2531002Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-07-17T07:11:36.2531098Z definition above. 2025-07-17T07:11:36.2531181Z 2025-07-17T07:11:36.2531482Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-07-17T07:11:36.2531860Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-07-17T07:11:36.2532227Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-07-17T07:11:36.2532611Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-07-17T07:11:36.2532842Z their elements are considered close according to the above definition. 2025-07-17T07:11:36.2532916Z 2025-07-17T07:11:36.2533008Z .. note:: 2025-07-17T07:11:36.2533084Z 2025-07-17T07:11:36.2533412Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-07-17T07:11:36.2533747Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-07-17T07:11:36.2534021Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-07-17T07:11:36.2534098Z 2025-07-17T07:11:36.2534184Z Args: 2025-07-17T07:11:36.2534288Z actual (Any): Actual input. 2025-07-17T07:11:36.2534400Z expected (Any): Expected input. 2025-07-17T07:11:36.2534803Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-07-17T07:11:36.2534962Z are allowed. Otherwise type equality is required. 2025-07-17T07:11:36.2535449Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-07-17T07:11:36.2535707Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-07-17T07:11:36.2536070Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-07-17T07:11:36.2536335Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-07-17T07:11:36.2536578Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-07-17T07:11:36.2536907Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-07-17T07:11:36.2537153Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-07-17T07:11:36.2537383Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-07-17T07:11:36.2537742Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-07-17T07:11:36.2538084Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-07-17T07:11:36.2538252Z :func:`torch.promote_types`) before being compared. 2025-07-17T07:11:36.2538606Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-07-17T07:11:36.2538938Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-07-17T07:11:36.2539065Z compared. 2025-07-17T07:11:36.2539451Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-07-17T07:11:36.2539804Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-07-17T07:11:36.2540169Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-07-17T07:11:36.2540277Z should return the new message. 2025-07-17T07:11:36.2540363Z 2025-07-17T07:11:36.2540442Z Raises: 2025-07-17T07:11:36.2540667Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-07-17T07:11:36.2540841Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-07-17T07:11:36.2541166Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-07-17T07:11:36.2541547Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-07-17T07:11:36.2541647Z different types. 2025-07-17T07:11:36.2542006Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-07-17T07:11:36.2542387Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-07-17T07:11:36.2542704Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-07-17T07:11:36.2542997Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-07-17T07:11:36.2543123Z :attr:`~torch.Tensor.layout`. 2025-07-17T07:11:36.2543339Z AssertionError: If only one of corresponding tensors is quantized. 2025-07-17T07:11:36.2543737Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-07-17T07:11:36.2544058Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-07-17T07:11:36.2544171Z :attr:`~torch.Tensor.device`. 2025-07-17T07:11:36.2544512Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-07-17T07:11:36.2544873Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-07-17T07:11:36.2545242Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-07-17T07:11:36.2545319Z 2025-07-17T07:11:36.2545675Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-07-17T07:11:36.2545834Z ``dtype``'s, the maximum of both tolerances is used. 2025-07-17T07:11:36.2545934Z 2025-07-17T07:11:36.2546060Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2546193Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-07-17T07:11:36.2546297Z +===========================+============+==========+ 2025-07-17T07:11:36.2546439Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-07-17T07:11:36.2546559Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2546691Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-07-17T07:11:36.2546820Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2546951Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:36.2547067Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2547202Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-07-17T07:11:36.2547358Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2547490Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-07-17T07:11:36.2547616Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2547772Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:36.2547897Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2548029Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-07-17T07:11:36.2548146Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2548281Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:36.2548397Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2548522Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:36.2548647Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2548778Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:36.2548909Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2549036Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:36.2549152Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2549286Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-07-17T07:11:36.2549406Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2549517Z | other | ``0.0`` | ``0.0`` | 2025-07-17T07:11:36.2549642Z +---------------------------+------------+----------+ 2025-07-17T07:11:36.2549719Z 2025-07-17T07:11:36.2549809Z .. note:: 2025-07-17T07:11:36.2549885Z 2025-07-17T07:11:36.2550261Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-07-17T07:11:36.2550628Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-07-17T07:11:36.2550916Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-07-17T07:11:36.2550990Z 2025-07-17T07:11:36.2551097Z >>> import functools 2025-07-17T07:11:36.2551355Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-07-17T07:11:36.2551467Z >>> assert_equal(1e-9, 1e-10) 2025-07-17T07:11:36.2551583Z Traceback (most recent call last): 2025-07-17T07:11:36.2551659Z ... 2025-07-17T07:11:36.2551790Z AssertionError: Scalars are not equal! 2025-07-17T07:11:36.2551878Z 2025-07-17T07:11:36.2551984Z Expected 1e-10 but got 1e-09. 2025-07-17T07:11:36.2552119Z Absolute difference: 9.000000000000001e-10 2025-07-17T07:11:36.2552220Z Relative difference: 9.0 2025-07-17T07:11:36.2552295Z 2025-07-17T07:11:36.2552392Z Examples: 2025-07-17T07:11:36.2552525Z >>> # tensor to tensor comparison 2025-07-17T07:11:36.2552657Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-07-17T07:11:36.2552801Z >>> actual = torch.acos(torch.cos(expected)) 2025-07-17T07:11:36.2552942Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:36.2553029Z 2025-07-17T07:11:36.2553134Z >>> # scalar to scalar comparison 2025-07-17T07:11:36.2553224Z >>> import math 2025-07-17T07:11:36.2553339Z >>> expected = math.sqrt(2.0) 2025-07-17T07:11:36.2553439Z >>> actual = 2.0 / math.sqrt(2.0) 2025-07-17T07:11:36.2553578Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:36.2553665Z 2025-07-17T07:11:36.2553785Z >>> # numpy array to numpy array comparison 2025-07-17T07:11:36.2553881Z >>> import numpy as np 2025-07-17T07:11:36.2554009Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-07-17T07:11:36.2554160Z >>> actual = np.arccos(np.cos(expected)) 2025-07-17T07:11:36.2554310Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:36.2554384Z 2025-07-17T07:11:36.2554523Z >>> # sequence to sequence comparison 2025-07-17T07:11:36.2554633Z >>> import numpy as np 2025-07-17T07:11:36.2554883Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-07-17T07:11:36.2555011Z >>> # length and their elements have to match. 2025-07-17T07:11:36.2555174Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-07-17T07:11:36.2555275Z >>> actual = tuple(expected) 2025-07-17T07:11:36.2555413Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:36.2555499Z 2025-07-17T07:11:36.2555613Z >>> # mapping to mapping comparison 2025-07-17T07:11:36.2555742Z >>> from collections import OrderedDict 2025-07-17T07:11:36.2555841Z >>> import numpy as np 2025-07-17T07:11:36.2555944Z >>> foo = torch.tensor(1.0) 2025-07-17T07:11:36.2556045Z >>> bar = 2.0 2025-07-17T07:11:36.2556140Z >>> baz = np.array(3.0) 2025-07-17T07:11:36.2556389Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-07-17T07:11:36.2556601Z >>> # have to have the same set of keys and their elements have to match. 2025-07-17T07:11:36.2556800Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-07-17T07:11:36.2556941Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-07-17T07:11:36.2557082Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:36.2557157Z 2025-07-17T07:11:36.2557289Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-07-17T07:11:36.2557393Z >>> actual = expected.clone() 2025-07-17T07:11:36.2557557Z >>> # By default, directly related instances can be compared 2025-07-17T07:11:36.2557789Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-07-17T07:11:36.2558009Z >>> # This check can be made more strict with allow_subclasses=False 2025-07-17T07:11:36.2558125Z >>> torch.testing.assert_close( 2025-07-17T07:11:36.2558331Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-07-17T07:11:36.2558409Z ... ) 2025-07-17T07:11:36.2558534Z Traceback (most recent call last): 2025-07-17T07:11:36.2558611Z ... 2025-07-17T07:11:36.2558813Z TypeError: No comparison pair was able to handle inputs of type 2025-07-17T07:11:36.2559041Z and . 2025-07-17T07:11:36.2559261Z >>> # If the inputs are not directly related, they are never considered close 2025-07-17T07:11:36.2559430Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-07-17T07:11:36.2559585Z Traceback (most recent call last): 2025-07-17T07:11:36.2559663Z ... 2025-07-17T07:11:36.2559965Z TypeError: No comparison pair was able to handle inputs of type 2025-07-17T07:11:36.2560066Z and . 2025-07-17T07:11:36.2560326Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-07-17T07:11:36.2560453Z >>> # their type if check_dtype=False. 2025-07-17T07:11:36.2560616Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-07-17T07:11:36.2560690Z 2025-07-17T07:11:36.2560799Z >>> # NaN != NaN by default. 2025-07-17T07:11:36.2560919Z >>> expected = torch.tensor(float("Nan")) 2025-07-17T07:11:36.2561024Z >>> actual = expected.clone() 2025-07-17T07:11:36.2561177Z >>> torch.testing.assert_close(actual, expected) 2025-07-17T07:11:36.2561315Z Traceback (most recent call last): 2025-07-17T07:11:36.2561404Z ... 2025-07-17T07:11:36.2561523Z AssertionError: Scalars are not close! 2025-07-17T07:11:36.2561607Z 2025-07-17T07:11:36.2561750Z Expected nan but got nan. 2025-07-17T07:11:36.2561885Z Absolute difference: nan (up to 1e-05 allowed) 2025-07-17T07:11:36.2562029Z Relative difference: nan (up to 1.3e-06 allowed) 2025-07-17T07:11:36.2562233Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-07-17T07:11:36.2562306Z 2025-07-17T07:11:36.2562429Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-07-17T07:11:36.2562549Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-07-17T07:11:36.2562686Z >>> # The default error message can be overwritten. 2025-07-17T07:11:36.2562987Z >>> torch.testing.assert_close(actual, expected, msg="Argh, the tensors are not close!") 2025-07-17T07:11:36.2563101Z Traceback (most recent call last): 2025-07-17T07:11:36.2563181Z ... 2025-07-17T07:11:36.2563344Z AssertionError: Argh, the tensors are not close! 2025-07-17T07:11:36.2563565Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-07-17T07:11:36.2563662Z >>> # extra information 2025-07-17T07:11:36.2563778Z >>> torch.testing.assert_close( 2025-07-17T07:11:36.2563970Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-07-17T07:11:36.2564062Z ... ) 2025-07-17T07:11:36.2564211Z Traceback (most recent call last): 2025-07-17T07:11:36.2564330Z ... 2025-07-17T07:11:36.2564472Z AssertionError: Header 2025-07-17T07:11:36.2564554Z 2025-07-17T07:11:36.2564657Z Tensor-likes are not close! 2025-07-17T07:11:36.2564754Z 2025-07-17T07:11:36.2564861Z Mismatched elements: 2 / 3 (66.7%) 2025-07-17T07:11:36.2565088Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-07-17T07:11:36.2565357Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-07-17T07:11:36.2565446Z 2025-07-17T07:11:36.2565536Z Footer 2025-07-17T07:11:36.2565613Z 2025-07-17T07:11:36.2565862Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2565946Z 2025-07-17T07:11:36.2566036Z warnings.warn(msg) 2025-07-17T07:11:36.2566111Z 2025-07-17T07:11:36.2566335Z --- Parse Warning: 127 / 136 --- 2025-07-17T07:11:36.2567261Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=134. 2025-07-17T07:11:36.2567521Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2567702Z Register a container-like type as pytree node. 2025-07-17T07:11:36.2567780Z 2025-07-17T07:11:36.2567869Z Args: 2025-07-17T07:11:36.2568054Z cls (type): A Python type to treat as an internal pytree node. 2025-07-17T07:11:36.2568323Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-07-17T07:11:36.2568580Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-07-17T07:11:36.2568867Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-07-17T07:11:36.2568982Z passed to the ``unflatten_fn``. 2025-07-17T07:11:36.2569263Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-07-17T07:11:36.2569519Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-07-17T07:11:36.2569714Z The function should return an instance of ``cls``. 2025-07-17T07:11:36.2569977Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-07-17T07:11:36.2570164Z qualified name used when serializing the tree spec. 2025-07-17T07:11:36.2570484Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-07-17T07:11:36.2570759Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-07-17T07:11:36.2571038Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-07-17T07:11:36.2571339Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-07-17T07:11:36.2571598Z how to convert the custom json dumpable representation of the context back to the 2025-07-17T07:11:36.2572007Z original context. This is used for json deserialization, which is being used in 2025-07-17T07:11:36.2572127Z :mod:`torch.export` right now. 2025-07-17T07:11:36.2572205Z 2025-07-17T07:11:36.2572305Z Example:: 2025-07-17T07:11:36.2572381Z 2025-07-17T07:11:36.2572483Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2572639Z >>> # Registry a Python type with lambda functions 2025-07-17T07:11:36.2572742Z >>> register_pytree_node( 2025-07-17T07:11:36.2572840Z ... set, 2025-07-17T07:11:36.2572961Z ... lambda s: (sorted(s), None, None), 2025-07-17T07:11:36.2573085Z ... lambda children, _: set(children), 2025-07-17T07:11:36.2573181Z ... ) 2025-07-17T07:11:36.2573257Z 2025-07-17T07:11:36.2573505Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2573595Z 2025-07-17T07:11:36.2573686Z warnings.warn(msg) 2025-07-17T07:11:36.2573763Z 2025-07-17T07:11:36.2573968Z --- Parse Warning: 128 / 136 --- 2025-07-17T07:11:36.2575009Z /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=1218. 2025-07-17T07:11:36.2575374Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2575451Z 2025-07-17T07:11:36.2575662Z Context passed to policy function during selective checkpointing. 2025-07-17T07:11:36.2575752Z 2025-07-17T07:11:36.2575971Z This class is used to pass relevant metadata to the policy function during 2025-07-17T07:11:36.2576226Z selective checkpointing. The metadata includes whether the current invocation 2025-07-17T07:11:36.2576393Z of the policy function is during recomputation or not. 2025-07-17T07:11:36.2576468Z 2025-07-17T07:11:36.2576560Z Example: 2025-07-17T07:11:36.2576716Z >>> # xdoctest: +SKIP(stub) 2025-07-17T07:11:36.2576791Z >>> 2025-07-17T07:11:36.2576923Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-07-17T07:11:36.2577022Z >>> print(ctx.is_recompute) 2025-07-17T07:11:36.2577100Z >>> 2025-07-17T07:11:36.2577384Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-07-17T07:11:36.2577459Z >>> 2025-07-17T07:11:36.2577597Z >>> out = torch.utils.checkpoint.checkpoint( 2025-07-17T07:11:36.2577692Z >>> fn, x, y, 2025-07-17T07:11:36.2577788Z >>> use_reentrant=False, 2025-07-17T07:11:36.2577886Z >>> context_fn=context_fn, 2025-07-17T07:11:36.2577972Z >>> ) 2025-07-17T07:11:36.2578045Z 2025-07-17T07:11:36.2578304Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2578377Z 2025-07-17T07:11:36.2578465Z warnings.warn(msg) 2025-07-17T07:11:36.2578615Z 2025-07-17T07:11:36.2578803Z --- Parse Warning: 129 / 136 --- 2025-07-17T07:11:36.2579849Z /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=1358. 2025-07-17T07:11:36.2580119Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2580192Z 2025-07-17T07:11:36.2580434Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-07-17T07:11:36.2580510Z 2025-07-17T07:11:36.2580717Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-07-17T07:11:36.2580886Z operations are recomputed during the backward pass. 2025-07-17T07:11:36.2580959Z 2025-07-17T07:11:36.2581035Z Args: 2025-07-17T07:11:36.2581161Z policy_fn_or_list (Callable or List): 2025-07-17T07:11:36.2581323Z - If a policy function is provided, it should accept a 2025-07-17T07:11:36.2581569Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-07-17T07:11:36.2581788Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-07-17T07:11:36.2582025Z indicating whether the execution of the op should be recomputed or not. 2025-07-17T07:11:36.2582234Z - If a list of operations is provided, it is equivalent to a policy 2025-07-17T07:11:36.2582412Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-07-17T07:11:36.2582622Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-07-17T07:11:36.2582720Z operations. 2025-07-17T07:11:36.2582926Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-07-17T07:11:36.2583133Z raised if any tensors cached by selective activation checkpoint are 2025-07-17T07:11:36.2583350Z mutated in order to ensure correctness. If set to `True`, this check 2025-07-17T07:11:36.2583438Z is disabled. 2025-07-17T07:11:36.2583528Z Returns: 2025-07-17T07:11:36.2583658Z A tuple of two context managers. 2025-07-17T07:11:36.2583732Z 2025-07-17T07:11:36.2583822Z Example: 2025-07-17T07:11:36.2583924Z >>> # xdoctest: +REQUIRES(LINUX) 2025-07-17T07:11:36.2584015Z >>> import functools 2025-07-17T07:11:36.2584103Z >>> 2025-07-17T07:11:36.2584222Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-07-17T07:11:36.2584340Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-07-17T07:11:36.2584429Z >>> 2025-07-17T07:11:36.2584515Z >>> ops_to_save = [ 2025-07-17T07:11:36.2584623Z >>> torch.ops.aten.mm.default, 2025-07-17T07:11:36.2584710Z >>> ] 2025-07-17T07:11:36.2584783Z >>> 2025-07-17T07:11:36.2584905Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-07-17T07:11:36.2585011Z >>> if op in ops_to_save: 2025-07-17T07:11:36.2585165Z >>> return CheckpointPolicy.MUST_SAVE 2025-07-17T07:11:36.2585255Z >>> else: 2025-07-17T07:11:36.2585392Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-07-17T07:11:36.2585473Z >>> 2025-07-17T07:11:36.2585752Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-07-17T07:11:36.2585827Z >>> 2025-07-17T07:11:36.2585919Z >>> # or equivalently 2025-07-17T07:11:36.2586205Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-07-17T07:11:36.2586283Z >>> 2025-07-17T07:11:36.2586369Z >>> def fn(x, y): 2025-07-17T07:11:36.2586575Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-07-17T07:11:36.2586654Z >>> 2025-07-17T07:11:36.2586795Z >>> out = torch.utils.checkpoint.checkpoint( 2025-07-17T07:11:36.2586881Z >>> fn, x, y, 2025-07-17T07:11:36.2587006Z >>> use_reentrant=False, 2025-07-17T07:11:36.2587118Z >>> context_fn=context_fn, 2025-07-17T07:11:36.2587196Z >>> ) 2025-07-17T07:11:36.2587277Z 2025-07-17T07:11:36.2587557Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2587634Z 2025-07-17T07:11:36.2587728Z warnings.warn(msg) 2025-07-17T07:11:36.2587812Z 2025-07-17T07:11:36.2587998Z --- Parse Warning: 130 / 136 --- 2025-07-17T07:11:36.2588925Z /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=1147. 2025-07-17T07:11:36.2589185Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2589263Z 2025-07-17T07:11:36.2589411Z Create a :class:`setuptools.Extension` for C++. 2025-07-17T07:11:36.2589490Z 2025-07-17T07:11:36.2589731Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T07:11:36.2589955Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-07-17T07:11:36.2590032Z 2025-07-17T07:11:36.2590233Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T07:11:36.2590380Z constructor. Full list arguments can be found at 2025-07-17T07:11:36.2590708Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-07-17T07:11:36.2590788Z 2025-07-17T07:11:36.2590878Z .. warning:: 2025-07-17T07:11:36.2591104Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T07:11:36.2591316Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T07:11:36.2591510Z the user's responsibility in their library to not use APIs from 2025-07-17T07:11:36.2591735Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T07:11:36.2591957Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T07:11:36.2592197Z example, to give access to custom ops from python, the library should 2025-07-17T07:11:36.2592329Z register the ops through the dispatcher. 2025-07-17T07:11:36.2592406Z 2025-07-17T07:11:36.2592623Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T07:11:36.2592825Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T07:11:36.2593028Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T07:11:36.2593232Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T07:11:36.2593450Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T07:11:36.2593659Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T07:11:36.2593768Z CPython version of choice. 2025-07-17T07:11:36.2593868Z 2025-07-17T07:11:36.2593947Z Example: 2025-07-17T07:11:36.2594049Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2594197Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:36.2594303Z >>> from setuptools import setup 2025-07-17T07:11:36.2594522Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-07-17T07:11:36.2594603Z >>> setup( 2025-07-17T07:11:36.2594691Z ... name='extension', 2025-07-17T07:11:36.2594784Z ... ext_modules=[ 2025-07-17T07:11:36.2594876Z ... CppExtension( 2025-07-17T07:11:36.2594976Z ... name='extension', 2025-07-17T07:11:36.2595092Z ... sources=['extension.cpp'], 2025-07-17T07:11:36.2595203Z ... extra_compile_args=['-g'], 2025-07-17T07:11:36.2595355Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-07-17T07:11:36.2595474Z ... ], 2025-07-17T07:11:36.2595562Z ... cmdclass={ 2025-07-17T07:11:36.2595674Z ... 'build_ext': BuildExtension 2025-07-17T07:11:36.2595753Z ... }) 2025-07-17T07:11:36.2595823Z 2025-07-17T07:11:36.2596101Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2596176Z 2025-07-17T07:11:36.2596265Z warnings.warn(msg) 2025-07-17T07:11:36.2596347Z 2025-07-17T07:11:36.2596525Z --- Parse Warning: 131 / 136 --- 2025-07-17T07:11:36.2597454Z /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=1217. 2025-07-17T07:11:36.2597714Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2597784Z 2025-07-17T07:11:36.2597943Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-07-17T07:11:36.2598019Z 2025-07-17T07:11:36.2598251Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T07:11:36.2598456Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-07-17T07:11:36.2598683Z extension. This includes the CUDA include path, library path and runtime 2025-07-17T07:11:36.2598772Z library. 2025-07-17T07:11:36.2598847Z 2025-07-17T07:11:36.2599049Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T07:11:36.2599201Z constructor. Full list arguments can be found at 2025-07-17T07:11:36.2599519Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-07-17T07:11:36.2599593Z 2025-07-17T07:11:36.2599684Z .. warning:: 2025-07-17T07:11:36.2599905Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T07:11:36.2600109Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T07:11:36.2600311Z the user's responsibility in their library to not use APIs from 2025-07-17T07:11:36.2600558Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T07:11:36.2600783Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T07:11:36.2600990Z example, to give access to custom ops from python, the library should 2025-07-17T07:11:36.2601112Z register the ops through the dispatcher. 2025-07-17T07:11:36.2601196Z 2025-07-17T07:11:36.2601410Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T07:11:36.2601610Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T07:11:36.2601823Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T07:11:36.2602032Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T07:11:36.2602246Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T07:11:36.2602484Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T07:11:36.2602584Z CPython version of choice. 2025-07-17T07:11:36.2602665Z 2025-07-17T07:11:36.2602747Z Example: 2025-07-17T07:11:36.2602836Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2602990Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:36.2603094Z >>> from setuptools import setup 2025-07-17T07:11:36.2603313Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-07-17T07:11:36.2603400Z >>> setup( 2025-07-17T07:11:36.2603495Z ... name='cuda_extension', 2025-07-17T07:11:36.2603591Z ... ext_modules=[ 2025-07-17T07:11:36.2603683Z ... CUDAExtension( 2025-07-17T07:11:36.2603792Z ... name='cuda_extension', 2025-07-17T07:11:36.2603958Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T07:11:36.2604108Z ... extra_compile_args={'cxx': ['-g'], 2025-07-17T07:11:36.2604224Z ... 'nvcc': ['-O2']}, 2025-07-17T07:11:36.2604407Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-07-17T07:11:36.2604484Z ... ], 2025-07-17T07:11:36.2604578Z ... cmdclass={ 2025-07-17T07:11:36.2604686Z ... 'build_ext': BuildExtension 2025-07-17T07:11:36.2604761Z ... }) 2025-07-17T07:11:36.2604842Z 2025-07-17T07:11:36.2604936Z Compute capabilities: 2025-07-17T07:11:36.2605008Z 2025-07-17T07:11:36.2605313Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-07-17T07:11:36.2605594Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-07-17T07:11:36.2605882Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-07-17T07:11:36.2606189Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-07-17T07:11:36.2606473Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-07-17T07:11:36.2606599Z support (see below for details on PTX). 2025-07-17T07:11:36.2606673Z 2025-07-17T07:11:36.2606978Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-07-17T07:11:36.2607100Z CCs you want the extension to support: 2025-07-17T07:11:36.2607173Z 2025-07-17T07:11:36.2607352Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-07-17T07:11:36.2607587Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-07-17T07:11:36.2607659Z 2025-07-17T07:11:36.2607972Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-07-17T07:11:36.2608291Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-07-17T07:11:36.2608614Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-07-17T07:11:36.2608912Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-07-17T07:11:36.2609232Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-07-17T07:11:36.2609498Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-07-17T07:11:36.2609817Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-07-17T07:11:36.2610129Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-07-17T07:11:36.2610228Z "8.0 8.6" would be better. 2025-07-17T07:11:36.2610303Z 2025-07-17T07:11:36.2610594Z Note that while it's possible to include all supported archs, the more archs get included the 2025-07-17T07:11:36.2610918Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-07-17T07:11:36.2610990Z 2025-07-17T07:11:36.2611320Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-07-17T07:11:36.2611537Z To workaround the issue, move python binding logic to pure C++ file. 2025-07-17T07:11:36.2611610Z 2025-07-17T07:11:36.2611700Z Example use: 2025-07-17T07:11:36.2611794Z #include 2025-07-17T07:11:36.2611940Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-07-17T07:11:36.2612020Z 2025-07-17T07:11:36.2612098Z Instead of: 2025-07-17T07:11:36.2612197Z #include 2025-07-17T07:11:36.2612355Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-07-17T07:11:36.2612425Z 2025-07-17T07:11:36.2612690Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-07-17T07:11:36.2613235Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-07-17T07:11:36.2613311Z 2025-07-17T07:11:36.2613448Z Relocatable device code linking: 2025-07-17T07:11:36.2613521Z 2025-07-17T07:11:36.2613796Z If you want to reference device symbols across compilation units (across object files), 2025-07-17T07:11:36.2614052Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-07-17T07:11:36.2614408Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-07-17T07:11:36.2614731Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-07-17T07:11:36.2615053Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-07-17T07:11:36.2615321Z helps reduce the protentional perf degradation of `-rdc`. 2025-07-17T07:11:36.2615495Z Note that it needs to be used at both steps to be useful. 2025-07-17T07:11:36.2615571Z 2025-07-17T07:11:36.2615938Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-07-17T07:11:36.2616115Z There is also a case where `-dlink` is used without `-rdc`: 2025-07-17T07:11:36.2616366Z when an extension is linked against a static lib containing rdc-compiled objects 2025-07-17T07:11:36.2616572Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-07-17T07:11:36.2616652Z 2025-07-17T07:11:36.2620231Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-07-17T07:11:36.2620323Z 2025-07-17T07:11:36.2620408Z Example: 2025-07-17T07:11:36.2620514Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2620667Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:36.2620764Z >>> CUDAExtension( 2025-07-17T07:11:36.2620879Z ... name='cuda_extension', 2025-07-17T07:11:36.2621105Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T07:11:36.2621197Z ... dlink=True, 2025-07-17T07:11:36.2621326Z ... dlink_libraries=["dlink_lib"], 2025-07-17T07:11:36.2621447Z ... extra_compile_args={'cxx': ['-g'], 2025-07-17T07:11:36.2621573Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-07-17T07:11:36.2621652Z 2025-07-17T07:11:36.2621902Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2621984Z 2025-07-17T07:11:36.2622077Z warnings.warn(msg) 2025-07-17T07:11:36.2622149Z 2025-07-17T07:11:36.2622389Z --- Parse Warning: 132 / 136 --- 2025-07-17T07:11:36.2623322Z /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=1408. 2025-07-17T07:11:36.2623630Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2623707Z 2025-07-17T07:11:36.2623867Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-07-17T07:11:36.2623946Z 2025-07-17T07:11:36.2624182Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-07-17T07:11:36.2624374Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-07-17T07:11:36.2624463Z extension. 2025-07-17T07:11:36.2624535Z 2025-07-17T07:11:36.2624736Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-07-17T07:11:36.2624826Z constructor. 2025-07-17T07:11:36.2624896Z 2025-07-17T07:11:36.2624982Z .. warning:: 2025-07-17T07:11:36.2625212Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-07-17T07:11:36.2625444Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-07-17T07:11:36.2625645Z the user's responsibility in their library to not use APIs from 2025-07-17T07:11:36.2625867Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-07-17T07:11:36.2626105Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-07-17T07:11:36.2626321Z example, to give access to custom ops from python, the library should 2025-07-17T07:11:36.2626443Z register the ops through the dispatcher. 2025-07-17T07:11:36.2626513Z 2025-07-17T07:11:36.2626738Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-07-17T07:11:36.2626936Z as a compile flag when py_limited_api is specified as an option for 2025-07-17T07:11:36.2627143Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-07-17T07:11:36.2627350Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-07-17T07:11:36.2627560Z safety, and sanity in order to encourage best practices. To target a 2025-07-17T07:11:36.2627776Z different version, set min_supported_cpython to the hexcode of the 2025-07-17T07:11:36.2627875Z CPython version of choice. 2025-07-17T07:11:36.2627952Z 2025-07-17T07:11:36.2628043Z Example: 2025-07-17T07:11:36.2628133Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2628277Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:36.2628504Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-07-17T07:11:36.2628582Z >>> setup( 2025-07-17T07:11:36.2628691Z ... name='xpu_extension', 2025-07-17T07:11:36.2628775Z ... ext_modules=[ 2025-07-17T07:11:36.2628865Z ... SyclExtension( 2025-07-17T07:11:36.2628988Z ... name='xpu_extension', 2025-07-17T07:11:36.2629146Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-07-17T07:11:36.2629323Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-07-17T07:11:36.2629439Z ... ], 2025-07-17T07:11:36.2629526Z ... cmdclass={ 2025-07-17T07:11:36.2629640Z ... 'build_ext': BuildExtension 2025-07-17T07:11:36.2629728Z ... }) 2025-07-17T07:11:36.2629801Z 2025-07-17T07:11:36.2630110Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-07-17T07:11:36.2630357Z building process of the extension. If down the road a new card is installed the 2025-07-17T07:11:36.2630603Z extension may need to be recompiled. You can override the default behavior using 2025-07-17T07:11:36.2630915Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-07-17T07:11:36.2630995Z to support: 2025-07-17T07:11:36.2631069Z 2025-07-17T07:11:36.2631272Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-07-17T07:11:36.2631387Z 2025-07-17T07:11:36.2631694Z Note that while it's possible to include all supported archs, the more archs get included the 2025-07-17T07:11:36.2631989Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-07-17T07:11:36.2632062Z 2025-07-17T07:11:36.2632207Z Note: Ninja is required to build SyclExtension. 2025-07-17T07:11:36.2632279Z 2025-07-17T07:11:36.2632523Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2632604Z 2025-07-17T07:11:36.2632691Z warnings.warn(msg) 2025-07-17T07:11:36.2632764Z 2025-07-17T07:11:36.2632960Z --- Parse Warning: 133 / 136 --- 2025-07-17T07:11:36.2633837Z /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=1585. 2025-07-17T07:11:36.2634127Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2634204Z 2025-07-17T07:11:36.2634345Z Load a PyTorch C++ extension just-in-time (JIT). 2025-07-17T07:11:36.2634429Z 2025-07-17T07:11:36.2634657Z To load an extension, a Ninja build file is emitted, which is used to 2025-07-17T07:11:36.2634857Z compile the given sources into a dynamic library. This library is 2025-07-17T07:11:36.2635082Z subsequently loaded into the current Python process as a module and 2025-07-17T07:11:36.2635206Z returned from this function, ready for use. 2025-07-17T07:11:36.2635281Z 2025-07-17T07:11:36.2635494Z By default, the directory to which the build file is emitted and the 2025-07-17T07:11:36.2635723Z resulting library compiled to is ``/torch_extensions/``, where 2025-07-17T07:11:36.2635927Z ```` is the temporary folder on the current platform and ```` 2025-07-17T07:11:36.2636137Z the name of the extension. This location can be overridden in two ways. 2025-07-17T07:11:36.2636344Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-07-17T07:11:36.2636566Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-07-17T07:11:36.2636775Z into subfolders of this directory. Second, if the ``build_directory`` 2025-07-17T07:11:36.2637007Z argument to this function is supplied, it overrides the entire path, i.e. 2025-07-17T07:11:36.2637167Z the library will be compiled into that folder directly. 2025-07-17T07:11:36.2637240Z 2025-07-17T07:11:36.2637451Z To compile the sources, the default system compiler (``c++``) is used, 2025-07-17T07:11:36.2637682Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-07-17T07:11:36.2637900Z additional arguments to the compilation process, ``extra_cflags`` or 2025-07-17T07:11:36.2638128Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-07-17T07:11:36.2638333Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-07-17T07:11:36.2638512Z ``extra_cflags`` to pass further include directories. 2025-07-17T07:11:36.2638599Z 2025-07-17T07:11:36.2638829Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-07-17T07:11:36.2639021Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-07-17T07:11:36.2639254Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-07-17T07:11:36.2639456Z passing the CUDA lib64 directory as a library directory, and linking 2025-07-17T07:11:36.2639611Z ``cudart``. You can pass additional flags to nvcc via 2025-07-17T07:11:36.2639810Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-07-17T07:11:36.2640040Z heuristics for finding the CUDA install directory are used, which usually 2025-07-17T07:11:36.2640257Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-07-17T07:11:36.2640373Z safest option. 2025-07-17T07:11:36.2640455Z 2025-07-17T07:11:36.2640685Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-07-17T07:11:36.2640882Z files (``.sycl``) along with other sources. Such files will be detected 2025-07-17T07:11:36.2641099Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-07-17T07:11:36.2641298Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-07-17T07:11:36.2641483Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-07-17T07:11:36.2641694Z SYCL compiler is expected to be found via system PATH environment 2025-07-17T07:11:36.2641772Z variable. 2025-07-17T07:11:36.2641852Z 2025-07-17T07:11:36.2641929Z Args: 2025-07-17T07:11:36.2642128Z name: The name of the extension to build. This MUST be the same as the 2025-07-17T07:11:36.2642242Z name of the pybind11 module! 2025-07-17T07:11:36.2642465Z sources: A list of relative or absolute paths to C++ source files. 2025-07-17T07:11:36.2642687Z extra_cflags: optional list of compiler flags to forward to the build. 2025-07-17T07:11:36.2642938Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-07-17T07:11:36.2643043Z when building CUDA sources. 2025-07-17T07:11:36.2643256Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-07-17T07:11:36.2643386Z compiler when building SYCL sources. 2025-07-17T07:11:36.2643599Z extra_ldflags: optional list of linker flags to forward to the build. 2025-07-17T07:11:36.2643819Z extra_include_paths: optional list of include directories to forward 2025-07-17T07:11:36.2643906Z to the build. 2025-07-17T07:11:36.2644080Z build_directory: optional path to use as build workspace. 2025-07-17T07:11:36.2644263Z verbose: If ``True``, turns on verbose logging of load steps. 2025-07-17T07:11:36.2644483Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-07-17T07:11:36.2644638Z the build. If set to ``None`` (default), this value is 2025-07-17T07:11:36.2644846Z automatically determined based on the existence of ``.cu`` or 2025-07-17T07:11:36.2645006Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-07-17T07:11:36.2645116Z and libraries to be included. 2025-07-17T07:11:36.2645332Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-07-17T07:11:36.2645485Z the build. If set to ``None`` (default), this value is 2025-07-17T07:11:36.2645696Z automatically determined based on the existence of ``.sycl`` in 2025-07-17T07:11:36.2645852Z ``sources``. Set it to `True`` to force SYCL headers and 2025-07-17T07:11:36.2645953Z libraries to be included. 2025-07-17T07:11:36.2646153Z is_python_module: If ``True`` (default), imports the produced shared 2025-07-17T07:11:36.2646346Z library as a Python module. If ``False``, behavior depends on 2025-07-17T07:11:36.2646463Z ``is_standalone``. 2025-07-17T07:11:36.2646665Z is_standalone: If ``False`` (default) loads the constructed extension 2025-07-17T07:11:36.2646866Z into the process as a plain dynamic library. If ``True``, build a 2025-07-17T07:11:36.2646965Z standalone executable. 2025-07-17T07:11:36.2647046Z 2025-07-17T07:11:36.2647127Z Returns: 2025-07-17T07:11:36.2647234Z If ``is_python_module`` is ``True``: 2025-07-17T07:11:36.2647419Z Returns the loaded PyTorch extension as a Python module. 2025-07-17T07:11:36.2647495Z 2025-07-17T07:11:36.2647689Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-07-17T07:11:36.2647907Z Returns nothing. (The shared library is loaded into the process as 2025-07-17T07:11:36.2647998Z a side effect.) 2025-07-17T07:11:36.2648096Z 2025-07-17T07:11:36.2648209Z If ``is_standalone`` is ``True``. 2025-07-17T07:11:36.2648403Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-07-17T07:11:36.2648577Z added to the PATH environment variable as a side effect.) 2025-07-17T07:11:36.2648650Z 2025-07-17T07:11:36.2648727Z Example: 2025-07-17T07:11:36.2648822Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2648956Z >>> from torch.utils.cpp_extension import load 2025-07-17T07:11:36.2649041Z >>> module = load( 2025-07-17T07:11:36.2649145Z ... name='extension', 2025-07-17T07:11:36.2649298Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-07-17T07:11:36.2649396Z ... extra_cflags=['-O2'], 2025-07-17T07:11:36.2649492Z ... verbose=True) 2025-07-17T07:11:36.2649569Z 2025-07-17T07:11:36.2649825Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2649927Z 2025-07-17T07:11:36.2650017Z warnings.warn(msg) 2025-07-17T07:11:36.2650098Z 2025-07-17T07:11:36.2650289Z --- Parse Warning: 134 / 136 --- 2025-07-17T07:11:36.2651218Z /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=1890. 2025-07-17T07:11:36.2651487Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2651562Z 2025-07-17T07:11:36.2651766Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-07-17T07:11:36.2651851Z 2025-07-17T07:11:36.2652076Z This function behaves exactly like :func:`load`, but takes its sources as 2025-07-17T07:11:36.2652311Z strings rather than filenames. These strings are stored to files in the 2025-07-17T07:11:36.2652514Z build directory, after which the behavior of :func:`load_inline` is 2025-07-17T07:11:36.2652616Z identical to :func:`load`. 2025-07-17T07:11:36.2652700Z 2025-07-17T07:11:36.2652780Z See `the 2025-07-17T07:11:36.2653111Z tests `_ 2025-07-17T07:11:36.2653242Z for good examples of using this function. 2025-07-17T07:11:36.2653315Z 2025-07-17T07:11:36.2653543Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-07-17T07:11:36.2653787Z the necessary header includes, as well as the (pybind11) binding code. More 2025-07-17T07:11:36.2654022Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-07-17T07:11:36.2654216Z single ``.cpp`` file. This file is then prepended with ``#include 2025-07-17T07:11:36.2654309Z `` 2025-07-17T07:11:36.2654386Z 2025-07-17T07:11:36.2654616Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-07-17T07:11:36.2654848Z automatically generated for each function specified. ``functions`` can 2025-07-17T07:11:36.2655094Z either be a list of function names, or a dictionary mapping from function 2025-07-17T07:11:36.2655441Z names to docstrings. If a list is given, the name of each function is used 2025-07-17T07:11:36.2655531Z as its docstring. 2025-07-17T07:11:36.2655620Z 2025-07-17T07:11:36.2655829Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-07-17T07:11:36.2655998Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-07-17T07:11:36.2656216Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-07-17T07:11:36.2656436Z separately, but ultimately linked into a single library. Note that no 2025-07-17T07:11:36.2656660Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-07-17T07:11:36.2656881Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-07-17T07:11:36.2657123Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-07-17T07:11:36.2657238Z include its name in ``functions``). 2025-07-17T07:11:36.2657311Z 2025-07-17T07:11:36.2657529Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-07-17T07:11:36.2657751Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-07-17T07:11:36.2657941Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-07-17T07:11:36.2658153Z linked into a single library. Note that no bindings are generated for 2025-07-17T07:11:36.2658373Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-07-17T07:11:36.2658574Z create a C++ function that calls it, and either declare or define this 2025-07-17T07:11:36.2658766Z C++ function in one of the ``cpp_sources`` (and include its name 2025-07-17T07:11:36.2658854Z in ``functions``). 2025-07-17T07:11:36.2658957Z 2025-07-17T07:11:36.2659047Z 2025-07-17T07:11:36.2659119Z 2025-07-17T07:11:36.2659302Z See :func:`load` for a description of arguments omitted below. 2025-07-17T07:11:36.2659389Z 2025-07-17T07:11:36.2659464Z Args: 2025-07-17T07:11:36.2659700Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-07-17T07:11:36.2659926Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-07-17T07:11:36.2660141Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-07-17T07:11:36.2660342Z functions: A list of function names for which to generate function 2025-07-17T07:11:36.2660560Z bindings. If a dictionary is given, it should map function names to 2025-07-17T07:11:36.2660735Z docstrings (which are otherwise just the function names). 2025-07-17T07:11:36.2660967Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-07-17T07:11:36.2661120Z the build. If set to ``None`` (default), this value is 2025-07-17T07:11:36.2661322Z automatically determined based on whether ``cuda_sources`` is 2025-07-17T07:11:36.2661478Z provided. Set it to ``True`` to force CUDA headers 2025-07-17T07:11:36.2661584Z and libraries to be included. 2025-07-17T07:11:36.2661799Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-07-17T07:11:36.2661955Z the build. If set to ``None`` (default), this value is 2025-07-17T07:11:36.2662150Z automatically determined based on whether ``sycl_sources`` is 2025-07-17T07:11:36.2662310Z provided. Set it to ``True`` to force SYCL headers 2025-07-17T07:11:36.2662412Z and libraries to be included. 2025-07-17T07:11:36.2662613Z with_pytorch_error_handling: Determines whether pytorch error and 2025-07-17T07:11:36.2662821Z warning macros are handled by pytorch instead of pybind. To do 2025-07-17T07:11:36.2663033Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-07-17T07:11:36.2663268Z function. This redirection might cause issues in obscure cases 2025-07-17T07:11:36.2663448Z of cpp. This flag should be set to ``False`` when this redirect 2025-07-17T07:11:36.2663535Z causes issues. 2025-07-17T07:11:36.2663789Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-07-17T07:11:36.2664009Z ``#include `` and ``#include `` lines. 2025-07-17T07:11:36.2664160Z Use this option to improve cold start times when you 2025-07-17T07:11:36.2664426Z already include the necessary headers in your source code. Default: ``False``. 2025-07-17T07:11:36.2664501Z 2025-07-17T07:11:36.2664581Z Example: 2025-07-17T07:11:36.2664734Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-07-17T07:11:36.2664884Z >>> from torch.utils.cpp_extension import load_inline 2025-07-17T07:11:36.2665008Z >>> source = """ 2025-07-17T07:11:36.2665155Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-07-17T07:11:36.2665254Z return x.sin() + y.sin(); 2025-07-17T07:11:36.2665340Z } 2025-07-17T07:11:36.2665414Z """ 2025-07-17T07:11:36.2665548Z >>> module = load_inline(name='inline_extension', 2025-07-17T07:11:36.2665672Z ... cpp_sources=[source], 2025-07-17T07:11:36.2665782Z ... functions=['sin_add']) 2025-07-17T07:11:36.2665856Z 2025-07-17T07:11:36.2665942Z .. note:: 2025-07-17T07:11:36.2666173Z Since load_inline will just-in-time compile the source code, please ensure 2025-07-17T07:11:36.2666404Z that you have the right toolchains installed in the runtime. For example, 2025-07-17T07:11:36.2666613Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-07-17T07:11:36.2666878Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-07-17T07:11:36.2667133Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-07-17T07:11:36.2667389Z are not included when you install torch and must be additionally installed. 2025-07-17T07:11:36.2667464Z 2025-07-17T07:11:36.2667723Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-07-17T07:11:36.2667934Z the extension. This may use up too many resources on some systems. One 2025-07-17T07:11:36.2668158Z can control the number of workers by setting the `MAX_JOBS` environment 2025-07-17T07:11:36.2668267Z variable to a non-negative number. 2025-07-17T07:11:36.2668342Z 2025-07-17T07:11:36.2668595Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2668667Z 2025-07-17T07:11:36.2668758Z warnings.warn(msg) 2025-07-17T07:11:36.2668844Z 2025-07-17T07:11:36.2669045Z --- Parse Warning: 135 / 136 --- 2025-07-17T07:11:36.2670039Z /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. 2025-07-17T07:11:36.2670307Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2670380Z 2025-07-17T07:11:36.2670679Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-07-17T07:11:36.2670754Z 2025-07-17T07:11:36.2671047Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-07-17T07:11:36.2671301Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-07-17T07:11:36.2671525Z server like load. It can emulate multiple calling threads to a single module 2025-07-17T07:11:36.2671904Z provided. In the future we plan to enhance this component to support inter and 2025-07-17T07:11:36.2672206Z intra-op parallelism as well as multiple models running in a single process. 2025-07-17T07:11:36.2672283Z 2025-07-17T07:11:36.2672545Z Please note that even though nn.Module is supported, it might incur an overhead 2025-07-17T07:11:36.2672762Z from the need to hold GIL every time we execute Python code or pass around 2025-07-17T07:11:36.2672995Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-07-17T07:11:36.2673236Z model for inference deployment it is better to switch to using it in this 2025-07-17T07:11:36.2673317Z benchmark. 2025-07-17T07:11:36.2673403Z 2025-07-17T07:11:36.2673486Z Example:: 2025-07-17T07:11:36.2673564Z 2025-07-17T07:11:36.2673690Z >>> # xdoctest: +SKIP("undefined vars") 2025-07-17T07:11:36.2673826Z >>> from torch.utils import ThroughputBenchmark 2025-07-17T07:11:36.2673950Z >>> bench = ThroughputBenchmark(my_module) 2025-07-17T07:11:36.2674155Z >>> # Pre-populate benchmark's data set with the inputs 2025-07-17T07:11:36.2674253Z >>> for input in inputs: 2025-07-17T07:11:36.2674473Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-07-17T07:11:36.2674607Z ... bench.add_input(input[0], x2=input[1]) 2025-07-17T07:11:36.2674795Z >>> # Inputs supplied above are randomly used during the execution 2025-07-17T07:11:36.2674909Z >>> stats = bench.benchmark( 2025-07-17T07:11:36.2675006Z ... num_calling_threads=4, 2025-07-17T07:11:36.2675102Z ... num_warmup_iters = 100, 2025-07-17T07:11:36.2675202Z ... num_iters = 1000, 2025-07-17T07:11:36.2675279Z ... ) 2025-07-17T07:11:36.2675451Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-07-17T07:11:36.2675629Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-07-17T07:11:36.2675737Z 2025-07-17T07:11:36.2675984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2676070Z 2025-07-17T07:11:36.2676162Z warnings.warn(msg) 2025-07-17T07:11:36.2676236Z 2025-07-17T07:11:36.2676471Z --- Parse Warning: 136 / 136 --- 2025-07-17T07:11:36.2677425Z /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. 2025-07-17T07:11:36.2677690Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-07-17T07:11:36.2677885Z Sampler that restricts data loading to a subset of the dataset. 2025-07-17T07:11:36.2677958Z 2025-07-17T07:11:36.2678094Z It is especially useful in conjunction with 2025-07-17T07:11:36.2678335Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-07-17T07:11:36.2678602Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-07-17T07:11:36.2678826Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-07-17T07:11:36.2678951Z original dataset that is exclusive to it. 2025-07-17T07:11:36.2679033Z 2025-07-17T07:11:36.2679112Z .. note:: 2025-07-17T07:11:36.2679338Z Dataset is assumed to be of constant size and that any instance of it always 2025-07-17T07:11:36.2679477Z returns the same elements in the same order. 2025-07-17T07:11:36.2679550Z 2025-07-17T07:11:36.2679626Z Args: 2025-07-17T07:11:36.2679747Z dataset: Dataset used for sampling. 2025-07-17T07:11:36.2679955Z num_replicas (int, optional): Number of processes participating in 2025-07-17T07:11:36.2680204Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-07-17T07:11:36.2680313Z current distributed group. 2025-07-17T07:11:36.2680547Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-07-17T07:11:36.2680781Z By default, :attr:`rank` is retrieved from the current distributed 2025-07-17T07:11:36.2680868Z group. 2025-07-17T07:11:36.2681084Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-07-17T07:11:36.2681178Z indices. 2025-07-17T07:11:36.2681364Z seed (int, optional): random seed used to shuffle the sampler if 2025-07-17T07:11:36.2681562Z :attr:`shuffle=True`. This number should be identical across all 2025-07-17T07:11:36.2681724Z processes in the distributed group. Default: ``0``. 2025-07-17T07:11:36.2681932Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-07-17T07:11:36.2682133Z tail of the data to make it evenly divisible across the number of 2025-07-17T07:11:36.2682331Z replicas. If ``False``, the sampler will add extra indices to make 2025-07-17T07:11:36.2682563Z the data evenly divisible across the replicas. Default: ``False``. 2025-07-17T07:11:36.2682646Z 2025-07-17T07:11:36.2682733Z .. warning:: 2025-07-17T07:11:36.2682923Z In distributed mode, calling the :meth:`set_epoch` method at 2025-07-17T07:11:36.2683171Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-07-17T07:11:36.2683423Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-07-17T07:11:36.2683550Z the same ordering will be always used. 2025-07-17T07:11:36.2683627Z 2025-07-17T07:11:36.2683709Z Example:: 2025-07-17T07:11:36.2683795Z 2025-07-17T07:11:36.2683889Z >>> # xdoctest: +SKIP 2025-07-17T07:11:36.2684101Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-07-17T07:11:36.2684306Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-07-17T07:11:36.2684428Z ... sampler=sampler) 2025-07-17T07:11:36.2684563Z >>> for epoch in range(start_epoch, n_epochs): 2025-07-17T07:11:36.2684681Z ... if is_distributed: 2025-07-17T07:11:36.2684790Z ... sampler.set_epoch(epoch) 2025-07-17T07:11:36.2684911Z ... train(loader) 2025-07-17T07:11:36.2685031Z 2025-07-17T07:11:36.2685314Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-07-17T07:11:36.2685395Z 2025-07-17T07:11:36.2685483Z warnings.warn(msg) 2025-07-17T07:11:36.2685553Z 2025-07-17T07:11:36.2685671Z  2025-07-17T07:11:36.2685836Z === Found 9 run-time warnings === 2025-07-17T07:11:36.2686004Z --- Runtime Warning: 1 / 9 --- 2025-07-17T07:11:36.2686265Z example = 2025-07-17T07:11:36.2687621Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py:1351: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1978.) 2025-07-17T07:11:36.2687735Z return super().refine_names(names) 2025-07-17T07:11:36.2687806Z 2025-07-17T07:11:36.2687971Z --- Runtime Warning: 2 / 9 --- 2025-07-17T07:11:36.2688282Z example = 2025-07-17T07:11:36.2688908Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py:281: UserWarning: Warning only once for all operators, other operators may also be overridden. 2025-07-17T07:11:36.2689219Z Overriding a previously registered kernel for the same operator and the same dispatch key 2025-07-17T07:11:36.2689419Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2025-07-17T07:11:36.2689744Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2025-07-17T07:11:36.2689843Z dispatch key: CPU 2025-07-17T07:11:36.2690267Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2025-07-17T07:11:36.2690827Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:225.) 2025-07-17T07:11:36.2690978Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2025-07-17T07:11:36.2691048Z 2025-07-17T07:11:36.2691223Z --- Runtime Warning: 3 / 9 --- 2025-07-17T07:11:36.2691454Z example = 2025-07-17T07:11:36.2693315Z /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.) 2025-07-17T07:11:36.2693584Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2025-07-17T07:11:36.2693656Z 2025-07-17T07:11:36.2693833Z --- Runtime Warning: 4 / 9 --- 2025-07-17T07:11:36.2694077Z example = 2025-07-17T07:11:36.2695797Z :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.) 2025-07-17T07:11:36.2695913Z 2025-07-17T07:11:36.2696091Z --- Runtime Warning: 5 / 9 --- 2025-07-17T07:11:36.2696436Z example = 2025-07-17T07:11:36.2697944Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/const_fold.py:271: 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 2025-07-17T07:11:36.2698110Z new_node = root_const_gm.graph.get_attr(in_node.target) 2025-07-17T07:11:36.2698184Z 2025-07-17T07:11:36.2698353Z --- Runtime Warning: 6 / 9 --- 2025-07-17T07:11:36.2698643Z example = 2025-07-17T07:11:36.2699744Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:392: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2025-07-17T07:11:36.2699836Z warnings.warn( 2025-07-17T07:11:36.2699909Z 2025-07-17T07:11:36.2700077Z --- Runtime Warning: 7 / 9 --- 2025-07-17T07:11:36.2700407Z example = 2025-07-17T07:11:36.2701498Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:392: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2025-07-17T07:11:36.2701593Z warnings.warn( 2025-07-17T07:11:36.2701669Z 2025-07-17T07:11:36.2701837Z --- Runtime Warning: 8 / 9 --- 2025-07-17T07:11:36.2702151Z example = 2025-07-17T07:11:36.2702963Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2025-07-17T07:11:36.2703076Z WeightNorm.apply(module, name, dim) 2025-07-17T07:11:36.2703152Z 2025-07-17T07:11:36.2703319Z --- Runtime Warning: 9 / 9 --- 2025-07-17T07:11:36.2703619Z example = 2025-07-17T07:11:36.2704413Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2025-07-17T07:11:36.2704526Z WeightNorm.apply(module, name, dim) 2025-07-17T07:11:36.2704631Z 2025-07-17T07:11:36.2704924Z === 342 passed, 377 skipped, 145 warnings in 12.49 seconds === 2025-07-17T07:11:36.2705136Z Running test_namedtuple_return_api 1/1 ... [2025-07-17 07:11:36.084427] 2025-07-17T07:11:36.2705248Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:11:36.2706117Z 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'] ... [2025-07-17 07:11:36.084760] 2025-07-17T07:11:39.9038824Z 2025-07-17T07:11:39.9039823Z 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_2c2429fe367c806b_.log 2025-07-17T07:11:39.9041658Z 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 2025-07-17T07:11:39.9043078Z 2025-07-17T07:11:39.9043719Z Running test_nn 1/1 ... [2025-07-17 07:11:39.904238] 2025-07-17T07:11:39.9044177Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:11:39.9047331Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:11:39.904545] 2025-07-17T07:13:51.9351987Z 2025-07-17T07:13:51.9352784Z test_nn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.1_1e7d63d3db2a9ad0_.log 2025-07-17T07:13:52.0675023Z Running 2223 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_reduce, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_BCELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_CELU_no_batch_dim, test/test_nn.py::TestNN::test_CELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_CTCLoss_critical_target_len, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cpu, test/test_nn.py::TestNN::test_CTCLoss_lengthchecks_cuda, test/test_nn.py::TestNN::test_CTCLoss_long_targets, test/test_nn.py::TestNN::test_CTCLoss_typechecks, test/test_nn.py::TestNN::test_CTCLoss_zero_infinity, test/test_nn.py::TestNN::test_CTCLoss_zero_lengths, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_cuda, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_groups, test/test_nn.py::TestNN::test_Conv1d_groups_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1size1, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2size1, test/test_nn.py::TestNN::test_Conv1d_pad2size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same, test/test_nn.py::TestNN::test_Conv1d_pad_same2, test/test_nn.py::TestNN::test_Conv1d_pad_same2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_valid, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv1d_stride_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv2d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_pad_valid, test/test_nn.py::TestNN::test_Conv2d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv2d_padding, test/test_nn.py::TestNN::test_Conv2d_padding_cuda, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_strided, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_strided_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch, test/test_nn.py::TestNN::test_Conv2d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated, test/test_nn.py::TestNN::test_Conv3d_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_dilated_strided, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv3d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_stride_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose1d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated_cuda, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_CrossMapLRN2d_cuda, test/test_nn.py::TestNN::test_ELU_no_batch_dim, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Embedding, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max, test/test_nn.py::TestNN::test_EmbeddingBag_max_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_max_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean, test/test_nn.py::TestNN::test_EmbeddingBag_mean_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum, test/test_nn.py::TestNN::test_EmbeddingBag_sum_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sum_padding_idx_cuda, test/test_nn.py::TestNN::test_Embedding_cuda, test/test_nn.py::TestNN::test_Embedding_discontiguous, test/test_nn.py::TestNN::test_Embedding_discontiguous_cuda, test/test_nn.py::TestNN::test_Embedding_sparse, test/test_nn.py::TestNN::test_Embedding_sparse_cuda, test/test_nn.py::TestNN::test_Flatten, test/test_nn.py::TestNN::test_Flatten_cuda, test/test_nn.py::TestNN::test_Flatten_no_batch_dim, test/test_nn.py::TestNN::test_Flatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Fold, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_int_input, test/test_nn.py::TestNN::test_Fold_int_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim, test/test_nn.py::TestNN::test_GELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_GLU_no_batch_dim, test/test_nn.py::TestNN::test_GLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim, test/test_nn.py::TestNN::test_Hardsigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim, test/test_nn.py::TestNN::test_Hardtanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_HuberLoss_delta, test/test_nn.py::TestNN::test_HuberLoss_delta_cuda, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_hidden_size, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_eval_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear, test/test_nn.py::TestNN::test_Linear_cuda, test/test_nn.py::TestNN::test_Linear_no_batch_dim, test/test_nn.py::TestNN::test_Linear_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Linear_no_bias, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MSELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar, test/test_nn.py::TestNN::test_MSELoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net, test/test_nn.py::TestNN::test_MaxUnpool1d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net, test/test_nn.py::TestNN::test_MaxUnpool2d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net, test/test_nn.py::TestNN::test_MaxUnpool3d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleDict, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_0d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_p_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_reduce, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_nn.py::TestNN::test_PReLU_backward_requires_grad_false, test/test_nn.py::TestNN::test_PReLU_no_batch_dim, test/test_nn.py::TestNN::test_PReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_lhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs, test/test_nn.py::TestNN::test_PairwiseDistance_broadcast_rhs_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim, test/test_nn.py::TestNN::test_PairwiseDistance_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterDict_replication, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_ParameterList_meta, test/test_nn.py::TestNN::test_ParameterList_replication, test/test_nn.py::TestNN::test_PixelShuffle, test/test_nn.py::TestNN::test_PixelShuffle_cuda, test/test_nn.py::TestNN::test_PixelUnshuffle, test/test_nn.py::TestNN::test_PixelUnshuffle_cuda, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_RNN_cell, test/test_nn.py::TestNN::test_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_change_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_no_dropout, test/test_nn.py::TestNN::test_RNN_cpu_vs_cudnn_with_dropout, test/test_nn.py::TestNN::test_RNN_cudnn_weight_norm, test/test_nn.py::TestNN::test_RNN_dropout, test/test_nn.py::TestNN::test_RNN_dropout_state, test/test_nn.py::TestNN::test_RNN_input_size_zero, test/test_nn.py::TestNN::test_RNN_nonlinearity, test/test_nn.py::TestNN::test_RNN_nonlinearity_passed_as_arg, test/test_nn.py::TestNN::test_RReLU, test/test_nn.py::TestNN::test_RReLU_cuda, test/test_nn.py::TestNN::test_RReLU_no_batch_dim, test/test_nn.py::TestNN::test_RReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down, test/test_nn.py::TestNN::test_RReLU_with_up_down_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar_cuda, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim, test/test_nn.py::TestNN::test_ReLU6_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReLU_no_batch_dim, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d, test/test_nn.py::TestNN::test_ReplicationPad3d_complex, test/test_nn.py::TestNN::test_ReplicationPad3d_complex_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_add, test/test_nn.py::TestNN::test_Sequential_append, test/test_nn.py::TestNN::test_Sequential_delitem, test/test_nn.py::TestNN::test_Sequential_extend, test/test_nn.py::TestNN::test_Sequential_getitem, test/test_nn.py::TestNN::test_Sequential_iadd, test/test_nn.py::TestNN::test_Sequential_imul, test/test_nn.py::TestNN::test_Sequential_insert, test/test_nn.py::TestNN::test_Sequential_insert_fail_case, test/test_nn.py::TestNN::test_Sequential_mul, test/test_nn.py::TestNN::test_Sequential_pop, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_Sequential_setitem, test/test_nn.py::TestNN::test_Sequential_setitem_named, test/test_nn.py::TestNN::test_SiLU_no_batch_dim, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_beta_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softplus_no_batch_dim, test/test_nn.py::TestNN::test_Softplus_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Softsign_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanh_no_batch_dim, test/test_nn.py::TestNN::test_Tanh_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Threshold_no_batch_dim, test/test_nn.py::TestNN::test_Threshold_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_Transformer_cell, test/test_nn.py::TestNN::test_Transformer_multilayer_coder, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim, test/test_nn.py::TestNN::test_Unflatten_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_Unfold_int_input, test/test_nn.py::TestNN::test_Unfold_int_input_cuda, test/test_nn.py::TestNN::test_adaptive_log_softmax, test/test_nn.py::TestNN::test_add_module, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid, test/test_nn.py::TestNN::test_affine_grid_3d, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_2, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_3, test/test_nn.py::TestNN::test_affine_grid_error_checking, test/test_nn.py::TestNN::test_assignment, test/test_nn.py::TestNN::test_batch_norm_update_stats, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_float32, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_buffer_update_when_stats_are_not_tracked, test/test_nn.py::TestNN::test_batchnorm_cudnn_half, test/test_nn.py::TestNN::test_batchnorm_cudnn_nhwc, test/test_nn.py::TestNN::test_batchnorm_half_overflow, test/test_nn.py::TestNN::test_batchnorm_load_state_dict, test/test_nn.py::TestNN::test_batchnorm_nhwc_cpu, test/test_nn.py::TestNN::test_batchnorm_nhwc_cuda, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_BatchNorm2d, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_SyncBatchNorm, test/test_nn.py::TestNN::test_batchnorm_nonaffine_cuda_half_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_bias_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_less_than_one_value_per_channel, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_mean_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_is_not_same_size_as_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_running_var_or_running_mean_have_forward_grad, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_weight_is_not_same_size_as_input, test/test_nn.py::TestNN::test_bce_loss_always_nonnegative, test/test_nn.py::TestNN::test_bce_loss_broadcasts_weights, test/test_nn.py::TestNN::test_bce_loss_input_range, test/test_nn.py::TestNN::test_bce_loss_size_mismatch, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_pos_weights, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_weights, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_forward_grad, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none, test/test_nn.py::TestNN::test_bce_with_logits_raises_if_target_and_input_are_different_size, test/test_nn.py::TestNN::test_bce_with_logits_stability, test/test_nn.py::TestNN::test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bilinear, test/test_nn.py::TestNN::test_bilinear_broadcasting, test/test_nn.py::TestNN::test_bilinear_no_bias, test/test_nn.py::TestNN::test_bilinear_non_contiguous, test/test_nn.py::TestNN::test_bilinear_value_error, test/test_nn.py::TestNN::test_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_broadcast_not_requiring_grad, test/test_nn.py::TestNN::test_buffer_bad_module_subclass, test/test_nn.py::TestNN::test_buffer_not_persistent, test/test_nn.py::TestNN::test_buffer_not_persistent_assign, test/test_nn.py::TestNN::test_buffer_not_persistent_del, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffer_not_persistent_overwrite, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_call_supports_python_dict_output, test/test_nn.py::TestNN::test_channel_shuffle_input_checks, test/test_nn.py::TestNN::test_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_children, test/test_nn.py::TestNN::test_container_copy, test/test_nn.py::TestNN::test_convert_sync_batchnorm, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_diff_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_error_on_nonexpandable_shapes, test/test_nn.py::TestNN::test_cosine_embedding_loss_invalid_shape, test/test_nn.py::TestNN::test_cosine_embedding_loss_margin_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_with_diff_type, test/test_nn.py::TestNN::test_cosine_similarity, test/test_nn.py::TestNN::test_cross_entropy_loss, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cross_entropy_loss_zero_div, test/test_nn.py::TestNN::test_cudnn_forward_exception, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_cudnn_weight_format, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_dir, test/test_nn.py::TestNN::test_dir_digit, test/test_nn.py::TestNN::test_elu_inplace_gradgrad, test/test_nn.py::TestNN::test_elu_inplace_on_view, test/test_nn.py::TestNN::test_error_RNN_seq_len_zero, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_get_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_extra_state_non_dict, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fold_invalid_arg, test/test_nn.py::TestNN::test_fractional_max_pool2d_invalid_output_ratio, test/test_nn.py::TestNN::test_gaussian_nll_loss_args, test/test_nn.py::TestNN::test_gaussian_nll_loss_broadcasting, test/test_nn.py::TestNN::test_gaussian_nll_loss_scalar_var, test/test_nn.py::TestNN::test_get_buffer, test/test_nn.py::TestNN::test_get_buffer_from_submodules, test/test_nn.py::TestNN::test_getattr_with_property, test/test_nn.py::TestNN::test_grid_sample, test/test_nn.py::TestNN::test_grid_sample_3d, test/test_nn.py::TestNN::test_grid_sample_error_checking, test/test_nn.py::TestNN::test_grid_sample_nearest_neighbor_rounding_mode_consistency, test/test_nn.py::TestNN::test_hardtanh_backward, test/test_nn.py::TestNN::test_hardtanh_inplace_gradgrad, test/test_nn.py::TestNN::test_huber_loss_invalid_delta, test/test_nn.py::TestNN::test_inplace_thnn, test/test_nn.py::TestNN::test_interpolate, test/test_nn.py::TestNN::test_interpolate_bicubic_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_buffer_overflow, test/test_nn.py::TestNN::test_interpolate_illegal_memory_access, test/test_nn.py::TestNN::test_interpolate_linear_1d, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d, test/test_nn.py::TestNN::test_interpolate_nearest_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d, test/test_nn.py::TestNN::test_interpolate_nearest_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d, test/test_nn.py::TestNN::test_interpolate_nearest_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_undefined_behavior_casting, test/test_nn.py::TestNN::test_kl_div_log_softmax_target, test/test_nn.py::TestNN::test_kl_div_with_diff_type, test/test_nn.py::TestNN::test_kl_div_with_diff_type_log_target, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_backwards_eps, test/test_nn.py::TestNN::test_layer_norm_eps, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_layer_norm_large_tensor, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightStrided, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSC, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_linear_broadcasting, test/test_nn.py::TestNN::test_linear_raise_on_scalar_input, test/test_nn.py::TestNN::test_log_softmax_dim0, test/test_nn.py::TestNN::test_log_softmax_dim0_cuda, test/test_nn.py::TestNN::test_log_softmax_dim3, test/test_nn.py::TestNN::test_log_softmax_dim3_cuda, test/test_nn.py::TestNN::test_log_softmax_lastdim, test/test_nn.py::TestNN::test_log_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_log_softmax_scalar_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial, test/test_nn.py::TestNN::test_log_softmax_spatial_cuda, test/test_nn.py::TestNN::test_log_softmax_spatial_special, test/test_nn.py::TestNN::test_log_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_loss_equal_input_target_shape, test/test_nn.py::TestNN::test_margin_ranking_loss_margin_no_reduce, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_max_pool1d_invalid_output_size, test/test_nn.py::TestNN::test_module_apply_inplace_op, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_module_to_argparse, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_mse_loss_size_warning, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_nn.py::TestNN::test_named_children, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_native_channel_shuffle_return_alias_of_self, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_nested_tensor_from_mask_error, test/test_nn.py::TestNN::test_no_grad, test/test_nn.py::TestNN::test_non_leaf_parameters, test/test_nn.py::TestNN::test_normalize, test/test_nn.py::TestNN::test_overwrite_module_params_on_conversion, test/test_nn.py::TestNN::test_pack_sequence_batch_sizes_throw, test/test_nn.py::TestNN::test_pad_scalar_error, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_pairwise_distance, test/test_nn.py::TestNN::test_parameter_assignment, test/test_nn.py::TestNN::test_parameterlistdict_pickle, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_parameters_and_named_parameters, test/test_nn.py::TestNN::test_parameters_to_vector, test/test_nn.py::TestNN::test_parse_to, test/test_nn.py::TestNN::test_partial_flat_weights, test/test_nn.py::TestNN::test_pdist, test/test_nn.py::TestNN::test_pdist_cpu_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_cuda_gradgrad_unimplemented, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pdist_empty_row, test/test_nn.py::TestNN::test_pdist_large, test/test_nn.py::TestNN::test_pdist_zeros, test/test_nn.py::TestNN::test_pickle_module_no_weights_only_warning, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_pointwise_loss_target_grad_none_reduction, test/test_nn.py::TestNN::test_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, test/test_nn.py::TestNN::test_projections_lstm_check_device, test/test_nn.py::TestNN::test_projections_lstm_initial_hidden_state, test/test_nn.py::TestNN::test_register_buffer_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_not_tensor, test/test_nn.py::TestNN::test_register_parameter_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, test/test_nn.py::TestNN::test_repr, test/test_nn.py::TestNN::test_requires_grad_, test/test_nn.py::TestNN::test_rnn_args_check, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_set_submodule, test/test_nn.py::TestNN::test_share_memory, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_smoothl1loss_negative_beta_not_supported, test/test_nn.py::TestNN::test_softmax_functional_dim0, test/test_nn.py::TestNN::test_softmax_functional_dim0_cuda, test/test_nn.py::TestNN::test_softmax_functional_dim3, test/test_nn.py::TestNN::test_softmax_functional_dim3_cuda, test/test_nn.py::TestNN::test_softmax_functional_scalar, test/test_nn.py::TestNN::test_softmax_functional_scalar_cuda, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_cuda, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_lastdim_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_cuda, test/test_nn.py::TestNN::test_softmax_spatial_dtype, test/test_nn.py::TestNN::test_softmax_spatial_dtype_cuda, test/test_nn.py::TestNN::test_softmax_spatial_special, test/test_nn.py::TestNN::test_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_softmin, test/test_nn.py::TestNN::test_spectral_norm, test/test_nn.py::TestNN::test_spectral_norm_dim, test/test_nn.py::TestNN::test_spectral_norm_forward, test/test_nn.py::TestNN::test_spectral_norm_load_state_dict, test/test_nn.py::TestNN::test_spectral_norm_pickle, test/test_nn.py::TestNN::test_state_dict, test/test_nn.py::TestNN::test_swap_module_params_poisons_acc_grad, test/test_nn.py::TestNN::test_sync_batchnorm_accuracy_cuda, test/test_nn.py::TestNN::test_sync_batchnorm_backward_elemt, test/test_nn.py::TestNN::test_threshold_bfloat16_half, test/test_nn.py::TestNN::test_threshold_int, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_train_errors_for_invalid_mode, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformer_layer_args_check, test/test_nn.py::TestNN::test_transformerdecoder, test/test_nn.py::TestNN::test_transformerdecoderlayer, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss, test/test_nn.py::TestNN::test_triplet_margin_loss_no_reduce, test/test_nn.py::TestNN::test_triplet_margin_loss_swap, test/test_nn.py::TestNN::test_triplet_margin_loss_swap_no_reduce, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_unflatten_invalid_arg, test/test_nn.py::TestNN::test_unfold_invalid_arg, test/test_nn.py::TestNN::test_upsamplingBilinear2d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingLinear1d, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsamplingTrilinear3d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_bfloat16, test/test_nn.py::TestNN::test_upsampling_not_recompute_scale_factor, test/test_nn.py::TestNN::test_upsampling_small_scale, test/test_nn.py::TestNN::test_vector_to_parameters, test/test_nn.py::TestNN::test_weight_norm, test/test_nn.py::TestNN::test_weight_norm_pickle, test/test_nn.py::TestNN::test_weighted_huber_loss, test/test_nn.py::TestNN::test_weighted_l1_loss_with_weights, test/test_nn.py::TestNN::test_weighted_mse_loss, test/test_nn.py::TestNN::test_zero_grad, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestConstantPadNd::test_preserves_memory_format, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestAddRelu::test_add_relu_broadcasting, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestFusionUtils::test_fuse_conv_bn_requires_grad, test/test_nn.py::TestFusionUtils::test_fuse_linear_bn_requires_grad, test/test_nn.py::TestUtils::test_consume_prefix_in_state_dict_if_present, test/test_nn.py::TestNNDeviceTypeCPU::test_BatchNorm_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_Bilinear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_empty_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GRU_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_memory_format_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_raises_error_if_one_value_per_group_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm1d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm2d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm3d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LocalResponseNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_deterministic_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_fails_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad1d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad3d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerDecoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoderLayer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Transformer_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_adaptiveavg_pool1d_shmem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate45_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate90_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_large_batch_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_update_stats_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_channel_shuffle_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_error_if_nonfinite_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_4_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_consistent_index_target_and_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_errors_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_weight_ignore_indices_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_with_probs_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_2d_out_of_bounds_class_index_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_index_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_one_hot_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_all_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_unit_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cpu_length_cuda_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cuda_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_error_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_device_mask_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_with_neg_alpha_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_fold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_bfloat16_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_2d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_large_index_3d_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_hardsigmoid_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_corner_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_for_single_spatial_element_during_training_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm1d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_half_precision_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_layernorm_weight_bias_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_neg_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_zero_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_linear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_big_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_logsigmoid_out_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_lstmcell_backward_only_one_output_grad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_TxT_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_devices_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_lowp_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_mask_types_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_mish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_non_recursive_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_mse_loss_error_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_all_ignored_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_mean_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_none_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_empty_tensor_reduction_sum_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_target_dim_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_mismatched_batch_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_out_of_bounds_ignore_index_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_total_weight_is_zero_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nonlinearity_propagate_nan_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_one_hot_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_overwrite_module_params_on_conversion_cpu_device_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_replicatepad_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_numeric_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_numeric_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_retain_variables_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_save_lstm_compatibility_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_silu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_smoothl1loss_backward_zero_beta_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_smem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_unaligned_grad_output_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_unaligned_output_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_without_fully_vectorized_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_double_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_forward_64bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softplus_low_threshold_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softshrink_negative_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_threshold_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_fast_path_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_gelu_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_default_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_fail_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_rocm_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_rescale_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_False_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingTrilinear3d_align_corners_True_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingnearest2d_backward_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_variable_sequence_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_warp_softmax_64bit_indexing_cpu_float32 2025-07-17T07:13:52.1863967Z 2025-07-17T07:13:52.1864228Z Running test_spectral_ops 1/1 ... [2025-07-17 07:13:51.938777] 2025-07-17T07:13:52.1864654Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:13:52.1865721Z 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'] ... [2025-07-17 07:13:51.939108] 2025-07-17T07:14:02.0173049Z 2025-07-17T07:14:02.0174134Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_a0c9a123a57e1c5c_.log 2025-07-17T07:14:02.0268031Z 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, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_ifft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft2_invalid_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_ifft_rfft_irfft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_input_modification_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_invalid_dtypes_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_plan_repeatable_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_int8, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_against_librosa_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_linearity_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_of_sine_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_simple_cases_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_various_params_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_with_padding_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_throws_cpu, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_stft_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 2025-07-17T07:14:02.0360691Z 2025-07-17T07:14:02.0360954Z Running distributions/test_distributions 1/1 ... [2025-07-17 07:14:02.017890] 2025-07-17T07:14:02.0361458Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:14:02.0362655Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:14:02.018206] 2025-07-17T07:14:56.6547830Z 2025-07-17T07:14:56.6549438Z distributions/test_distributions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.1_c02d5fbdb00623f8_.log 2025-07-17T07:14:56.6644830Z Running 230 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_argmax_relaxed_categorical, test/distributions/test_distributions.py::TestDistributions::test_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_beta_log_prob, test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_gpu, test/distributions/test_distributions.py::TestDistributions::test_binomial, test/distributions/test_distributions.py::TestDistributions::test_binomial_bfloat16, test/distributions/test_distributions.py::TestDistributions::test_binomial_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_binomial_extreme_vals, test/distributions/test_distributions.py::TestDistributions::test_binomial_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_binomial_sample, test/distributions/test_distributions.py::TestDistributions::test_binomial_stable, test/distributions/test_distributions.py::TestDistributions::test_binomial_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_cauchy, test/distributions/test_distributions.py::TestDistributions::test_cdf_icdf_inverse, test/distributions/test_distributions.py::TestDistributions::test_cdf_log_prob, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, test/distributions/test_distributions.py::TestDistributions::test_chi2_shape, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob_zero, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_mode, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributions::test_distribution_expand, test/distributions/test_distributions.py::TestDistributions::test_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_enumerate_support_type, test/distributions/test_distributions.py::TestDistributions::test_exponential, test/distributions/test_distributions.py::TestDistributions::test_exponential_sample, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_shape, test/distributions/test_distributions.py::TestDistributions::test_gamma_log_prob_at_boundary, test/distributions/test_distributions.py::TestDistributions::test_gamma_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_shape, test/distributions/test_distributions.py::TestDistributions::test_generalized_pareto, test/distributions/test_distributions.py::TestDistributions::test_generalized_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_geometric, test/distributions/test_distributions.py::TestDistributions::test_geometric_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_geometric_sample, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_gumbel_sample, test/distributions/test_distributions.py::TestDistributions::test_halfcauchy, test/distributions/test_distributions.py::TestDistributions::test_halfnormal, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_has_examples, test/distributions/test_distributions.py::TestDistributions::test_independent_expand, test/distributions/test_distributions.py::TestDistributions::test_independent_shape, test/distributions/test_distributions.py::TestDistributions::test_invalid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_inversegamma, test/distributions/test_distributions.py::TestDistributions::test_inversegamma_sample, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_mean_variance, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_shape, test/distributions/test_distributions.py::TestDistributions::test_laplace, test/distributions/test_distributions.py::TestDistributions::test_laplace_sample, test/distributions/test_distributions.py::TestDistributions::test_lazy_property_grad, test/distributions/test_distributions.py::TestDistributions::test_lkj_cholesky_log_prob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_lognormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_sample, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributions::test_mode, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_multinomial_2d, test/distributions/test_distributions.py::TestDistributions::test_multinomial_sequential_draw, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_normal, test/distributions/test_distributions.py::TestDistributions::test_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_pareto, test/distributions/test_distributions.py::TestDistributions::test_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_forward_ad, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_log_prob, test/distributions/test_distributions.py::TestDistributions::test_poisson_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_shape, test/distributions/test_distributions.py::TestDistributions::test_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_repr, test/distributions/test_distributions.py::TestDistributions::test_rounded_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_rsample_requires_grad, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, test/distributions/test_distributions.py::TestDistributions::test_studentT, test/distributions/test_distributions.py::TestDistributions::test_studentT_log_prob, test/distributions/test_distributions.py::TestDistributions::test_studentT_sample, test/distributions/test_distributions.py::TestDistributions::test_support_attributes, test/distributions/test_distributions.py::TestDistributions::test_torch_binomial_dtype_errors, test/distributions/test_distributions.py::TestDistributions::test_uniform, test/distributions/test_distributions.py::TestDistributions::test_valid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_vonmises_logprob, test/distributions/test_distributions.py::TestDistributions::test_vonmises_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_log_prob, 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_shape, 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_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, 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::TestRsample::test_gamma, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_scalar_params, 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_beta_shape_tensor_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_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_entropy_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_scalar_param, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_tensor_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_gumbel_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_kumaraswamy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_mean_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_one_hot_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_weibull_scale_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_tensor_params, test/distributions/test_distributions.py::TestKL::test_entropy_exponential_family, test/distributions/test_distributions.py::TestKL::test_entropy_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_infinite, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestKL::test_kl_transformed, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestConstraints::test_support_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob_with_logits, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_probs_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_cdf, test/distributions/test_distributions.py::TestAgainstScipy::test_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_event_dim, test/distributions/test_distributions.py::TestFunctors::test_cat_transform, test/distributions/test_distributions.py::TestFunctors::test_cat_transform_non_uniform, test/distributions/test_distributions.py::TestFunctors::test_stack_transform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestValidation::test_invalid_log_probs_arg, test/distributions/test_distributions.py::TestValidation::test_valid, test/distributions/test_distributions.py::TestValidation::test_warning_unimplemented_constraints, test/distributions/test_distributions.py::TestJit::test_cdf, test/distributions/test_distributions.py::TestJit::test_entropy, test/distributions/test_distributions.py::TestJit::test_enumerate_support, test/distributions/test_distributions.py::TestJit::test_log_prob, test/distributions/test_distributions.py::TestJit::test_mean, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_sample, test/distributions/test_distributions.py::TestJit::test_variance 2025-07-17T07:14:56.6738976Z 2025-07-17T07:14:56.6739209Z Running test_extension_utils 1/1 ... [2025-07-17 07:14:56.655394] 2025-07-17T07:14:56.6739649Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:14:56.6740728Z 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'] ... [2025-07-17 07:14:56.655729] 2025-07-17T07:14:59.5741768Z 2025-07-17T07:14:59.5742647Z test_extension_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_extension_utils_1.1_4a1c6c8b90769645_.log 2025-07-17T07:14:59.5744421Z 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 2025-07-17T07:14:59.5745361Z 2025-07-17T07:14:59.5745738Z Running test_cpp_api_parity 1/1 ... [2025-07-17 07:14:59.574426] 2025-07-17T07:14:59.5746154Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:14:59.5749273Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_api_parity.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:14:59.574738] 2025-07-17T07:15:09.0513086Z 2025-07-17T07:15:09.0513980Z test_cpp_api_parity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_api_parity_1.1_9876e08f0d0ad13a_.log 2025-07-17T07:15:09.0725816Z Running 488 items in this shard: test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_launch_configs_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_2d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda 2025-07-17T07:15:09.0915668Z 2025-07-17T07:15:09.0915935Z Running inductor/test_cutlass_backend 1/1 ... [2025-07-17 07:15:09.052320] 2025-07-17T07:15:09.0916408Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:15:09.0917606Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cutlass_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:15:09.052682] 2025-07-17T07:15:15.1346694Z 2025-07-17T07:15:15.1347705Z inductor/test_cutlass_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cutlass_backend_1.1_a05a533d0ebc535c_.log 2025-07-17T07:15:15.1348472Z 2025-07-17T07:15:15.1350510Z Running test_cpp_extensions_open_device_registration 1/1 ... [2025-07-17 07:15:15.134890] 2025-07-17T07:15:16.6117863Z -- The CXX compiler identification is Clang 12.0.1 2025-07-17T07:15:16.7206148Z -- The C compiler identification is Clang 12.0.1 2025-07-17T07:15:16.7463089Z -- Detecting CXX compiler ABI info 2025-07-17T07:15:16.9392383Z -- Detecting CXX compiler ABI info - done 2025-07-17T07:15:16.9527576Z -- Check for working CXX compiler: /opt/cache/bin/c++ - skipped 2025-07-17T07:15:16.9537214Z -- Detecting CXX compile features 2025-07-17T07:15:16.9549629Z -- Detecting CXX compile features - done 2025-07-17T07:15:16.9741793Z -- Detecting C compiler ABI info 2025-07-17T07:15:17.0924900Z -- Detecting C compiler ABI info - done 2025-07-17T07:15:17.1050970Z -- Check for working C compiler: /opt/cache/bin/cc - skipped 2025-07-17T07:15:17.1054549Z -- Detecting C compile features 2025-07-17T07:15:17.1059147Z -- Detecting C compile features - done 2025-07-17T07:15:17.2375809Z CMake Warning at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message): 2025-07-17T07:15:17.2376611Z static library kineto_LIBRARY-NOTFOUND not found. 2025-07-17T07:15:17.2377003Z Call Stack (most recent call first): 2025-07-17T07:15:17.2377723Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:125 (append_torchlib_if_found) 2025-07-17T07:15:17.2378444Z CMakeLists.txt:27 (find_package) 2025-07-17T07:15:17.2378661Z 2025-07-17T07:15:17.2378916Z  2025-07-17T07:15:17.2384178Z -- Found Torch: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so 2025-07-17T07:15:17.2449123Z -- Configuring done (0.8s) 2025-07-17T07:15:17.2573906Z -- Generating done (0.0s) 2025-07-17T07:15:17.2587476Z -- Build files have been written to: /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/build 2025-07-17T07:15:17.4023816Z [ 5%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/device.cpp.o 2025-07-17T07:15:17.4025436Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/memory.cpp.o 2025-07-17T07:15:17.5050534Z [ 17%] Linking CXX shared library libopenreg.so 2025-07-17T07:15:17.5842526Z [ 17%] Built target openreg 2025-07-17T07:15:17.5936518Z [ 23%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegExtra.cpp.o 2025-07-17T07:15:17.5939466Z [ 29%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegMinimal.cpp.o 2025-07-17T07:15:17.5940987Z [ 35%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Extra.cpp.o 2025-07-17T07:15:17.5945986Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegDeviceAllocator.cpp.o 2025-07-17T07:15:17.5947659Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Minimal.cpp.o 2025-07-17T07:15:17.5949910Z [ 52%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegFunctions.cpp.o 2025-07-17T07:15:17.5951431Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGenerator.cpp.o 2025-07-17T07:15:17.5954903Z [ 64%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGuard.cpp.o 2025-07-17T07:15:17.5961046Z [ 70%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHooks.cpp.o 2025-07-17T07:15:17.5963078Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHostAllocator.cpp.o 2025-07-17T07:15:17.5964751Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegSerialization.cpp.o 2025-07-17T07:15:18.1451069Z [ 88%] Linking CXX shared library libtorch_openreg.so 2025-07-17T07:15:18.5877534Z [ 88%] Built target torch_openreg 2025-07-17T07:15:18.5965922Z [ 94%] Building CXX object torch_openreg/csrc/CMakeFiles/torch_bindings.dir/Module.cpp.o 2025-07-17T07:15:19.1449515Z [100%] Linking CXX shared library libtorch_bindings.so 2025-07-17T07:15:19.2870528Z [100%] Built target torch_bindings 2025-07-17T07:15:19.2935457Z Install the project... 2025-07-17T07:15:19.2961966Z -- Install configuration: "" 2025-07-17T07:15:19.3568885Z running install 2025-07-17T07:15:19.3583069Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T07:15:19.3583884Z !! 2025-07-17T07:15:19.3584018Z 2025-07-17T07:15:19.3584159Z ******************************************************************************** 2025-07-17T07:15:19.3584687Z Please avoid running ``setup.py`` directly. 2025-07-17T07:15:19.3585103Z Instead, use pypa/build, pypa/installer or other 2025-07-17T07:15:19.3585489Z standards-based tools. 2025-07-17T07:15:19.3585681Z 2025-07-17T07:15:19.3585910Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T07:15:19.3586404Z or your builds will no longer be supported. 2025-07-17T07:15:19.3586660Z 2025-07-17T07:15:19.3586983Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T07:15:19.3587521Z ******************************************************************************** 2025-07-17T07:15:19.3587766Z 2025-07-17T07:15:19.3587848Z !! 2025-07-17T07:15:19.3588071Z self.initialize_options() 2025-07-17T07:15:19.3716798Z running build 2025-07-17T07:15:19.3717182Z running build_py 2025-07-17T07:15:19.3802017Z creating build/lib.linux-x86_64-cpython-313/torch_openreg 2025-07-17T07:15:19.3803837Z copying torch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg 2025-07-17T07:15:19.3813275Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-07-17T07:15:19.3814228Z copying torch_openreg/openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-07-17T07:15:19.3822529Z copying torch_openreg/openreg/random.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-07-17T07:15:19.3833880Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:19.3835000Z copying torch_openreg/lib/libopenreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:19.3848380Z copying torch_openreg/lib/libtorch_openreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:19.3958683Z copying torch_openreg/lib/libtorch_bindings.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:19.3999356Z running build_ext 2025-07-17T07:15:19.4129643Z building 'torch_openreg._C' extension 2025-07-17T07:15:19.4131502Z creating build/temp.linux-x86_64-cpython-313/torch_openreg/csrc 2025-07-17T07:15:19.4134892Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_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/include/python3.13 -c torch_openreg/csrc/stub.c -o build/temp.linux-x86_64-cpython-313/torch_openreg/csrc/stub.o -g -Wall -Werror 2025-07-17T07:15:19.5685549Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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/torch_openreg/csrc/stub.o -L/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/lib -ltorch_bindings -o build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -Wl,-rpath,$ORIGIN/lib 2025-07-17T07:15:19.5927963Z running install_lib 2025-07-17T07:15:19.6057533Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-07-17T07:15:19.6060713Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-07-17T07:15:19.6062000Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-07-17T07:15:19.6063721Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg 2025-07-17T07:15:19.6064994Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/openreg/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg 2025-07-17T07:15:19.6066838Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/openreg/random.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg 2025-07-17T07:15:19.6067990Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:19.6069179Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libopenreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:19.6071297Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_openreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:19.6091667Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_bindings.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:19.6097550Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-07-17T07:15:19.6103030Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/__init__.py to __init__.cpython-313.pyc 2025-07-17T07:15:19.6106665Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg/__init__.py to __init__.cpython-313.pyc 2025-07-17T07:15:19.6112468Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg/random.py to random.cpython-313.pyc 2025-07-17T07:15:19.6117617Z running install_egg_info 2025-07-17T07:15:19.6335593Z running egg_info 2025-07-17T07:15:19.6411903Z creating torch_openreg.egg-info 2025-07-17T07:15:19.6413089Z writing torch_openreg.egg-info/PKG-INFO 2025-07-17T07:15:19.6416806Z writing dependency_links to torch_openreg.egg-info/dependency_links.txt 2025-07-17T07:15:19.6418677Z writing requirements to torch_openreg.egg-info/requires.txt 2025-07-17T07:15:19.6419722Z writing top-level names to torch_openreg.egg-info/top_level.txt 2025-07-17T07:15:19.6420763Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T07:15:19.6504912Z reading manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T07:15:19.6512221Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T07:15:19.6513613Z Copying torch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg-0.0.1-py3.13.egg-info 2025-07-17T07:15:19.6519855Z running install_scripts 2025-07-17T07:15:19.9096636Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:15:19.9100311Z 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'] ... [2025-07-17 07:15:19.909805] 2025-07-17T07:15:24.0794728Z 2025-07-17T07:15:24.0796058Z 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_ee527c1a109f2ca1_.log 2025-07-17T07:15:24.0798788Z Running 3 items in this shard: test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRegistration::test_open_device_scalar_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRegistration::test_open_device_tensor_type_fallback, test/test_cpp_extensions_open_device_registration.py::TestCppExtensionOpenRegistration::test_open_device_tensorlist_type_fallback 2025-07-17T07:15:24.0800681Z 2025-07-17T07:15:24.0800943Z Running test_transformers_privateuse1 1/1 ... [2025-07-17 07:15:24.079758] 2025-07-17T07:15:25.4414388Z -- The CXX compiler identification is Clang 12.0.1 2025-07-17T07:15:25.5129981Z -- The C compiler identification is Clang 12.0.1 2025-07-17T07:15:25.5211356Z -- Detecting CXX compiler ABI info 2025-07-17T07:15:25.6701169Z -- Detecting CXX compiler ABI info - done 2025-07-17T07:15:25.6836114Z -- Check for working CXX compiler: /opt/cache/bin/c++ - skipped 2025-07-17T07:15:25.6839455Z -- Detecting CXX compile features 2025-07-17T07:15:25.6845362Z -- Detecting CXX compile features - done 2025-07-17T07:15:25.6957367Z -- Detecting C compiler ABI info 2025-07-17T07:15:25.8164331Z -- Detecting C compiler ABI info - done 2025-07-17T07:15:25.8292527Z -- Check for working C compiler: /opt/cache/bin/cc - skipped 2025-07-17T07:15:25.8296725Z -- Detecting C compile features 2025-07-17T07:15:25.8301353Z -- Detecting C compile features - done 2025-07-17T07:15:25.9391071Z CMake Warning at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message): 2025-07-17T07:15:25.9391867Z static library kineto_LIBRARY-NOTFOUND not found. 2025-07-17T07:15:25.9392255Z Call Stack (most recent call first): 2025-07-17T07:15:25.9392979Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:125 (append_torchlib_if_found) 2025-07-17T07:15:25.9393925Z CMakeLists.txt:27 (find_package) 2025-07-17T07:15:25.9394132Z 2025-07-17T07:15:25.9394264Z  2025-07-17T07:15:25.9399437Z -- Found Torch: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so 2025-07-17T07:15:25.9424392Z -- Configuring done (0.7s) 2025-07-17T07:15:25.9540722Z -- Generating done (0.0s) 2025-07-17T07:15:25.9580060Z -- Build files have been written to: /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/build 2025-07-17T07:15:26.1021765Z [ 5%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/device.cpp.o 2025-07-17T07:15:26.1023454Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/memory.cpp.o 2025-07-17T07:15:26.2035059Z [ 17%] Linking CXX shared library libopenreg.so 2025-07-17T07:15:26.2815940Z [ 17%] Built target openreg 2025-07-17T07:15:26.2909186Z [ 23%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegExtra.cpp.o 2025-07-17T07:15:26.2910614Z [ 29%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegMinimal.cpp.o 2025-07-17T07:15:26.2912784Z [ 35%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Extra.cpp.o 2025-07-17T07:15:26.2916526Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Minimal.cpp.o 2025-07-17T07:15:26.2917503Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegDeviceAllocator.cpp.o 2025-07-17T07:15:26.2922224Z [ 52%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegFunctions.cpp.o 2025-07-17T07:15:26.2924037Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGenerator.cpp.o 2025-07-17T07:15:26.2925605Z [ 64%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGuard.cpp.o 2025-07-17T07:15:26.2933770Z [ 70%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHooks.cpp.o 2025-07-17T07:15:26.2936962Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHostAllocator.cpp.o 2025-07-17T07:15:26.2938235Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegSerialization.cpp.o 2025-07-17T07:15:26.8960407Z [ 88%] Linking CXX shared library libtorch_openreg.so 2025-07-17T07:15:27.3431011Z [ 88%] Built target torch_openreg 2025-07-17T07:15:27.3519813Z [ 94%] Building CXX object torch_openreg/csrc/CMakeFiles/torch_bindings.dir/Module.cpp.o 2025-07-17T07:15:27.9062804Z [100%] Linking CXX shared library libtorch_bindings.so 2025-07-17T07:15:28.0498004Z [100%] Built target torch_bindings 2025-07-17T07:15:28.0567008Z Install the project... 2025-07-17T07:15:28.0595018Z -- Install configuration: "" 2025-07-17T07:15:28.1184209Z running install 2025-07-17T07:15:28.1186606Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T07:15:28.1187590Z !! 2025-07-17T07:15:28.1187719Z 2025-07-17T07:15:28.1187865Z ******************************************************************************** 2025-07-17T07:15:28.1188249Z Please avoid running ``setup.py`` directly. 2025-07-17T07:15:28.1188664Z Instead, use pypa/build, pypa/installer or other 2025-07-17T07:15:28.1189041Z standards-based tools. 2025-07-17T07:15:28.1189230Z 2025-07-17T07:15:28.1189473Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T07:15:28.1189960Z or your builds will no longer be supported. 2025-07-17T07:15:28.1190220Z 2025-07-17T07:15:28.1190528Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T07:15:28.1191069Z ******************************************************************************** 2025-07-17T07:15:28.1191381Z 2025-07-17T07:15:28.1191463Z !! 2025-07-17T07:15:28.1191693Z self.initialize_options() 2025-07-17T07:15:28.1321622Z running build 2025-07-17T07:15:28.1321913Z running build_py 2025-07-17T07:15:28.1409238Z creating build/lib.linux-x86_64-cpython-313/torch_openreg 2025-07-17T07:15:28.1410530Z copying torch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg 2025-07-17T07:15:28.1413070Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-07-17T07:15:28.1414136Z copying torch_openreg/openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-07-17T07:15:28.1416055Z copying torch_openreg/openreg/random.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-07-17T07:15:28.1421589Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:28.1422606Z copying torch_openreg/lib/libopenreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:28.1438629Z copying torch_openreg/lib/libtorch_openreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:28.1551862Z copying torch_openreg/lib/libtorch_bindings.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:28.1592369Z running build_ext 2025-07-17T07:15:28.1694987Z building 'torch_openreg._C' extension 2025-07-17T07:15:28.1696866Z creating build/temp.linux-x86_64-cpython-313/torch_openreg/csrc 2025-07-17T07:15:28.1700462Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_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/include/python3.13 -c torch_openreg/csrc/stub.c -o build/temp.linux-x86_64-cpython-313/torch_openreg/csrc/stub.o -g -Wall -Werror 2025-07-17T07:15:28.3056637Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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/torch_openreg/csrc/stub.o -L/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/lib -ltorch_bindings -o build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -Wl,-rpath,$ORIGIN/lib 2025-07-17T07:15:28.3288809Z running install_lib 2025-07-17T07:15:28.3383818Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libopenreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:28.3386359Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_openreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:28.3409517Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_bindings.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:28.3416351Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-07-17T07:15:28.3422093Z running install_egg_info 2025-07-17T07:15:28.3613529Z running egg_info 2025-07-17T07:15:28.3691033Z writing torch_openreg.egg-info/PKG-INFO 2025-07-17T07:15:28.3694776Z writing dependency_links to torch_openreg.egg-info/dependency_links.txt 2025-07-17T07:15:28.3704559Z writing requirements to torch_openreg.egg-info/requires.txt 2025-07-17T07:15:28.3710876Z writing top-level names to torch_openreg.egg-info/top_level.txt 2025-07-17T07:15:28.3800134Z reading manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T07:15:28.3809213Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T07:15:28.3816653Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg-0.0.1-py3.13.egg-info' (and everything under it) 2025-07-17T07:15:28.3818281Z Copying torch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg-0.0.1-py3.13.egg-info 2025-07-17T07:15:28.3824735Z running install_scripts 2025-07-17T07:15:28.6362365Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:15:28.6365641Z 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'] ... [2025-07-17 07:15:28.636350] 2025-07-17T07:15:31.6043732Z 2025-07-17T07:15:31.6044706Z test_transformers_privateuse1 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_privateuse1_1.1_494fd36b3b297993_.log 2025-07-17T07:15:31.6047149Z 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 2025-07-17T07:15:31.6048967Z 2025-07-17T07:15:31.6049225Z Running inductor/test_flex_attention 1/1 ... [2025-07-17 07:15:31.604622] 2025-07-17T07:15:31.6049700Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:15:31.6051807Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_flex_attention.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:15:31.604964] 2025-07-17T07:15:37.2264703Z 2025-07-17T07:15:37.2265670Z inductor/test_flex_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_flex_attention_1.1_906b3496b5303aa6_.log 2025-07-17T07:15:37.2613939Z Running 636 items in this shard: test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_causal_mask_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_GQA_score_mod7_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod0_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod1_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod2_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod3_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod4_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_aot_eager_gradcheck_score_mod5_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_autograd_function_in_score_mod_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_block_mask_non_divisible_cpu, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod0_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod1_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod3_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod4_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod5_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod6_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_automatic_dynamic_score_mod7_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE2_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE2_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE2_cpu_float32, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE3_cpu_bfloat16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE3_cpu_float16, test/inductor/test_flex_attention.py::TestFlexAttentionCPU::test_builtin_score_mods_different_block_size_score_mod0_BLOCK_SIZE3_cpu_float32, 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test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_relative_1d_bias_only_grad_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_default_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_symmetric_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_mode_max-autotune-no-cudagraphs_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:256_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:277_headdim:16_dtype:float32_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float16_cpu, test/inductor/test_flex_attention.py::TestLearnableBiasesCPU::test_weird_bias_batch:2_head:4_seq_len:37_headdim:16_dtype:float32_cpu 2025-07-17T07:15:37.2956884Z 2025-07-17T07:15:37.2957076Z Running test_openreg 1/1 ... [2025-07-17 07:15:37.227889] 2025-07-17T07:15:38.5876262Z -- The CXX compiler identification is Clang 12.0.1 2025-07-17T07:15:38.6582023Z -- The C compiler identification is Clang 12.0.1 2025-07-17T07:15:38.6661648Z -- Detecting CXX compiler ABI info 2025-07-17T07:15:38.8505071Z -- Detecting CXX compiler ABI info - done 2025-07-17T07:15:38.8639973Z -- Check for working CXX compiler: /opt/cache/bin/c++ - skipped 2025-07-17T07:15:38.8643462Z -- Detecting CXX compile features 2025-07-17T07:15:38.8650282Z -- Detecting CXX compile features - done 2025-07-17T07:15:38.8761853Z -- Detecting C compiler ABI info 2025-07-17T07:15:38.9958956Z -- Detecting C compiler ABI info - done 2025-07-17T07:15:39.0087250Z -- Check for working C compiler: /opt/cache/bin/cc - skipped 2025-07-17T07:15:39.0090415Z -- Detecting C compile features 2025-07-17T07:15:39.0094845Z -- Detecting C compile features - done 2025-07-17T07:15:39.1177967Z CMake Warning at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message): 2025-07-17T07:15:39.1178759Z static library kineto_LIBRARY-NOTFOUND not found. 2025-07-17T07:15:39.1179132Z Call Stack (most recent call first): 2025-07-17T07:15:39.1179861Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:125 (append_torchlib_if_found) 2025-07-17T07:15:39.1180596Z CMakeLists.txt:27 (find_package) 2025-07-17T07:15:39.1180803Z 2025-07-17T07:15:39.1180925Z  2025-07-17T07:15:39.1186696Z -- Found Torch: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so 2025-07-17T07:15:39.1211417Z -- Configuring done (0.7s) 2025-07-17T07:15:39.1327036Z -- Generating done (0.0s) 2025-07-17T07:15:39.1339958Z -- Build files have been written to: /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/build 2025-07-17T07:15:39.2791627Z [ 5%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/device.cpp.o 2025-07-17T07:15:39.2793245Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/memory.cpp.o 2025-07-17T07:15:39.3796654Z [ 17%] Linking CXX shared library libopenreg.so 2025-07-17T07:15:39.4583424Z [ 17%] Built target openreg 2025-07-17T07:15:39.4676337Z [ 23%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegExtra.cpp.o 2025-07-17T07:15:39.4680836Z [ 35%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegMinimal.cpp.o 2025-07-17T07:15:39.4682490Z [ 35%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Extra.cpp.o 2025-07-17T07:15:39.4683743Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Minimal.cpp.o 2025-07-17T07:15:39.4688214Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegDeviceAllocator.cpp.o 2025-07-17T07:15:39.4690084Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegFunctions.cpp.o 2025-07-17T07:15:39.4691654Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGenerator.cpp.o 2025-07-17T07:15:39.4696753Z [ 64%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGuard.cpp.o 2025-07-17T07:15:39.4702337Z [ 70%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHooks.cpp.o 2025-07-17T07:15:39.4703998Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHostAllocator.cpp.o 2025-07-17T07:15:39.4706894Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegSerialization.cpp.o 2025-07-17T07:15:40.0165618Z [ 88%] Linking CXX shared library libtorch_openreg.so 2025-07-17T07:15:40.4708845Z [ 88%] Built target torch_openreg 2025-07-17T07:15:40.4798141Z [ 94%] Building CXX object torch_openreg/csrc/CMakeFiles/torch_bindings.dir/Module.cpp.o 2025-07-17T07:15:41.0285455Z [100%] Linking CXX shared library libtorch_bindings.so 2025-07-17T07:15:41.1712340Z [100%] Built target torch_bindings 2025-07-17T07:15:41.1780497Z Install the project... 2025-07-17T07:15:41.1807111Z -- Install configuration: "" 2025-07-17T07:15:41.2389022Z running install 2025-07-17T07:15:41.2391547Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T07:15:41.2392349Z !! 2025-07-17T07:15:41.2392459Z 2025-07-17T07:15:41.2392591Z ******************************************************************************** 2025-07-17T07:15:41.2393005Z Please avoid running ``setup.py`` directly. 2025-07-17T07:15:41.2393558Z Instead, use pypa/build, pypa/installer or other 2025-07-17T07:15:41.2393938Z standards-based tools. 2025-07-17T07:15:41.2394133Z 2025-07-17T07:15:41.2394378Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T07:15:41.2394859Z or your builds will no longer be supported. 2025-07-17T07:15:41.2395118Z 2025-07-17T07:15:41.2395432Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T07:15:41.2395982Z ******************************************************************************** 2025-07-17T07:15:41.2396227Z 2025-07-17T07:15:41.2396321Z !! 2025-07-17T07:15:41.2396530Z self.initialize_options() 2025-07-17T07:15:41.2526579Z running build 2025-07-17T07:15:41.2526841Z running build_py 2025-07-17T07:15:41.2612822Z creating build/lib.linux-x86_64-cpython-313/torch_openreg 2025-07-17T07:15:41.2614507Z copying torch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg 2025-07-17T07:15:41.2617246Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-07-17T07:15:41.2618423Z copying torch_openreg/openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-07-17T07:15:41.2620216Z copying torch_openreg/openreg/random.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-07-17T07:15:41.2625633Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:41.2626770Z copying torch_openreg/lib/libopenreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:41.2642934Z copying torch_openreg/lib/libtorch_openreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:41.2760064Z copying torch_openreg/lib/libtorch_bindings.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-07-17T07:15:41.2800024Z running build_ext 2025-07-17T07:15:41.2899644Z building 'torch_openreg._C' extension 2025-07-17T07:15:41.2901437Z creating build/temp.linux-x86_64-cpython-313/torch_openreg/csrc 2025-07-17T07:15:41.2904850Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_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/include/python3.13 -c torch_openreg/csrc/stub.c -o build/temp.linux-x86_64-cpython-313/torch_openreg/csrc/stub.o -g -Wall -Werror 2025-07-17T07:15:41.4012549Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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/torch_openreg/csrc/stub.o -L/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/lib -ltorch_bindings -o build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -Wl,-rpath,$ORIGIN/lib 2025-07-17T07:15:41.4240117Z running install_lib 2025-07-17T07:15:41.4331135Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libopenreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:41.4333397Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_openreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:41.4356490Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_bindings.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-07-17T07:15:41.4363120Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-07-17T07:15:41.4368350Z running install_egg_info 2025-07-17T07:15:41.4556675Z running egg_info 2025-07-17T07:15:41.4633874Z writing torch_openreg.egg-info/PKG-INFO 2025-07-17T07:15:41.4637280Z writing dependency_links to torch_openreg.egg-info/dependency_links.txt 2025-07-17T07:15:41.4639353Z writing requirements to torch_openreg.egg-info/requires.txt 2025-07-17T07:15:41.4640995Z writing top-level names to torch_openreg.egg-info/top_level.txt 2025-07-17T07:15:41.4724415Z reading manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T07:15:41.4733228Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-07-17T07:15:41.4734910Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg-0.0.1-py3.13.egg-info' (and everything under it) 2025-07-17T07:15:41.4736540Z Copying torch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg-0.0.1-py3.13.egg-info 2025-07-17T07:15:41.4742852Z running install_scripts 2025-07-17T07:15:41.7285254Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:15:41.7288539Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_openreg.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:15:41.728619] 2025-07-17T07:15:45.8983328Z 2025-07-17T07:15:45.8984171Z test_openreg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_openreg_1.1_3c6c80d3f33f1bec_.log 2025-07-17T07:15:45.8995741Z Running 41 items in this shard: test/test_openreg.py::TestPrivateUse1::test_backend_dispatchstub, test/test_openreg.py::TestPrivateUse1::test_backend_generate_methods, test/test_openreg.py::TestPrivateUse1::test_backend_module_function, test/test_openreg.py::TestPrivateUse1::test_backend_module_methods, test/test_openreg.py::TestPrivateUse1::test_backend_module_registration, test/test_openreg.py::TestPrivateUse1::test_backend_name, test/test_openreg.py::TestPrivateUse1::test_backend_operator_registration, test/test_openreg.py::TestPrivateUse1::test_backend_packed_sequence_methods, test/test_openreg.py::TestPrivateUse1::test_backend_storage_methods, test/test_openreg.py::TestPrivateUse1::test_backend_tensor_methods, test/test_openreg.py::TestPrivateUse1::test_backend_tensor_type, test/test_openreg.py::TestPrivateUse1::test_backend_type_methods, test/test_openreg.py::TestOpenReg::test_autograd_init, test/test_openreg.py::TestOpenReg::test_compile_autograd_function_aliasing, test/test_openreg.py::TestOpenReg::test_compile_autograd_function_returns_self, test/test_openreg.py::TestOpenReg::test_copy_same_device, test/test_openreg.py::TestOpenReg::test_cross_device_copy, test/test_openreg.py::TestOpenReg::test_cross_diff_devices_copy, test/test_openreg.py::TestOpenReg::test_data_dependent_output, test/test_openreg.py::TestOpenReg::test_event_elapsed_time, test/test_openreg.py::TestOpenReg::test_event_wait_stream, test/test_openreg.py::TestOpenReg::test_expand, test/test_openreg.py::TestOpenReg::test_factory, test/test_openreg.py::TestOpenReg::test_fake_tensor, test/test_openreg.py::TestOpenReg::test_generator, test/test_openreg.py::TestOpenReg::test_manual_seed, test/test_openreg.py::TestOpenReg::test_named_tensor, test/test_openreg.py::TestOpenReg::test_open_device_cpu_serialization, test/test_openreg.py::TestOpenReg::test_open_device_dlpack, test/test_openreg.py::TestOpenReg::test_open_device_numpy_serialization, test/test_openreg.py::TestOpenReg::test_pin_memory, test/test_openreg.py::TestOpenReg::test_printing, test/test_openreg.py::TestOpenReg::test_quantize, test/test_openreg.py::TestOpenReg::test_record_event, test/test_openreg.py::TestOpenReg::test_resize, test/test_openreg.py::TestOpenReg::test_rewrapped_storage, test/test_openreg.py::TestOpenReg::test_rng_state, test/test_openreg.py::TestOpenReg::test_serialization, test/test_openreg.py::TestOpenReg::test_stream_synchronize, test/test_openreg.py::TestOpenReg::test_stream_wait_event, test/test_openreg.py::TestOpenReg::test_stream_wait_stream 2025-07-17T07:15:45.9006532Z 2025-07-17T07:15:45.9006746Z Running test_fx 1/1 ... [2025-07-17 07:15:45.898590] 2025-07-17T07:15:45.9007116Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:15:45.9008140Z 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'] ... [2025-07-17 07:15:45.898916] 2025-07-17T07:19:31.1197071Z 2025-07-17T07:19:31.1197935Z test_fx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.1_1e63d33459a3f7c2_.log 2025-07-17T07:19:31.1684926Z Running 1259 items in this shard: test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationInput_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationMetadata_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationTorchTensorCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_Mutation_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_ReturnList_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_TakeList_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_FactoryFunctionCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_MutationFactory_cpu, test/test_fx.py::TestCSEPass::test_banned_list, test/test_fx.py::TestCSEPass::test_empty, test/test_fx.py::TestCSEPass::test_immutable_list_multiple_entries, test/test_fx.py::TestCSEPass::test_immutable_list_type, test/test_fx.py::TestCSEPass::test_kwarg, test/test_fx.py::TestCSEPass::test_nested_immutable_list_type, test/test_fx.py::TestCSEPass::test_nochange, test/test_fx.py::TestCSEPass::test_rand_like, test/test_fx.py::TestCSEPass::test_rand_n, test/test_fx.py::TestCSEPass::test_random, test/test_fx.py::TestCSEPass::test_simple, test/test_fx.py::TestCSEPass::test_simple_2, test/test_fx.py::TestCSEPass::test_simple_multiple_same_ops, test/test_fx.py::TestCSEPass::test_two_args, test/test_fx.py::TestCSEPass::test_two_args_default, test/test_fx.py::TestDCE::test_dead_chain, test/test_fx.py::TestDCE::test_dead_getattr, test/test_fx.py::TestDCE::test_dead_placeholder, test/test_fx.py::TestDCE::test_dead_placeholder_with_user, test/test_fx.py::TestDCE::test_impure_custom, test/test_fx.py::TestDCE::test_impure_kwargs, test/test_fx.py::TestDCE::test_impure_nodes_args, test/test_fx.py::TestDCE::test_impure_random, test/test_fx.py::TestDCE::test_keep_collectives, test/test_fx.py::TestDCE::test_keep_collectives_no_overload, test/test_fx.py::TestDCE::test_keep_module_with_side_effects, test/test_fx.py::TestDCE::test_keep_setitem, test/test_fx.py::TestDCE::test_keep_torch_assert, test/test_fx.py::TestDCE::test_simple, test/test_fx.py::TestConstFold::test_check_inline_non_const, test/test_fx.py::TestConstFold::test_check_inline_non_const_mult_return, test/test_fx.py::TestConstFold::test_check_skip_folding_quant_dequant_pattern, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_no_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_placeholder_reordered, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr_three_input, test/test_fx.py::TestConstFold::test_const_fold_has_inlined_call_module_node, test/test_fx.py::TestConstFold::test_const_fold_module_attr, test/test_fx.py::TestConstFold::test_const_fold_multi_const_folded_attrs, test/test_fx.py::TestConstFold::test_const_fold_noop, test/test_fx.py::TestConstFold::test_const_fold_submod_hierarchy, test/test_fx.py::TestConstFold::test_const_fold_tensor_meta, test/test_fx.py::TestConstFold::test_const_fold_unused_placeholder, test/test_fx.py::TestConstFold::test_dict_output, test/test_fx.py::TestConstFold::test_fold_module, test/test_fx.py::TestConstFold::test_retain_node_meta, test/test_fx.py::TestConstFold::test_three_outputs, test/test_fx.py::TestConstFold::test_two_outputs, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_dim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_ndim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_nelement_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_numel_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_shape_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_size_const, test/test_fx.py::AnnotationsTest::test_annotate, test/test_fx.py::AnnotationsTest::test_annotations, test/test_fx.py::AnnotationsTest::test_broadcasting1, test/test_fx.py::AnnotationsTest::test_broadcasting2, test/test_fx.py::AnnotationsTest::test_broadcasting3, test/test_fx.py::AnnotationsTest::test_consistency, test/test_fx.py::AnnotationsTest::test_precision, test/test_fx.py::TypeCheckerTest::test_flatten_fully_static, test/test_fx.py::TypeCheckerTest::test_resnet50, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast_2, test/test_fx.py::TypeCheckerTest::test_type_check_add_false, test/test_fx.py::TypeCheckerTest::test_type_check_add_true, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_scalar, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_false, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_symbolic, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2_fully_static, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_types, test/test_fx.py::TypeCheckerTest::test_type_check_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_flatten3, test/test_fx.py::TypeCheckerTest::test_type_check_flatten_2, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_true, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_true_param_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_true, test/test_fx.py::TypeCheckerTest::test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_transpose_False, test/test_fx.py::TypeCheckerTest::test_type_check_transpose_true, test/test_fx.py::TypeCheckerTest::test_type_maxpool2d_fully_static, test/test_fx.py::TypeCheckerTest::test_type_typechecl_maxpool2d_3dinput, test/test_fx.py::TypeCheckerTest::test_typecheck_basicblock, test/test_fx.py::TestMatcher::test_matcher_with_name_node_map_function, test/test_fx.py::TestMatcher::test_matcher_with_name_node_map_module, test/test_fx.py::TestMatcher::test_split_to_graph_and_name_node_map, test/test_fx.py::TestMatcher::test_subgraph_matcher_ignore_literals, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_attributes, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list_bad, test/test_fx.py::TestMatcher::test_variatic_arg_matching, test/test_fx.py::TestPassManager::test_pass_manager, test/test_fx.py::TestPassManager::test_pass_manager_bad_checks, test/test_fx.py::TestPassManager::test_pass_manager_checks, test/test_fx.py::TestPassManager::test_pass_manager_error, test/test_fx.py::TestPassManager::test_this_before_that_pass_constraint, test/test_fx.py::TestPassManager::test_topological_sort, test/test_fx.py::TestSourceMatcher::test_legalize_slice, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_True, test/test_fx.py::TestSubgraphRewriter::test_matching_pattern_with_list_type_arg, test/test_fx.py::TestSubgraphRewriter::test_matching_variable_arguments, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_callback, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_filters, test/test_fx.py::TestSubgraphRewriter::test_replaced_nodes, test/test_fx.py::TestSubgraphRewriter::test_replacement_with_attrs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_call_method, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_correct_output_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_graph_argument_order, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_local_revert, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_multiple_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_nodes_with_kwargs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_is_entire_graph, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_placeholder_matching, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_consecutive_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_duplicated_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_multiple_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_traced_as_callable, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_oneliner_pattern, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_overlapping_matches, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_trivial_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_args, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_results, test/test_fx.py::TestFX::test_all_input_nodes, test/test_fx.py::TestFX::test_annotation_with_future, test/test_fx.py::TestFX::test_annotations_empty_tuple, test/test_fx.py::TestFX::test_annotations_with_forward_references, test/test_fx.py::TestFX::test_annotations_with_no_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_internal_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_no_internal_forward_references, test/test_fx.py::TestFX::test_args_kwargs, test/test_fx.py::TestFX::test_args_kwargs_no_self, test/test_fx.py::TestFX::test_assert, test/test_fx.py::TestFX::test_ast_rewriter_reassigns_submodules, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert_with_message, test/test_fx.py::TestFX::test_ast_rewriter_wrap, test/test_fx.py::TestFX::test_ast_rewriter_wrap_fn_directly, test/test_fx.py::TestFX::test_ast_rewriter_wrap_with_submodule, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_autowrap_functions, test/test_fx.py::TestFX::test_concrete_arg_none_assert, test/test_fx.py::TestFX::test_construct_root_dict, test/test_fx.py::TestFX::test_control_flow_tracing, test/test_fx.py::TestFX::test_copy_it, test/test_fx.py::TestFX::test_copy_no_remap, test/test_fx.py::TestFX::test_ctx_mgr, test/test_fx.py::TestFX::test_custom_codegen, test/test_fx.py::TestFX::test_custom_codegen_with_transformer, test/test_fx.py::TestFX::test_custom_import, test/test_fx.py::TestFX::test_custom_proxy_dynamic_value, test/test_fx.py::TestFX::test_custom_proxy_input_dependent_control_flow, test/test_fx.py::TestFX::test_custom_proxy_type, test/test_fx.py::TestFX::test_custom_proxy_type_literal, test/test_fx.py::TestFX::test_custom_traceback_not_raised_when_exception_source_is_submodule, test/test_fx.py::TestFX::test_custom_traceback_raised_when_exception_source_is_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graph_with_tracer_cls, test/test_fx.py::TestFX::test_deepcopy_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graphmodule_with_transform, test/test_fx.py::TestFX::test_deepcopy_no_recursion, test/test_fx.py::TestFX::test_deepcopy_recursion_depth, test/test_fx.py::TestFX::test_deepcopy_tracer, test/test_fx.py::TestFX::test_deepcopy_with_submods_params, test/test_fx.py::TestFX::test_delete_unused_submodules_leaf, test/test_fx.py::TestFX::test_delete_unused_values, test/test_fx.py::TestFX::test_dict, test/test_fx.py::TestFX::test_direct_param_use, test/test_fx.py::TestFX::test_disallow_override, test/test_fx.py::TestFX::test_ellipsis, test/test_fx.py::TestFX::test_empty_graph_codegen, test/test_fx.py::TestFX::test_enum, test/test_fx.py::TestFX::test_erase_node_error, test/test_fx.py::TestFX::test_example_shape_prop, test/test_fx.py::TestFX::test_find_uses, test/test_fx.py::TestFX::test_fn_type_annotation_empty, test/test_fx.py::TestFX::test_fn_type_annotations, test/test_fx.py::TestFX::test_fx_and_or, test/test_fx.py::TestFX::test_fx_create_arg, test/test_fx.py::TestFX::test_fx_shifts, test/test_fx.py::TestFX::test_fx_stateless, test/test_fx.py::TestFX::test_get_torch_func_signature, test/test_fx.py::TestFX::test_getitem, test/test_fx.py::TestFX::test_getitem_subproc, test/test_fx.py::TestFX::test_graph_edit_with_proxy, test/test_fx.py::TestFX::test_graph_fns, test/test_fx.py::TestFX::test_graph_module, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_dict_init, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_mod_init, test/test_fx.py::TestFX::test_graph_module_replicate_for_dp, test/test_fx.py::TestFX::test_graph_unique_names, test/test_fx.py::TestFX::test_graph_unique_names_manual, test/test_fx.py::TestFX::test_immutable_dict_pytree_ops, test/test_fx.py::TestFX::test_immutable_list_pytree_ops, test/test_fx.py::TestFX::test_imul_code_print, test/test_fx.py::TestFX::test_inf_nan, test/test_fx.py::TestFX::test_inf_nan_kwds, test/test_fx.py::TestFX::test_informative_co_filename, test/test_fx.py::TestFX::test_inline_graph, test/test_fx.py::TestFX::test_insert_arg, test/test_fx.py::TestFX::test_insertion_point, test/test_fx.py::TestFX::test_interpreter, test/test_fx.py::TestFX::test_interpreter_default_args, test/test_fx.py::TestFX::test_interpreter_gc_values, test/test_fx.py::TestFX::test_interpreter_noop_resnet18, test/test_fx.py::TestFX::test_interpreter_not_enough_args, test/test_fx.py::TestFX::test_interpreter_onthefly_swap, test/test_fx.py::TestFX::test_interpreter_other_graph, test/test_fx.py::TestFX::test_interpreter_partial_eval, test/test_fx.py::TestFX::test_interpreter_run_node_override, test/test_fx.py::TestFX::test_interpreter_star_args, test/test_fx.py::TestFX::test_interpreter_with_codegen, test/test_fx.py::TestFX::test_layout, test/test_fx.py::TestFX::test_leaf_module, test/test_fx.py::TestFX::test_lineno_map, test/test_fx.py::TestFX::test_matmul_tracing, test/test_fx.py::TestFX::test_metadata_on_ph, test/test_fx.py::TestFX::test_module_deepcopy_edit_nodes, test/test_fx.py::TestFX::test_move_before, test/test_fx.py::TestFX::test_multi_insert_point, test/test_fx.py::TestFX::test_multiple_default_args, test/test_fx.py::TestFX::test_named_tuple_inlined, test/test_fx.py::TestFX::test_namedtuple_return_qualname, test/test_fx.py::TestFX::test_namedtuple_return_trace, test/test_fx.py::TestFX::test_native_callable, test/test_fx.py::TestFX::test_nn_module_stack, test/test_fx.py::TestFX::test_no_mutation, test/test_fx.py::TestFX::test_node_tagging, test/test_fx.py::TestFX::test_nonetype_annotation, test/test_fx.py::TestFX::test_partial_trace, test/test_fx.py::TestFX::test_pickle_custom_import, test/test_fx.py::TestFX::test_pickle_graphmodule, test/test_fx.py::TestFX::test_pickle_nonetype_annotation, test/test_fx.py::TestFX::test_pickle_torch_custom_ops, test/test_fx.py::TestFX::test_prepend_self, test/test_fx.py::TestFX::test_pretty_print, test/test_fx.py::TestFX::test_pretty_print_graph, test/test_fx.py::TestFX::test_pretty_print_node, test/test_fx.py::TestFX::test_pretty_print_targets, test/test_fx.py::TestFX::test_print_graph, test/test_fx.py::TestFX::test_profiler_ranges_side_effect, test/test_fx.py::TestFX::test_proxy_deepcopy_with_tracer, test/test_fx.py::TestFX::test_proxy_deepcopy_without_tracer, test/test_fx.py::TestFX::test_pytree, test/test_fx.py::TestFX::test_pytree_concrete, test/test_fx.py::TestFX::test_reassign_args_kwargs_uses, test/test_fx.py::TestFX::test_regular_and_default_args, test/test_fx.py::TestFX::test_remove_uses, test/test_fx.py::TestFX::test_remove_uses_with_custom_filter, test/test_fx.py::TestFX::test_replace_input, test/test_fx.py::TestFX::test_replace_uses, test/test_fx.py::TestFX::test_reserved_getattr, test/test_fx.py::TestFX::test_return_tuple, test/test_fx.py::TestFX::test_return_type_exists, test/test_fx.py::TestFX::test_return_type_exists_pre_pep585, test/test_fx.py::TestFX::test_script_method_trace, test/test_fx.py::TestFX::test_script_tensor_constant, test/test_fx.py::TestFX::test_sequential, test/test_fx.py::TestFX::test_shape_prop_aggregate, test/test_fx.py::TestFX::test_shape_prop_layout, test/test_fx.py::TestFX::test_shape_prop_layout_3d, test/test_fx.py::TestFX::test_shape_prop_unbacked_sym, test/test_fx.py::TestFX::test_single_default_arg, test/test_fx.py::TestFX::test_snake_case, test/test_fx.py::TestFX::test_sqrt, test/test_fx.py::TestFX::test_stack_traces, test/test_fx.py::TestFX::test_stack_traces_with_transformer, test/test_fx.py::TestFX::test_string_literal_return, test/test_fx.py::TestFX::test_submodule_manipulation_API, test/test_fx.py::TestFX::test_symbolic_trace_assert, test/test_fx.py::TestFX::test_symbolic_trace_sequential, test/test_fx.py::TestFX::test_tensor_attribute, test/test_fx.py::TestFX::test_tensor_attribute_coalseced, test/test_fx.py::TestFX::test_tensor_constant, test/test_fx.py::TestFX::test_throw_out_variant, test/test_fx.py::TestFX::test_torch_custom_ops, test/test_fx.py::TestFX::test_torch_fx_getattr, test/test_fx.py::TestFX::test_torch_fx_len, test/test_fx.py::TestFX::test_torch_op_overloads, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx_tensor_arg, test/test_fx.py::TestFX::test_trace_buffer_slice, test/test_fx.py::TestFX::test_trace_dict_int_keys, test/test_fx.py::TestFX::test_trace_dict_proxy_keys, test/test_fx.py::TestFX::test_trace_fn_constant, test/test_fx.py::TestFX::test_trace_function, test/test_fx.py::TestFX::test_trace_multiple_funcs, test/test_fx.py::TestFX::test_trace_return_dataclass, test/test_fx.py::TestFX::test_trace_return_dataclass_nested, test/test_fx.py::TestFX::test_trace_return_namedtuple, test/test_fx.py::TestFX::test_tracing_graphmodules_as_leaf_submodules, test/test_fx.py::TestFX::test_transformer_multi_outputs, test/test_fx.py::TestFX::test_transformer_noop, test/test_fx.py::TestFX::test_transformer_op_swap, test/test_fx.py::TestFX::test_transformer_preserves_nn_module_stack_for_get_attr, test/test_fx.py::TestFX::test_tuple_no_subscript, test/test_fx.py::TestFX::test_typename_print, test/test_fx.py::TestFX::test_typename_print_pre_pep585, test/test_fx.py::TestFX::test_unpack, test/test_fx.py::TestFX::test_unpack_dict_better_error, test/test_fx.py::TestFX::test_unpack_list_better_error, test/test_fx.py::TestFX::test_update_args_api, test/test_fx.py::TestFX::test_update_args_kwargs_yells_at_you, test/test_fx.py::TestFX::test_update_kwargs_api, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_function, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_module, test/test_fx.py::TestFX::test_varargs_concrete, test/test_fx.py::TestFX::test_wrap, test/test_fx.py::TestFX::test_wrap_decorated_function, test/test_fx.py::TestFX::test_wrap_fn_directly, test/test_fx.py::TestFX::test_wrap_with_submodule, test/test_fx.py::TestFX::test_wrapped_method, test/test_fx.py::TestFX::test_wrapped_retrace, test/test_fx.py::TestFX::test_wrapped_via_decorator, test/test_fx.py::TestFX::test_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_wrong_target_type, test/test_fx.py::TestFX::test_wrong_topo, test/test_fx.py::TestFXAPIBackwardCompatibility::test_adding_side_effect_function, test/test_fx.py::TestFXAPIBackwardCompatibility::test_class_member_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_function_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_preserve_unused_attr_after_unpickle, test/test_fx.py::TestFXAPIBackwardCompatibility::test_public_api_surface, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_affine_grid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_batch_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy_with_logits, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_tbc, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_similarity, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_ctc_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding_bag, test/test_fx.py::TestFunctionalTracing::test_nn_functional_feature_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gaussian_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_glu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_grid_sample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_group_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gumbel_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardswish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hinge_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_huber_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_instance_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_interpolate, test/test_fx.py::TestFunctionalTracing::test_nn_functional_kl_div, test/test_fx.py::TestFunctionalTracing::test_nn_functional_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_layer_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_linear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_local_response_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_log_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_logsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_margin_ranking_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mse_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_head_attention_forward, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_native_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_normalize, test/test_fx.py::TestFunctionalTracing::test_nn_functional_one_hot, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pad, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pairwise_distance, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pdist, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_unshuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_poisson_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_prelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu6, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rms_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_scaled_dot_product_attention, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_silu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_smooth_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmin, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softplus, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_with_distance_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_unfold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_nearest, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_H_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_T_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___getitem___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___radd___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rdiv___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmatmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmod___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rpow___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rsub___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__batch_norm_with_update_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__chunk_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__native_batch_norm_legit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_lengths_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_offsets_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__softmax_backward_data_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_abs_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcdiv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcmul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_decomposed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_alias_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_all_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_allclose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_aminmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_angle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_any_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_arange_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argsort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argwhere_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_partial_views_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asinh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_baddbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bernoulli_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bfloat16_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_block_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bool_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_shapes_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_to_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bucketize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_byte_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cartesian_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cauchy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdouble_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ceil_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cfloat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chalf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_char_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_inverse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chunk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_max_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_min_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clone_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_column_stack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_combinations_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_complex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_physical_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_constant_pad_nd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_contiguous_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_copysign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_corrcoef_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_count_nonzero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cov_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cross_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumprod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumulative_trapezoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_deg2rad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_embed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagflat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diff_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_digamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_floor_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_no_rounding_mode_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_trunc_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_double_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_einsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_permuted_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eq_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_equal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expm1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exponential_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eye_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flatten_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flip_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fliplr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flipud_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_power_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_divide_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frac_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gather_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ge_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geometric_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geqrf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gradient_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_grid_sampler_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_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, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_lraspp_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x2_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_1, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mc3_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v1_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r2plus1d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r3d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_s3d, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_h_14, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet101_2, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet50_2 2025-07-17T07:19:31.2161347Z 2025-07-17T07:19:31.2161571Z Running test_show_pickle 1/1 ... [2025-07-17 07:19:31.121498] 2025-07-17T07:19:31.2162146Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:19:31.2163212Z 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'] ... [2025-07-17 07:19:31.121861] 2025-07-17T07:19:34.0899532Z 2025-07-17T07:19:34.0900448Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_b024053443259de2_.log 2025-07-17T07:19:34.0901407Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2025-07-17T07:19:34.0901861Z 2025-07-17T07:19:34.0903670Z Running inductor/test_benchmark_fusion 1/1 ... [2025-07-17 07:19:34.090229] 2025-07-17T07:19:34.0904151Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:19:34.0907586Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_benchmark_fusion.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:19:34.090576] 2025-07-17T07:19:46.8211035Z 2025-07-17T07:19:46.8212326Z inductor/test_benchmark_fusion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_benchmark_fusion_1.1_622702619e4bd683_.log 2025-07-17T07:19:46.8216390Z Running 6 items in this shard: test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_avoid_register_spilling_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_foreach_kernel_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_register_spills_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_resnet18_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_softmax_cpu, test/inductor/test_benchmark_fusion.py::BenchmarkFusionCpuTest::test_tield_kernel_fusion_cpu 2025-07-17T07:19:46.8219114Z 2025-07-17T07:19:46.8219385Z Running inductor/test_max_autotune 1/1 ... [2025-07-17 07:19:46.821361] 2025-07-17T07:19:46.8219836Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:19:46.8220963Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_max_autotune.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:19:46.821707] 2025-07-17T07:19:53.2731138Z 2025-07-17T07:19:53.2732081Z inductor/test_max_autotune 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_max_autotune_1.1_ac6f054b2f7e44f5_.log 2025-07-17T07:19:53.2732839Z 2025-07-17T07:19:53.2735041Z Running test_utils 1/1 ... [2025-07-17 07:19:53.273328] 2025-07-17T07:19:53.2735554Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:19:53.2738557Z 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'] ... [2025-07-17 07:19:53.273657] 2025-07-17T07:23:03.3233355Z 2025-07-17T07:23:03.3234193Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_1b0d56ef98aaedb6_.log 2025-07-17T07:23:03.5420349Z Running 6035 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, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asinh_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_block_diag_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bmm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_bool_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_shapes_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bool, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_byte_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cartesian_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e4m3fn, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e4m3fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_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, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_bool, 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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, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e4m3fn, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e4m3fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e5m2, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e5m2fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_more_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_nn_module_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_set_default_device_cpu, test/test_utils.py::TestCppExtensionUtils::test_cc_compiler_is_ok, test/test_utils.py::TestCppExtensionUtils::test_cpp_compiler_is_ok, test/test_utils.py::TestTraceback::test_basic, test/test_utils.py::TestTraceback::test_captured_traceback, test/test_utils.py::TestTraceback::test_captured_traceback_format_all, test/test_utils.py::TestTraceback::test_captured_traceback_format_all_cached, test/test_utils.py::TestTraceback::test_format_traceback_short, test/test_utils.py::TestTryImport::test_import_existing, test/test_utils.py::TestTryImport::test_import_imported, test/test_utils.py::TestTryImport::test_import_missing, test/test_utils.py::TestDeprecate::test_deprecated 2025-07-17T07:23:03.8002630Z 2025-07-17T07:23:03.8002876Z Running test_multiprocessing 1/1 ... [2025-07-17 07:23:03.329885] 2025-07-17T07:23:03.8003329Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:23:03.8004411Z 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'] ... [2025-07-17 07:23:03.330205] 2025-07-17T07:23:59.7670860Z 2025-07-17T07:23:59.7671755Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_b34f86ec10569148_.log 2025-07-17T07:23:59.7686940Z Running 42 items in this shard: test/test_multiprocessing.py::TestMultiprocessing::test_autograd_errors, test/test_multiprocessing.py::TestMultiprocessing::test_autograd_fine_with_spawn, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_bad_call, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_ipc_deadlock, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_memory_allocation, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_send_many, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_simple, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_small_tensors, test/test_multiprocessing.py::TestMultiprocessing::test_cuda_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_shared, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_empty_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_event, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_exporter, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_importer, test/test_multiprocessing.py::TestMultiprocessing::test_event_handle_multi_gpu, test/test_multiprocessing.py::TestMultiprocessing::test_event_multiprocess, test/test_multiprocessing.py::TestMultiprocessing::test_fd_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fd_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fd_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs, test/test_multiprocessing.py::TestMultiprocessing::test_fs_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_fs_pool, test/test_multiprocessing.py::TestMultiprocessing::test_fs_preserve_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_fs_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_inherit_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cpu, test/test_multiprocessing.py::TestMultiprocessing::test_integer_parameter_serialization_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared, test/test_multiprocessing.py::TestMultiprocessing::test_is_shared_cuda, test/test_multiprocessing.py::TestMultiprocessing::test_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_meta_simple, test/test_multiprocessing.py::TestMultiprocessing::test_mixed_types_cuda_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_non_leaf_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_parameter_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_rebuild_cuda_tensor, test/test_multiprocessing.py::TestMultiprocessing::test_set_thread_name, test/test_multiprocessing.py::TestMultiprocessing::test_tensor_sharing_meta, test/test_multiprocessing.py::TestMultiprocessing::test_variable_sharing, test/test_multiprocessing.py::TestMultiprocessing::test_wrong_cuda_fork 2025-07-17T07:23:59.7701265Z 2025-07-17T07:23:59.7701455Z Running test_tensorexpr 1/1 ... [2025-07-17 07:23:59.767312] 2025-07-17T07:23:59.7701869Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:23:59.7703020Z 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'] ... [2025-07-17 07:23:59.767685] 2025-07-17T07:24:46.6397535Z 2025-07-17T07:24:46.6398416Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_241f5ea09ac2de56_.log 2025-07-17T07:24:46.6419332Z 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 2025-07-17T07:24:46.6439972Z 2025-07-17T07:24:46.6440162Z Running test_jit_disabled 1/1 ... [2025-07-17 07:24:46.640043] 2025-07-17T07:24:46.6440579Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:24:46.6441641Z 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'] ... [2025-07-17 07:24:46.640349] 2025-07-17T07:24:49.7084831Z 2025-07-17T07:24:49.7085857Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_2ff70e4a78d957f2_.log 2025-07-17T07:24:49.7087466Z 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 2025-07-17T07:24:49.7088628Z 2025-07-17T07:24:49.7088898Z Running test_cpp_extensions_mtia_backend 1/1 ... [2025-07-17 07:24:49.708725] 2025-07-17T07:24:49.7089365Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:24:49.7092362Z 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'] ... [2025-07-17 07:24:49.709024] 2025-07-17T07:24:53.1777886Z 2025-07-17T07:24:53.1778977Z 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_69522425fd799891_.log 2025-07-17T07:24:53.1782779Z 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 2025-07-17T07:24:53.1785106Z 2025-07-17T07:24:53.1785378Z Running test_cpp_extensions_stream_and_event 1/1 ... [2025-07-17 07:24:53.178012] 2025-07-17T07:24:53.1785874Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:24:53.1787018Z 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'] ... [2025-07-17 07:24:53.178310] 2025-07-17T07:24:56.6470364Z 2025-07-17T07:24:56.6471402Z 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_8181e226e0bd2a56_.log 2025-07-17T07:24:56.6473060Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event 2025-07-17T07:24:56.6473720Z 2025-07-17T07:24:56.6474422Z Running test_dispatch 1/1 ... [2025-07-17 07:24:56.647294] 2025-07-17T07:24:56.6474827Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:24:56.6478177Z 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'] ... [2025-07-17 07:24:56.647613] 2025-07-17T07:25:22.8478597Z 2025-07-17T07:25:22.8479474Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_f7d1b3702ebe7afa_.log 2025-07-17T07:25:22.8490258Z 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 2025-07-17T07:25:22.8500074Z 2025-07-17T07:25:22.8500284Z Running test_fake_tensor 1/1 ... [2025-07-17 07:25:22.848123] 2025-07-17T07:25:22.8500680Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:25:22.8501721Z 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'] ... [2025-07-17 07:25:22.848427] 2025-07-17T07:25:34.3777723Z 2025-07-17T07:25:34.3778906Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_949764f2ccb7cc6e_.log 2025-07-17T07:25:34.3895187Z Running 284 items in this shard: test/test_fake_tensor.py::FakeTensorTest::test__adaptive_avg_pool2d_backward, test/test_fake_tensor.py::FakeTensorTest::test_alias_call, test/test_fake_tensor.py::FakeTensorTest::test_allow_meta, test/test_fake_tensor.py::FakeTensorTest::test_aten_copy_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_index_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_slice_scatter_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_basic, test/test_fake_tensor.py::FakeTensorTest::test_batch_tensor, test/test_fake_tensor.py::FakeTensorTest::test_binary_op_type_promotion, test/test_fake_tensor.py::FakeTensorTest::test_constructor, test/test_fake_tensor.py::FakeTensorTest::test_convert_fake_to_real, test/test_fake_tensor.py::FakeTensorTest::test_cpu_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cuda_initialized, test/test_fake_tensor.py::FakeTensorTest::test_cuda_lstm, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_with_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_without_fallback, test/test_fake_tensor.py::FakeTensorTest::test_custom_op_fallback, test/test_fake_tensor.py::FakeTensorTest::test_data_dependent_operator, test/test_fake_tensor.py::FakeTensorTest::test_deepcopy, test/test_fake_tensor.py::FakeTensorTest::test_device_inplace_copy, test/test_fake_tensor.py::FakeTensorTest::test_embedding_bag_meta, test/test_fake_tensor.py::FakeTensorTest::test_export_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fake_device, test/test_fake_tensor.py::FakeTensorTest::test_fake_dispatch_keys, test/test_fake_tensor.py::FakeTensorTest::test_fake_grad_copy, test/test_fake_tensor.py::FakeTensorTest::test_fake_mode_error, test/test_fake_tensor.py::FakeTensorTest::test_fast_div, test/test_fake_tensor.py::FakeTensorTest::test_from_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fsdp_flat_param, test/test_fake_tensor.py::FakeTensorTest::test_full, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex128, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int16, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int8, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_uint8, test/test_fake_tensor.py::FakeTensorTest::test_index_put_error, test/test_fake_tensor.py::FakeTensorTest::test_jagged_fake_to_fake_preserved, test/test_fake_tensor.py::FakeTensorTest::test_like_constructor, test/test_fake_tensor.py::FakeTensorTest::test_mixed_real_and_fake_inputs, test/test_fake_tensor.py::FakeTensorTest::test_mode, test/test_fake_tensor.py::FakeTensorTest::test_nan_to_num, test/test_fake_tensor.py::FakeTensorTest::test_nanmean_out, test/test_fake_tensor.py::FakeTensorTest::test_new, test/test_fake_tensor.py::FakeTensorTest::test_no_tag_func, test/test_fake_tensor.py::FakeTensorTest::test_non_kwarg_device, test/test_fake_tensor.py::FakeTensorTest::test_non_overlapping_stride_zero, test/test_fake_tensor.py::FakeTensorTest::test_non_parameter_grad, test/test_fake_tensor.py::FakeTensorTest::test_normalize_device, test/test_fake_tensor.py::FakeTensorTest::test_out_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_parameter_instantiation, test/test_fake_tensor.py::FakeTensorTest::test_parameter_view, test/test_fake_tensor.py::FakeTensorTest::test_print_in_fake_mode, test/test_fake_tensor.py::FakeTensorTest::test_randperm, test/test_fake_tensor.py::FakeTensorTest::test_recursive_invocation, test/test_fake_tensor.py::FakeTensorTest::test_repr, test/test_fake_tensor.py::FakeTensorTest::test_same_shape_env_preserved, test/test_fake_tensor.py::FakeTensorTest::test_scalar_inputs, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_False, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_True, test/test_fake_tensor.py::FakeTensorTest::test_setitem, test/test_fake_tensor.py::FakeTensorTest::test_shape_take_not_device, test/test_fake_tensor.py::FakeTensorTest::test_split_return_self, test/test_fake_tensor.py::FakeTensorTest::test_throw, test/test_fake_tensor.py::FakeTensorTest::test_tolist, test/test_fake_tensor.py::FakeTensorTest::test_type_as, test/test_fake_tensor.py::FakeTensorTest::test_unbind_copy_out, test/test_fake_tensor.py::FakeTensorTest::test_unsqueeze_copy, test/test_fake_tensor.py::FakeTensorTest::test_upsample_bilinear_small_channels, test/test_fake_tensor.py::FakeTensorTest::test_zero_dim, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test__adaptive_avg_pool2d_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_alias_call_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_allow_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_copy_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_index_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_slice_scatter_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_basic_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_batch_tensor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_binary_op_type_promotion_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_convert_fake_to_real_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cpu_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_initialized_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_lstm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_with_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_without_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_custom_op_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_data_dependent_operator_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_deepcopy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_device_inplace_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_embedding_bag_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_export_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_dispatch_keys_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_grad_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fast_div_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_from_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fsdp_flat_param_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_full_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex128_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int16_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_uint8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_put_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_jagged_fake_to_fake_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_like_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mixed_real_and_fake_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_nan_to_num_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_nanmean_out_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_no_tag_func_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_overlapping_stride_zero_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_parameter_grad_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_normalize_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_out_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_instantiation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_print_in_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_randperm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_recursive_invocation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_repr_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_same_shape_env_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scalar_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_False_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_True_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_setitem_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_shape_take_not_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_split_return_self_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_throw_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_tolist_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_type_as_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_unbind_copy_out_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_unsqueeze_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_upsample_bilinear_small_channels_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_zero_dim_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorConstHandling::test_aliased_const_write, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_propagate_through_functions, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_batch_norm_cpu, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_in_intlist_repro, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_add, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_view_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storage_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storages, test/test_fake_tensor.py::FakeTensorConstHandling::test_simple, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_aliased_const_write_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_propagate_through_functions_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_batch_norm_cpu_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_in_intlist_repro_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_add_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_view_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storage_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storages_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_simple_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_key, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_weak_ref, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_from_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_to_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_multiple_modes, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_active_mode, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_ref_cycle, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_mode_error, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_non_view, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_view, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_key_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_weak_ref_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_from_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_to_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_multiple_modes_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_active_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_ref_cycle_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_non_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_view_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_conv_c1_backward, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_cross_entropy_loss, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_embedding_bag_private, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_fake_gpu_no_init, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_flash_attention, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_like_ops, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_module_to, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_no_dispatch_with_like_function, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_non_kwarg_only_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_sparse_new, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_str_storage, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_new, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_conv_c1_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_cross_entropy_loss_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_embedding_bag_private_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_fake_gpu_no_init_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_flash_attention_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_like_ops_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_module_to_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_no_dispatch_with_like_function_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_non_kwarg_only_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_sparse_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_str_storage_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_new_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args, test/test_fake_tensor.py::FakeTensorPropTest::test_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_aten_index, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_bypass, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_dispatch_key_set, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_hit, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_inplace_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_constants, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_conj, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_inference, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_neg, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_memory_format, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_requires_grad, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_shape, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_storage_offset, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_stride, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_tuple_outputs, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_view_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_fft_hfft2_issue145522, test/test_fake_tensor.py::FakeTensorDispatchCache::test_from_buffer, test/test_fake_tensor.py::FakeTensorDispatchCache::test_inference_mode, test/test_fake_tensor.py::FakeTensorDispatchCache::test_invoke_subgraph, test/test_fake_tensor.py::FakeTensorDispatchCache::test_invoke_subgraph_cacheable_inplace, test/test_fake_tensor.py::FakeTensorDispatchCache::test_meta_tensor_to_fake_cpu, test/test_fake_tensor.py::FakeTensorDispatchCache::test_shape_env_settings, test/test_fake_tensor.py::FakeTensorDispatchCache::test_unbacked_output, test/test_fake_tensor.py::FakeTensorDispatchCache::test_wrapper_tensor_subclass_different_device 2025-07-17T07:25:34.4010177Z 2025-07-17T07:25:34.4010392Z Running test_python_dispatch 1/1 ... [2025-07-17 07:25:34.378432] 2025-07-17T07:25:34.4010828Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:25:34.4011907Z 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'] ... [2025-07-17 07:25:34.378748] 2025-07-17T07:25:39.3492965Z 2025-07-17T07:25:39.3494089Z test_python_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_python_dispatch_1.1_633ca0a5ee9a52ab_.log 2025-07-17T07:25:39.3540082Z Running 118 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_dispatchkeyset_eq, test/test_python_dispatch.py::TestPythonRegistration::test_dispatchkeyset_pickle, test/test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind, test/test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable, test/test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg, test/test_python_dispatch.py::TestPythonRegistration::test_fallback, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_keyset, test/test_python_dispatch.py::TestPythonRegistration::test_fallthrough_for_dense_key_with_meta_in_tls, test/test_python_dispatch.py::TestPythonRegistration::test_finalizer, test/test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries, test/test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum, test/test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator, test/test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint, test/test_python_dispatch.py::TestPythonDispatch::test_all_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr, test/test_python_dispatch.py::TestPythonDispatch::test_basic, test/test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor, test/test_python_dispatch.py::TestPythonDispatch::test_custom_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_custom_dispatch_mode_not_supports_higher_order_operators, test/test_python_dispatch.py::TestPythonDispatch::test_custom_dispatch_mode_supports_higher_order_operators, test/test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes, test/test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type, test/test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once, test/test_python_dispatch.py::TestPythonDispatch::test_device_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_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 2025-07-17T07:25:39.3586000Z 2025-07-17T07:25:39.3586215Z Running test_autograd_fallback 1/1 ... [2025-07-17 07:25:39.349684] 2025-07-17T07:25:39.3586655Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:25:39.3587727Z 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'] ... [2025-07-17 07:25:39.349998] 2025-07-17T07:25:42.4181579Z 2025-07-17T07:25:42.4182756Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_77bf6065b8189b55_.log 2025-07-17T07:25:42.4195605Z 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 2025-07-17T07:25:42.4207842Z 2025-07-17T07:25:42.4208077Z Running test_cuda_nvml_based_avail 1/1 ... [2025-07-17 07:25:42.418417] 2025-07-17T07:25:42.4208573Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:25:42.4209724Z 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'] ... [2025-07-17 07:25:42.418721] 2025-07-17T07:25:45.3002405Z 2025-07-17T07:25:45.3003329Z 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_e76694aa5ec1dbc8_.log 2025-07-17T07:25:45.3004183Z Running 0 items in this shard: 2025-07-17T07:25:45.3004376Z 2025-07-17T07:25:45.3006133Z Running test_tensor_creation_ops 1/1 ... [2025-07-17 07:25:45.300478] 2025-07-17T07:25:45.3006788Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:25:45.3009912Z 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'] ... [2025-07-17 07:25:45.300793] 2025-07-17T07:26:04.6399708Z 2025-07-17T07:26:04.6400641Z 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_3245cf283219dfe2_.log 2025-07-17T07:26:04.6645998Z Running 641 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_concat_empty_list_error_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_device_legacy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_constructor_dtypes_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ctor_with_numpy_array_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_device_rounding_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diag_embed_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_diagflat_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_dstack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_full_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_overflow_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_empty_tensor_props_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_eye_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_fill_all_dtypes_and_devices_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_finite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_float_to_int_conversion_nonfinite_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_False_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_from_file_shared_True_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_inference_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_full_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_hstack_column_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_kaiser_window_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_large_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_like_fn_stride_proparation_vs_tensoriterator_unary_op_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linlogspace_mem_overlap_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_linspace_vs_numpy_integral_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_base2_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_deduction_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_device_vs_cpu_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_special_steps_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_complex_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_logspace_vs_numpy_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_default_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_ij_indexing_is_default_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_inconsistent_dtype_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_non_1d_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_unsupported_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_vs_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_warns_if_no_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_meshgrid_xy_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_empty_strided_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_methods_requires_grad_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_new_tensor_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_offset_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_ones_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_default_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_bool_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_from_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_full_range_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_random_to_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_factories_64bit_indexing_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_range_warning_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_bfloat16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_refs_tensor_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_repeat_interleave_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_roll_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_bartlett_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_blackman_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hamming_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_window_functions_window_hann_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_bartlett_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_blackman_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_cosine_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hamming_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_hann_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_signal_windows_functions_window_nuttall_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_simple_scalar_cast_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_stack_out_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_storage_filename_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_strided_mismatched_stride_shape_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_ctor_device_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_device_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factories_empty_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_copy_var_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_gpu_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_factory_type_inference_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_non_writable_numpy_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_tensor_from_sequence_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_floating_dtype_error_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_out_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_complex_same_dtype_error_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_torch_polar_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_unpack_double_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_cpu, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_bool, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vander_types_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vsplit_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex128, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_complex64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_float64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int16, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int32, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int64, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_int8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_vstack_row_stack_cpu_uint8, test/test_tensor_creation_ops.py::TestTensorCreationCPU::test_zeros_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 2025-07-17T07:26:04.6884728Z 2025-07-17T07:26:04.6884936Z Running test_cuda_trace 1/1 ... [2025-07-17 07:26:04.641178] 2025-07-17T07:26:04.6885335Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:26:04.6886455Z 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'] ... [2025-07-17 07:26:04.641496] 2025-07-17T07:26:07.5081635Z 2025-07-17T07:26:07.5082486Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_71c17d4a59ace246_.log 2025-07-17T07:26:07.5083229Z Running 0 items in this shard: 2025-07-17T07:26:07.5083449Z 2025-07-17T07:26:07.5084199Z Running test_autocast 1/1 ... [2025-07-17 07:26:07.508291] 2025-07-17T07:26:07.5084580Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:26:07.5087640Z 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'] ... [2025-07-17 07:26:07.508586] 2025-07-17T07:26:11.1776031Z 2025-07-17T07:26:11.1776866Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_0c8bbace21b5ad9d_.log 2025-07-17T07:26:11.1784204Z 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 2025-07-17T07:26:11.1791030Z 2025-07-17T07:26:11.1791263Z Running dynamo/test_fake_distributed 1/1 ... [2025-07-17 07:26:11.177822] 2025-07-17T07:26:11.1791785Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:26:11.1793062Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_fake_distributed.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:26:11.178116] 2025-07-17T07:26:19.8032494Z 2025-07-17T07:26:19.8035395Z dynamo/test_fake_distributed 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fake_distributed_1.1_f08f0bb7a2342e02_.log 2025-07-17T07:26:19.8037402Z Running 1 items in this shard: test/dynamo/test_fake_distributed.py::TestFakeDistributed::test_all_to_all_single_autograd 2025-07-17T07:26:19.8038306Z 2025-07-17T07:26:19.8038621Z Running test_cpp_extensions_jit 1/1 ... [2025-07-17 07:26:19.803586] 2025-07-17T07:26:19.8039316Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:26:19.8043398Z 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'] ... [2025-07-17 07:26:19.804028] 2025-07-17T07:26:53.9643999Z 2025-07-17T07:26:53.9644896Z 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_88652d46248e884a_.log 2025-07-17T07:26:53.9659338Z Running 34 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_aoti_torch_call_dispatcher, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_arch_flags_default_gencode, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_arch_flags_non_default_gencode, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_pluggable_allocator_include, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_xpu, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_xpu_archlists, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_xpu_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_load_with_non_platform_default_encoding, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning 2025-07-17T07:26:53.9673075Z 2025-07-17T07:26:53.9673279Z Running test_cuda_primary_ctx 1/1 ... [2025-07-17 07:26:53.964699] 2025-07-17T07:26:53.9673709Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:26:53.9674836Z 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'] ... [2025-07-17 07:26:53.965016] 2025-07-17T07:26:56.8271509Z 2025-07-17T07:26:56.8272564Z 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_9d0547b88f318f56_.log 2025-07-17T07:26:56.8273379Z Running 0 items in this shard: 2025-07-17T07:26:56.8273571Z 2025-07-17T07:26:56.8275590Z Running test_torch 1/1 ... [2025-07-17 07:26:56.827397] 2025-07-17T07:26:56.8275971Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:26:56.8278974Z 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'] ... [2025-07-17 07:26:56.827702] 2025-07-17T07:27:12.8626280Z 2025-07-17T07:27:12.8627077Z test_torch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.1_87e88d680a6752a4_.log 2025-07-17T07:27:12.8962503Z Running 1045 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_concat_non_tuple_sequence, test/test_torch.py::TestTorch::test_Size_concat_wildcard, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bf16_supported_on_cpu, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_conj_physical_meta_stride, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_newaxis_numpy_comparison, test/test_torch.py::TestTorch::test_newindex, test/test_torch.py::TestTorch::test_no_cuda_monkeypatch, test/test_torch.py::TestTorch::test_norm_neg_dim, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parallel_info, test/test_torch.py::TestTorch::test_parsing_double, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_function, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pickle_size, test/test_torch.py::TestTorch::test_pin_memory, test/test_torch.py::TestTorch::test_print, test/test_torch.py::TestTorch::test_prod_neg_dim, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_qengine, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_resizable, test/test_torch.py::TestTorch::test_reversed, test/test_torch.py::TestTorch::test_scatter_neg_dim, test/test_torch.py::TestTorch::test_select_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_setting_real_imag_to_a_number, test/test_torch.py::TestTorch::test_show_config, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slice, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_continuing, test/test_torch.py::TestTorch::test_sobolengine_continuing_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_first_point, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_sobolengine_reset_scrambled, test/test_torch.py::TestTorch::test_sort_neg_dim, test/test_torch.py::TestTorch::test_split_neg_dim, test/test_torch.py::TestTorch::test_split_with_sizes_copy_out, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_init, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_casts, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_cycle_via_slots, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_resurrected, test/test_torch.py::TestTorch::test_storage_dealloc_subclass_zombie, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, test/test_torch.py::TestTorch::test_storage_error_no_attribute, test/test_torch.py::TestTorch::test_storage_finalizer_dealloc, test/test_torch.py::TestTorch::test_storage_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_resurrected, test/test_torch.py::TestTorch::test_storage_from_tensor_dealloc_zombie, test/test_torch.py::TestTorch::test_storage_preserve_nonhermetic_in_hermetic_context, test/test_torch.py::TestTorch::test_storage_resurrected_weak_ref, test/test_torch.py::TestTorch::test_storage_slot_dealloc, test/test_torch.py::TestTorch::test_storage_weakref_dealloc, test/test_torch.py::TestTorch::test_structseq_repr, test/test_torch.py::TestTorch::test_subclass_preserved, test/test_torch.py::TestTorch::test_subclass_tensors, test/test_torch.py::TestTorch::test_sum_neg_dim, test/test_torch.py::TestTorch::test_swap_basic, test/test_torch.py::TestTorch::test_swap_fail_slots, test/test_torch.py::TestTorch::test_t_not_2d_error, test/test_torch.py::TestTorch::test_tensor_base_init, test/test_torch.py::TestTorch::test_tensor_base_new, test/test_torch.py::TestTorch::test_tensor_ctor_scalar, test/test_torch.py::TestTorch::test_tensor_cycle_via_dict, test/test_torch.py::TestTorch::test_tensor_cycle_via_slots, test/test_torch.py::TestTorch::test_tensor_dead_weak_ref, test/test_torch.py::TestTorch::test_tensor_dict_dealloc, test/test_torch.py::TestTorch::test_tensor_finalizer_dealloc, test/test_torch.py::TestTorch::test_tensor_fix_weakref_no_leak, test/test_torch.py::TestTorch::test_tensor_item_no_warning, test/test_torch.py::TestTorch::test_tensor_ressurecting_clear, test/test_torch.py::TestTorch::test_tensor_resurrected_weak_ref, test/test_torch.py::TestTorch::test_tensor_set, test/test_torch.py::TestTorch::test_tensor_set_errors, test/test_torch.py::TestTorch::test_tensor_slot_dealloc, test/test_torch.py::TestTorch::test_tensor_weakref_dealloc, test/test_torch.py::TestTorch::test_tensor_where_scalar, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bernoulli_self_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_bfloat16_neg_abs_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_bool_tensor_value_change_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_add_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcdiv_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_addcmul_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_atan2_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_dist_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_div_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_broadcast_fn_eq_cpu, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_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, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_errors_index_copy_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_expected_failure_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_no_zero_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_deterministic_path_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_gather_backward_one_dim_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_geometric_kstest_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scale_will_not_overflow_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_deprecated_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaler_pass_itself_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_accumulation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach0_fused0_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach2_fused_True_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_grad_scaling_autocast_foreach_True_fused1_AdamW_cpu_float32, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_all_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_extreme_cases_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_spacing_list_length_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_gradient_type_promotion_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_hook_remove_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_add_deterministic_cpu, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amax_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_index_reduce_reduce_amin_cpu_uint8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_invalid_shapes_grid_sampler_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_is_set_to_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_is_signed_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e4m3fn, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e4m3fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e5m2, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_float8_e5m2fnuz, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_item_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_large_cumprod_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_large_cumsum_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_bfloat16, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_shift_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_skip_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_non_blocking_False_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_non_blocking_True_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_use_count_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_strides_propagation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_sync_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_shape_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_ternary_op_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_untyped_storage_meta_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_warn_always_caught_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu 2025-07-17T07:27:12.9290209Z 2025-07-17T07:27:12.9290407Z Running test_native_mha 1/1 ... [2025-07-17 07:27:12.863963] 2025-07-17T07:27:12.9290818Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:27:12.9291862Z 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'] ... [2025-07-17 07:27:12.864266] 2025-07-17T07:27:16.2830832Z 2025-07-17T07:27:16.2831987Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_2767978c6de5478a_.log 2025-07-17T07:27:16.2854200Z 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 2025-07-17T07:27:16.2875782Z 2025-07-17T07:27:16.2876041Z Running test_sort_and_select 1/1 ... [2025-07-17 07:27:16.283321] 2025-07-17T07:27:16.2876470Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:27:16.2877594Z 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'] ... [2025-07-17 07:27:16.283614] 2025-07-17T07:27:21.2542246Z 2025-07-17T07:27:21.2543093Z 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_cbdbbb36a78b2759_.log 2025-07-17T07:27:21.2583185Z 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 2025-07-17T07:27:21.2621706Z 2025-07-17T07:27:21.2621938Z Running test_multiprocessing_spawn 1/1 ... [2025-07-17 07:27:21.254561] 2025-07-17T07:27:21.2622397Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:27:21.2623529Z 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'] ... [2025-07-17 07:27:21.254853] 2025-07-17T07:29:29.3395880Z 2025-07-17T07:29:29.3396804Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_f4314fe5b3d8c02d_.log 2025-07-17T07:29:29.3408645Z 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 2025-07-17T07:29:29.3419881Z 2025-07-17T07:29:29.7300520Z Uploading artifacts took 0.39 seconds 2025-07-17T07:29:29.7303257Z Running nn/test_pooling 1/1 ... [2025-07-17 07:29:29.730188] 2025-07-17T07:29:29.7303742Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:29:29.7306958Z 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'] ... [2025-07-17 07:29:29.730492] 2025-07-17T07:29:56.8859237Z 2025-07-17T07:29:56.8861614Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_a957649aeed2eef3_.log 2025-07-17T07:29:56.8903274Z Running 106 items in this shard: test/nn/test_pooling.py::TestAvgPool::test_avg_pool1d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool2d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool3d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d_with_divisor, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d_with_divisor, test/nn/test_pooling.py::TestPoolingNN::test_MaxUnpool2d_output_size, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_nhwc_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_backward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_forward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_non_contiguous, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_lower_precision, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_none, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_overflow, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool2d_nhwc_cpu, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool3d_input_check, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool1d_empty_kernel, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool3d, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool2d_empty_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool3d_backward_after_cat_dim1_device_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_errors_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case10_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case4_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case5_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case6_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case7_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case8_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case9_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool2d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool3d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pooling_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_max_pooling_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pool_odd_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_uint8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool3d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_indices_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_with_indices_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool3d_non_square_backward_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool_indices_no_batch_dim_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_large_size_int64_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_size_one_feature_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_invalid_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_large_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_bfloat16_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_large_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_zero_stride_cpu 2025-07-17T07:29:56.8944003Z 2025-07-17T07:29:56.8944210Z Running test_mobile_optimizer 1/1 ... [2025-07-17 07:29:56.886258] 2025-07-17T07:29:56.8944637Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:29:56.8945699Z 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'] ... [2025-07-17 07:29:56.886561] 2025-07-17T07:30:02.7692925Z 2025-07-17T07:30:02.7693807Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_3e2531501677bf3c_.log 2025-07-17T07:30:02.7697072Z 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 2025-07-17T07:30:02.7699979Z 2025-07-17T07:30:02.7700192Z Running nn/test_convolution 1/1 ... [2025-07-17 07:30:02.769545] 2025-07-17T07:30:02.7700613Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:30:02.7701690Z 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'] ... [2025-07-17 07:30:02.769874] 2025-07-17T07:30:24.4148932Z 2025-07-17T07:30:24.4149990Z nn/test_convolution 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.1_7b8934705567654b_.log 2025-07-17T07:30:24.4504866Z Running 599 items in this shard: test/nn/test_convolution.py::TestConvolutionNN::test_Conv1d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_1x1, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_OneDNN, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_backward_twice, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias_v2, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_with_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_without_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_missing_argument, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_wbias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_half_cublas_gemm, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size_downsample_upsample, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose3d_correct_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv1d_issue_120547, test/nn/test_convolution.py::TestConvolutionNN::test_conv2d_discontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_issue_120406, test/nn/test_convolution.py::TestConvolutionNN::test_conv_backcompat, test/nn/test_convolution.py::TestConvolutionNN::test_conv_cudnn_memory_layout_dominance, test/nn/test_convolution.py::TestConvolutionNN::test_conv_invalid_groups, test/nn/test_convolution.py::TestConvolutionNN::test_conv_modules_raise_error_on_incorrect_input_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv_padding_mode, test/nn/test_convolution.py::TestConvolutionNN::test_conv_shapecheck, test/nn/test_convolution.py::TestConvolutionNN::test_conv_tbc, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_non_contiguous, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_noncontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_not_mutate_stride, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grouped_conv_cudnn_nhwc_support, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv1d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_invalid_conv3d, test/nn/test_convolution.py::TestConvolutionNN::test_mismatch_shape_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_nnpack_conv, test/nn/test_convolution.py::TestConvolutionNN::test_permute_conv2d_issue_120211, test/nn/test_convolution.py::TestConvolutionNN::test_thnn_conv_strided_padded_dilated, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_backward_depthwise_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_depthwise_naive_groups_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_1_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_2_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_Conv2d_deterministic_cudnn_dilation_3_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, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_large_output_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose2d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_ConvTranspose3d_size_1_kernel_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_contig_wrong_stride_cudnn_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv1d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_no_grad_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_backward_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv2d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_64bit_indexing_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_large_batch_1_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_backward_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_same_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_backward_cpu_complex128, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_valid_padding_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_same_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_same_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_valid_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv3d_vs_scipy_mode_valid_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convTranspose_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise1d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise2d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cuda_depthwise3d_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_cudnn1d_has_bias_False_strided_True_contiguous_False_cpu, 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test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_contiguous_for_oneDNN_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_mismatch_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_ndhwc_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_ndhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_support_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_nhwc_support_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_groups_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_no_bias_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_stride_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_strided_with_3D_input_and_weight_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_ic1_channels_last_for_oneDNN_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_batch_1_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_nosplit_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_noncontig_weights_and_bias_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_noncontig_weights_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose2d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose3d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transposed_large_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv2d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv3d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_depthwise_conv_64bit_indexing_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_convTranspose_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_conv_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float64 2025-07-17T07:30:24.4875756Z 2025-07-17T07:30:24.4875982Z Running test_autoload_disable 1/1 ... [2025-07-17 07:30:24.416195] 2025-07-17T07:30:26.6442149Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:30:26.6443974Z import pkg_resources 2025-07-17T07:30:26.7124696Z running install 2025-07-17T07:30:26.7126978Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T07:30:26.7127778Z !! 2025-07-17T07:30:26.7127906Z 2025-07-17T07:30:26.7128031Z ******************************************************************************** 2025-07-17T07:30:26.7128428Z Please avoid running ``setup.py`` directly. 2025-07-17T07:30:26.7128841Z Instead, use pypa/build, pypa/installer or other 2025-07-17T07:30:26.7129221Z standards-based tools. 2025-07-17T07:30:26.7129410Z 2025-07-17T07:30:26.7129640Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T07:30:26.7130276Z or your builds will no longer be supported. 2025-07-17T07:30:26.7130520Z 2025-07-17T07:30:26.7130840Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T07:30:26.7131436Z ******************************************************************************** 2025-07-17T07:30:26.7131686Z 2025-07-17T07:30:26.7131765Z !! 2025-07-17T07:30:26.7131989Z self.initialize_options() 2025-07-17T07:30:26.7261061Z running build 2025-07-17T07:30:26.7261333Z running build_py 2025-07-17T07:30:26.7347128Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-07-17T07:30:26.7355942Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-07-17T07:30:26.7365821Z running build_ext 2025-07-17T07:30:26.7380798Z building 'torch_test_cpp_extension.cpp' extension 2025-07-17T07:30:26.7382427Z creating build/temp.linux-x86_64-cpython-313 2025-07-17T07:30:26.7387279Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -std=c++17 2025-07-17T07:30:27.8715458Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-07-17T07:30:27.8716487Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-07-17T07:30:27.8717378Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2025-07-17T07:30:27.8718611Z from extension.cpp:1: 2025-07-17T07:30:27.8720311Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-07-17T07:30:27.8721194Z extension.cpp:45:53: required from here 2025-07-17T07:30:27.8722703Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-07-17T07:30:27.8724009Z 1539 | class class_ : public detail::generic_type { 2025-07-17T07:30:27.8724365Z | ^~~~~~ 2025-07-17T07:30:27.8726056Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-07-17T07:30:27.8728073Z extension.cpp:45:53: required from here 2025-07-17T07:30:27.8730587Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_::class_<>(pybind11::handle, const char*)::’ declared with greater visibility than the type of its field ‘pybind11::class_::class_<>(pybind11::handle, const char*)::::’ [-Wattributes] 2025-07-17T07:30:27.8732574Z 1599 | with_internals([&](internals &internals) { 2025-07-17T07:30:27.8732961Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:27.8733491Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-07-17T07:30:27.8734064Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:27.8734598Z 1601 | : internals.registered_types_cpp; 2025-07-17T07:30:27.8735081Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:27.8735591Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-07-17T07:30:27.8736010Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:27.8736411Z 1603 | = instances[std::type_index(typeid(type))]; 2025-07-17T07:30:27.8736813Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:27.8737140Z 1604 | }); 2025-07-17T07:30:27.8737393Z | ~ 2025-07-17T07:30:27.8740763Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:28.4074255Z building 'torch_test_cpp_extension.maia' extension 2025-07-17T07:30:28.4078524Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -std=c++17 2025-07-17T07:30:29.6530823Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:30.1726846Z building 'torch_test_cpp_extension.rng' extension 2025-07-17T07:30:30.1730875Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -std=c++17 2025-07-17T07:30:31.4365619Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T07:30:31.4367303Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T07:30:31.4368625Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T07:30:31.4370189Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T07:30:31.4371252Z from rng_extension.cpp:6: 2025-07-17T07:30:31.4373228Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1458: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T07:30:31.4374614Z 1458 | #pragma unroll 2025-07-17T07:30:31.4375014Z | 2025-07-17T07:30:31.4375924Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-07-17T07:30:31.4377276Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1510, 2025-07-17T07:30:31.4378658Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T07:30:31.4379994Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T07:30:31.4381291Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T07:30:31.4382974Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T07:30:31.4384009Z from rng_extension.cpp:6: 2025-07-17T07:30:31.4385003Z /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] 2025-07-17T07:30:31.4385805Z 59 | #pragma unroll 2025-07-17T07:30:31.4386192Z | 2025-07-17T07:30:31.4387033Z /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] 2025-07-17T07:30:31.4387835Z 72 | #pragma unroll 2025-07-17T07:30:31.4388069Z | 2025-07-17T07:30:31.4388891Z /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] 2025-07-17T07:30:31.4389692Z 87 | #pragma unroll 2025-07-17T07:30:31.4389954Z | 2025-07-17T07:30:31.4390896Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1511, 2025-07-17T07:30:31.4391820Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T07:30:31.4392647Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T07:30:31.4393437Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T07:30:31.4394439Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T07:30:31.4395122Z from rng_extension.cpp:6: 2025-07-17T07:30:31.4396094Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T07:30:31.4396895Z 160 | #pragma unroll 2025-07-17T07:30:31.4397140Z | 2025-07-17T07:30:31.4397899Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.4398763Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.4399630Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.4400476Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.4401231Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.4402079Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.4402998Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.4403895Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.4404795Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.4405752Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.4406711Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.4407558Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4408116Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4409193Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.4410763Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.4412594Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.4414405Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.4416395Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.4418472Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.4424881Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.4435763Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.4441915Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.4442864Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.4443239Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.4449748Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.4456502Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.4457805Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.4458213Z | ^~~~~~~~ 2025-07-17T07:30:31.4458842Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.4459853Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.4460783Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.4461645Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.4462410Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.4463259Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.4464311Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.4465461Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.4466619Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.4467750Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.4468758Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.4469710Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.4470556Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4471118Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4472155Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.4472916Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.4473272Z | ^~~~ 2025-07-17T07:30:31.4473924Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.4474808Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.4475623Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.4476429Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.4477182Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.4478030Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.4478946Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.4479848Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.4480788Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.4481735Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.4482694Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.4483543Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4484098Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4485108Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.4486786Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.4488534Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.4490322Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.4492216Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.4494297Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.4500609Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.4511276Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.4517795Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.4518812Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.4519203Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.4525527Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.4532126Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.4533432Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.4533839Z | ^~~~~~~~ 2025-07-17T07:30:31.4534471Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.4535583Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.4536498Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.4537312Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.4538077Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.4538929Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.4540001Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.4541147Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.4542338Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.4543465Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.4544482Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.4545422Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.4546273Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4546863Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4547698Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.4548488Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.4548842Z | ^~~~ 2025-07-17T07:30:31.4549423Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.4550286Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.4551109Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.4551891Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.4552648Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.4553505Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.4554443Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.4555333Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.4556233Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.4557184Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.4558157Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.4559004Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4559585Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4560585Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.4562168Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.4563891Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.4565668Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.4567577Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.4569666Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.4575867Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.4585989Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.4591691Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.4592658Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.4593058Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.4599029Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.4605418Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.4606720Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.4607180Z | ^~~~~~~~ 2025-07-17T07:30:31.4607818Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.4608863Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.4609768Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.4610583Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.4611358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.4612204Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.4613276Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.4614426Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.4615674Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.4616811Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.4617830Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.4618774Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.4619624Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4620190Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4621062Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.4621807Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.4622157Z | ^~~~ 2025-07-17T07:30:31.4622734Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.4623580Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.4624407Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.4625209Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.4625959Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.4626822Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.4627786Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.4628681Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.4629578Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.4630522Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.4631496Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.4632364Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4632917Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4633942Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.4635531Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.4637262Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.4639050Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.4640975Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.4643026Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.4648900Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.4658953Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.4664781Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.4665812Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.4666204Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.4672379Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.4678730Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.4680034Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.4680448Z | ^~~~~~~~ 2025-07-17T07:30:31.4681142Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.4682169Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.4683073Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.4683892Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.4684669Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.4685521Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.4686595Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.4687737Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.4688929Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.4690053Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.4691064Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.4692007Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.4692858Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4693456Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4694329Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.4695072Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.4695473Z | ^~~~ 2025-07-17T07:30:31.4696051Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.4696918Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.4697726Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.4698526Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.4699289Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.4700125Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.4701057Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.4701957Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.4702855Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.4703799Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.4704760Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.4705645Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4706201Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4707209Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.4708775Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.4710509Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.4712288Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.4714204Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.4716321Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.4722228Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.4732257Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.4738105Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.4739049Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.4739434Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.4745480Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.4751881Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.4753201Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.4753622Z | ^~~~~~~~ 2025-07-17T07:30:31.4754255Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.4755295Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.4756200Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.4757024Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.4757793Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.4758626Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.4759700Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.4760854Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.4762008Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.4763146Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.4764150Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.4765102Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.4765952Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4766543Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4767385Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.4768135Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.4768470Z | ^~~~ 2025-07-17T07:30:31.4769062Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.4769912Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.4770736Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.4771541Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.4772468Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.4773324Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.4774315Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.4775275Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.4776183Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.4777130Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.4778102Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.4779004Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4779580Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4780595Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.4782176Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.4783910Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.4795916Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.4797894Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.4799975Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.4806362Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.4817114Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.4823290Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.4824242Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.4824629Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.4830917Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.4837571Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.4838888Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.4839298Z | ^~~~~~~~ 2025-07-17T07:30:31.4839926Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.4840932Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.4841828Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.4842635Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.4843408Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.4844256Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.4845358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.4846501Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.4847660Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.4848783Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.4849804Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.4850777Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.4851650Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4852215Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4853051Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.4853782Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.4854133Z | ^~~~ 2025-07-17T07:30:31.4854705Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.4855630Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.4856459Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.4857268Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.4858026Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.4858852Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.4859769Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.4860656Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.4861542Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.4862494Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.4863452Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.4864284Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4864827Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4865800Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.4867358Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.4869081Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.4870891Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.4872983Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.4875024Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.4881327Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.4891986Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.4898048Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.4898973Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.4899346Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.4905633Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.4912365Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.4913647Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.4914047Z | ^~~~~~~~ 2025-07-17T07:30:31.4914649Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.4915632Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.4916513Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.4917336Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.4918096Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.4918921Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.4919974Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.4921107Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.4922244Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.4923377Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.4924385Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.4925322Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.4926158Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4926684Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4927498Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.4928220Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.4928542Z | ^~~~ 2025-07-17T07:30:31.4929105Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.4929957Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.4930806Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.4931589Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.4932334Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.4933178Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.4934105Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.4935017Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.4936017Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.4936969Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.4937930Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.4938770Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4939318Z from rng_extension.cpp:1: 2025-07-17T07:30:31.4940303Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.4941885Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.4943619Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.4945386Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.4947285Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.4949362Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.4955286Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.4965339Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.4971103Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.4972231Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.4972606Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.4978688Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.4985026Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.4986309Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.4986705Z | ^~~~~~~~ 2025-07-17T07:30:31.4987324Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.4988319Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.4989211Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.4990021Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.4990845Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.4991686Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.4992744Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.4993864Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.4995010Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.4996171Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.4997215Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.4998160Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.4998994Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.4999540Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5000362Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5001089Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5001431Z | ^~~~ 2025-07-17T07:30:31.5002004Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5002866Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5003670Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5004464Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5005204Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5006044Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5006955Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5007885Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5008780Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5009719Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5010671Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5011512Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5012061Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5013040Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5014603Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5016461Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5018236Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5020132Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5022233Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5028171Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5038226Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5043970Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5044894Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5045276Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5051190Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5057658Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5058949Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5059349Z | ^~~~~~~~ 2025-07-17T07:30:31.5059965Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5060955Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5061848Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5062662Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5063423Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5064254Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5065302Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5066428Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5067569Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5068725Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5069733Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5070664Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5071500Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5072217Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5073051Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5073769Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5074110Z | ^~~~ 2025-07-17T07:30:31.5074679Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5075594Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5076390Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5077178Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5077923Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5078765Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5079680Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5080621Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5081557Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5082511Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5083469Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5084321Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5084873Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5085866Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5087442Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5089177Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5090955Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5092852Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5094953Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5100954Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5111084Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5116816Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5117757Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5118130Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5124109Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5130378Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5131667Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5132085Z | ^~~~~~~~ 2025-07-17T07:30:31.5132708Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5133715Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5134600Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5135538Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5136308Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5137156Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5138212Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5139367Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5141377Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5142538Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5143537Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5144485Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5145338Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5145895Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5146730Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5147479Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5147828Z | ^~~~ 2025-07-17T07:30:31.5148391Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5149263Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5150080Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5150878Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5151613Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5152455Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5153387Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5154325Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5155226Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5156173Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5157144Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5158003Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5158548Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5159545Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5161126Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5162899Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5164683Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5166573Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5168688Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5174788Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5184968Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5190728Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5191660Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5192051Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5198093Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5204494Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5205797Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5206221Z | ^~~~~~~~ 2025-07-17T07:30:31.5206850Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5207851Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5208761Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5209599Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5210374Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5211205Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5212273Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5213430Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5214621Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5215822Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5216848Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5217803Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5218655Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5219200Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5220036Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5220779Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5221111Z | ^~~~ 2025-07-17T07:30:31.5221691Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5222587Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5223407Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5224191Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5224939Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5225787Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5226750Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5227672Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5228578Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5229525Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5230503Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5231340Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5231896Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5232882Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5234461Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5236197Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5237962Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5239866Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5241990Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5247911Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5257978Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5263692Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5264623Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5265008Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5271042Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5277551Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5278861Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5279267Z | ^~~~~~~~ 2025-07-17T07:30:31.5279897Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5280911Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5281807Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5282694Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5283465Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5284313Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5285377Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5286509Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5287706Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5288874Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5289886Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5290838Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5291673Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5292231Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5293061Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5293795Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5294141Z | ^~~~ 2025-07-17T07:30:31.5294717Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5295650Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5296457Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5297257Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5298007Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5298850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5299821Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5300720Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5301621Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5302566Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5303522Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5304366Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5304918Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5305916Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5307513Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5309243Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5311024Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5312922Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5315056Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5321274Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5331941Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5338021Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5338966Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5339399Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5345787Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5352379Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5353679Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5354087Z | ^~~~~~~~ 2025-07-17T07:30:31.5354724Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5355736Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5356629Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5357443Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5358215Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5359061Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5360132Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5361289Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5362441Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5363555Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5364565Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5365497Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5366341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5366891Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5367725Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5368500Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5368844Z | ^~~~ 2025-07-17T07:30:31.5369423Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5370283Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5371092Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5372035Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5372850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5373723Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5374659Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5375619Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5376514Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5377459Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5378415Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5379267Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5379822Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5380816Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5382391Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5384119Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5385901Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5387856Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5389925Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5396279Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5407148Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5413291Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5414220Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5414604Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5421214Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5427922Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5429254Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5429657Z | ^~~~~~~~ 2025-07-17T07:30:31.5430276Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5431283Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5432181Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5432986Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5433781Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5434666Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5435728Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5436859Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5438014Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5439142Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5440160Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5441097Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5441949Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5442506Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5443332Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5444065Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5444408Z | ^~~~ 2025-07-17T07:30:31.5444986Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5445839Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5446704Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5447514Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5448266Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5449096Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5450025Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5450923Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5451817Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5452752Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5453752Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5454604Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5455160Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5456215Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5457791Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5459583Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5461409Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5463320Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5465376Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5471729Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5482726Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5488894Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5489837Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5490223Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5496760Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5503393Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5504691Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5505096Z | ^~~~~~~~ 2025-07-17T07:30:31.5505727Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5506737Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5507679Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5508498Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5509256Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5510096Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5511162Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5512295Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5513445Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5514600Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5515612Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5516552Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5517402Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5517957Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5518783Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5519548Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5519894Z | ^~~~ 2025-07-17T07:30:31.5520505Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5521364Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5522188Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5522989Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5523742Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5524573Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5525505Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5526404Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5527302Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5528226Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5529193Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5530044Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5530595Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5531569Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5533189Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5534926Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5536817Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5538723Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5540787Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5547193Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5558049Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5564165Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5565140Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5565529Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5572143Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5578979Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5580333Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5580738Z | ^~~~~~~~ 2025-07-17T07:30:31.5581400Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5582416Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5583310Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5584117Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5584882Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5585716Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5586788Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5587926Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5589079Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5590202Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5591227Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5592169Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5593017Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5593608Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5594449Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5595182Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5595530Z | ^~~~ 2025-07-17T07:30:31.5596101Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5596953Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5597772Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5598568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5599327Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5600190Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5601120Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5602019Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5602916Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5603851Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5604831Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5605711Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5606299Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5607273Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5609089Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5611862Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5614601Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5618282Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5622156Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5631226Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5642155Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5648342Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5649284Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5649671Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5656165Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5662909Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5664219Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5664637Z | ^~~~~~~~ 2025-07-17T07:30:31.5665248Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5666253Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5667152Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5667976Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5668741Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5669619Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5670685Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5672013Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5673164Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5674287Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5675370Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5676368Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5677213Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5677777Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5678615Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5679366Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5679698Z | ^~~~ 2025-07-17T07:30:31.5680272Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5681139Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5681947Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5682747Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5683498Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5684332Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5685250Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5686149Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5687045Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5688049Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5689010Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5689856Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5690409Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5691397Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5692967Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5694714Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5696634Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5698534Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5700592Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5706951Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5717755Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5723872Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5724807Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5725197Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5731606Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5738452Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5739752Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5740173Z | ^~~~~~~~ 2025-07-17T07:30:31.5740794Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5741808Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5742709Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5743537Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5744293Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5745142Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5746209Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5747359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5748545Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5749668Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5750692Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5751638Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5752469Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5753024Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5753850Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5754595Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5754936Z | ^~~~ 2025-07-17T07:30:31.5755543Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5756404Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5757210Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5758003Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5758752Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5759595Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5760545Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5761477Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5762373Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5763310Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5764270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5765120Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5765677Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5766672Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5768241Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5769978Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5771920Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5773870Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5776078Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5782465Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5793875Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5800002Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5800941Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5801329Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5807798Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5814493Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5815880Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5816308Z | ^~~~~~~~ 2025-07-17T07:30:31.5816987Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5818008Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5818901Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5819709Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5820480Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5821319Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5822389Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5823541Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5824693Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5825825Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5826841Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5827792Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5828662Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5829221Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5830052Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5830789Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5831130Z | ^~~~ 2025-07-17T07:30:31.5831703Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5832560Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5833369Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5834165Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5834916Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5835827Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5836745Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5837645Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5838541Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5839485Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5840482Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5841367Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5841933Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5842929Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5844490Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5846223Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5848021Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5849946Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5851987Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5858402Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5869202Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5875536Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5876478Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5876869Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5883254Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5890024Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5891334Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5891742Z | ^~~~~~~~ 2025-07-17T07:30:31.5892372Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5893385Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5894282Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5895103Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5895951Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5896849Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5897917Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5899057Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5900203Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5901340Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5902421Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5903416Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5904254Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5904816Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5905650Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5906383Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5906726Z | ^~~~ 2025-07-17T07:30:31.5907298Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5908167Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5908979Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5909774Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5910523Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5911369Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5912286Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5913186Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5914113Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5915070Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5916027Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5916873Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5917428Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5918412Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5919978Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5921717Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5923535Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.5925435Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.5927478Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.5933850Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.5944773Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.5950842Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.5951778Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.5952162Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.5958636Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.5965366Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.5966659Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.5967062Z | ^~~~~~~~ 2025-07-17T07:30:31.5967691Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.5968713Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.5969610Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.5970418Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.5971187Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.5972160Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.5973234Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.5974365Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.5975639Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.5976769Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.5977790Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.5978738Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.5979573Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5980129Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5980970Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.5981705Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.5982089Z | ^~~~ 2025-07-17T07:30:31.5982670Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.5983526Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.5984332Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.5985129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.5985876Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.5986726Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.5987689Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.5988630Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.5989534Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.5990483Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.5991440Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.5992289Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.5992847Z from rng_extension.cpp:1: 2025-07-17T07:30:31.5993992Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.5995627Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.5997663Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.5999547Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.6001524Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.6003749Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.6009724Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.6020091Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.6026127Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.6027185Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.6027675Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.6033823Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.6040339Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.6041770Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.6042304Z | ^~~~~~~~ 2025-07-17T07:30:31.6042982Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.6044111Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.6045165Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.6046079Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.6047010Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.6047919Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.6049081Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.6050393Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.6051668Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.6052851Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.6053996Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.6055036Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.6066516Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6067088Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6068003Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.6068752Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.6069102Z | ^~~~ 2025-07-17T07:30:31.6069669Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.6070511Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.6071323Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.6072290Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.6073026Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.6073857Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.6074926Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.6075822Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.6076708Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.6077630Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.6078586Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.6079425Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6079969Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6080983Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.6082617Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.6084340Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.6086108Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.6088055Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.6090151Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.6096532Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.6107377Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.6113485Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.6114447Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.6114812Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.6121249Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.6128014Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.6129322Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.6129739Z | ^~~~~~~~ 2025-07-17T07:30:31.6130375Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.6131389Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.6132273Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.6133102Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.6133872Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.6134756Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.6135893Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.6137042Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.6138201Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.6139342Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.6140355Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.6141308Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.6142168Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6142761Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6143588Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.6144337Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.6144689Z | ^~~~ 2025-07-17T07:30:31.6145268Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.6146116Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.6146940Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.6147776Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.6148556Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.6149390Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.6150314Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.6151216Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.6152114Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.6153051Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.6154028Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.6154882Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6155444Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6156427Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.6158008Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.6159750Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.6161574Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.6163493Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.6165538Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.6172111Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.6183148Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.6189257Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.6190179Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.6190563Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.6197025Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.6203683Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.6204983Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.6205403Z | ^~~~~~~~ 2025-07-17T07:30:31.6206030Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.6207023Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.6207948Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.6208777Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.6209585Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.6210417Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.6211482Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.6212622Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.6213771Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.6214889Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.6215978Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.6216935Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.6217788Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6218333Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6219167Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.6219912Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.6220254Z | ^~~~ 2025-07-17T07:30:31.6220818Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.6221717Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.6222547Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.6223340Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.6224073Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.6224920Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.6225859Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.6226761Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.6227651Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.6228633Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.6229607Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.6230459Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6231000Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6231988Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.6233607Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.6235382Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.6237181Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.6239058Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.6241116Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.6247531Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.6258403Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.6264512Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.6265464Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.6265849Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.6272421Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.6279100Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.6280389Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.6280810Z | ^~~~~~~~ 2025-07-17T07:30:31.6281438Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.6282468Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.6283369Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.6284191Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.6284959Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.6285788Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.6286850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.6287988Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.6289181Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.6290291Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.6291308Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.6292257Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.6293101Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6293681Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6294520Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.6295373Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.6295727Z | ^~~~ 2025-07-17T07:30:31.6296296Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.6297157Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.6297982Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.6298769Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.6299516Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.6300369Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.6301306Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.6302194Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.6303089Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.6304037Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.6305012Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.6305863Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6306434Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6307436Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.6309000Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.6310741Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.6312518Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.6314425Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.6316502Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.6322473Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.6332439Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.6338249Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.6339226Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.6339616Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.6345521Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.6351869Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.6353167Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.6353614Z | ^~~~~~~~ 2025-07-17T07:30:31.6354228Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.6355268Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.6356157Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.6356973Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.6357750Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.6358578Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.6359646Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.6360803Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.6361961Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.6363082Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.6364096Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.6365053Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.6365910Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6366460Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6367327Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.6368084Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.6368421Z | ^~~~ 2025-07-17T07:30:31.6369001Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:31.6369875Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:31.6370698Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:31.6371483Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:31.6372394Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:31.6373253Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:31.6374244Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:31.6375127Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:31.6376090Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:31.6377037Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:31.6378003Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:31.6378895Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6379446Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6380491Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:31.6382066Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:31.6383826Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:31.6385595Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:31.6387510Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:31.6389565Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:31.6395981Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:31.6406767Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:31.6412960Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:31.6413880Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:31.6414262Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:31.6420746Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:31.6427502Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:31.6428806Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:31.6429222Z | ^~~~~~~~ 2025-07-17T07:30:31.6429842Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:31.6430837Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:31.6431738Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:31.6432568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:31.6433336Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:31.6434170Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:31.6435279Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:31.6436421Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:31.6437570Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:31.6438690Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:31.6439706Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:31.6440684Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:31.6441557Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:31.6442102Z from rng_extension.cpp:1: 2025-07-17T07:30:31.6442924Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:31.6443658Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:31.6443989Z | ^~~~ 2025-07-17T07:30:31.6447344Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:31.9726017Z running install_lib 2025-07-17T07:30:31.9812318Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-07-17T07:30:31.9815974Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-07-17T07:30:31.9817532Z 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 2025-07-17T07:30:31.9819697Z 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 2025-07-17T07:30:31.9888024Z 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 2025-07-17T07:30:31.9955663Z 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 2025-07-17T07:30:32.0032526Z 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 2025-07-17T07:30:32.0034979Z running install_egg_info 2025-07-17T07:30:32.0227146Z running egg_info 2025-07-17T07:30:32.0303158Z creating torch_test_cpp_extension.egg-info 2025-07-17T07:30:32.0304252Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-07-17T07:30:32.0307634Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-07-17T07:30:32.0309368Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-07-17T07:30:32.0311565Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-07-17T07:30:32.0312721Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T07:30:32.0395277Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T07:30:32.0402461Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T07:30:32.0404069Z 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 2025-07-17T07:30:32.0409974Z running install_scripts 2025-07-17T07:30:34.8439883Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:30:34.8441833Z import pkg_resources 2025-07-17T07:30:34.8743897Z 2025-07-17T07:30:34.8744076Z Running tests... 2025-07-17T07:30:34.8744674Z ---------------------------------------------------------------------- 2025-07-17T07:30:34.8896048Z . 2025-07-17T07:30:34.8896531Z ---------------------------------------------------------------------- 2025-07-17T07:30:34.8897106Z Ran 1 test in 0.015s 2025-07-17T07:30:34.8897265Z 2025-07-17T07:30:34.8897354Z OK 2025-07-17T07:30:34.8897460Z 2025-07-17T07:30:34.8897564Z Generating XML reports... 2025-07-17T07:30:35.4382892Z Running test_autoload_enable 1/1 ... [2025-07-17 07:30:35.437973] 2025-07-17T07:30:37.6663382Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:30:37.6664960Z import pkg_resources 2025-07-17T07:30:37.7337001Z running install 2025-07-17T07:30:37.7339613Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T07:30:37.7340419Z !! 2025-07-17T07:30:37.7340529Z 2025-07-17T07:30:37.7340652Z ******************************************************************************** 2025-07-17T07:30:37.7341044Z Please avoid running ``setup.py`` directly. 2025-07-17T07:30:37.7341452Z Instead, use pypa/build, pypa/installer or other 2025-07-17T07:30:37.7341829Z standards-based tools. 2025-07-17T07:30:37.7342014Z 2025-07-17T07:30:37.7342240Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T07:30:37.7342732Z or your builds will no longer be supported. 2025-07-17T07:30:37.7342974Z 2025-07-17T07:30:37.7343454Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T07:30:37.7344002Z ******************************************************************************** 2025-07-17T07:30:37.7344245Z 2025-07-17T07:30:37.7344329Z !! 2025-07-17T07:30:37.7344549Z self.initialize_options() 2025-07-17T07:30:37.7472085Z running build 2025-07-17T07:30:37.7472487Z running build_py 2025-07-17T07:30:37.7558161Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-07-17T07:30:37.7560688Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-07-17T07:30:37.7563796Z running build_ext 2025-07-17T07:30:37.7577961Z building 'torch_test_cpp_extension.cpp' extension 2025-07-17T07:30:37.7579515Z creating build/temp.linux-x86_64-cpython-313 2025-07-17T07:30:37.7584519Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -std=c++17 2025-07-17T07:30:38.8155573Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-07-17T07:30:38.8156598Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-07-17T07:30:38.8157671Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2025-07-17T07:30:38.8158237Z from extension.cpp:1: 2025-07-17T07:30:38.8159583Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-07-17T07:30:38.8160445Z extension.cpp:45:53: required from here 2025-07-17T07:30:38.8161929Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-07-17T07:30:38.8163189Z 1539 | class class_ : public detail::generic_type { 2025-07-17T07:30:38.8163526Z | ^~~~~~ 2025-07-17T07:30:38.8165152Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-07-17T07:30:38.8166650Z extension.cpp:45:53: required from here 2025-07-17T07:30:38.8169346Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_::class_<>(pybind11::handle, const char*)::’ declared with greater visibility than the type of its field ‘pybind11::class_::class_<>(pybind11::handle, const char*)::::’ [-Wattributes] 2025-07-17T07:30:38.8171331Z 1599 | with_internals([&](internals &internals) { 2025-07-17T07:30:38.8171701Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:38.8172563Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-07-17T07:30:38.8173165Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:38.8173712Z 1601 | : internals.registered_types_cpp; 2025-07-17T07:30:38.8174128Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:38.8174555Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-07-17T07:30:38.8174963Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:38.8175426Z 1603 | = instances[std::type_index(typeid(type))]; 2025-07-17T07:30:38.8175822Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:38.8176150Z 1604 | }); 2025-07-17T07:30:38.8176413Z | ~ 2025-07-17T07:30:38.8179784Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:39.3521688Z building 'torch_test_cpp_extension.maia' extension 2025-07-17T07:30:39.3526736Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -std=c++17 2025-07-17T07:30:40.3942782Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:40.8960794Z building 'torch_test_cpp_extension.rng' extension 2025-07-17T07:30:40.8965039Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -std=c++17 2025-07-17T07:30:42.0586000Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T07:30:42.0587610Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T07:30:42.0588973Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T07:30:42.0590375Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T07:30:42.0591484Z from rng_extension.cpp:6: 2025-07-17T07:30:42.0593188Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1458: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T07:30:42.0594519Z 1458 | #pragma unroll 2025-07-17T07:30:42.0594917Z | 2025-07-17T07:30:42.0595740Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-07-17T07:30:42.0597369Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1510, 2025-07-17T07:30:42.0598692Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T07:30:42.0599966Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T07:30:42.0601154Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T07:30:42.0602653Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T07:30:42.0603675Z from rng_extension.cpp:6: 2025-07-17T07:30:42.0605425Z /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] 2025-07-17T07:30:42.0606447Z 59 | #pragma unroll 2025-07-17T07:30:42.0606820Z | 2025-07-17T07:30:42.0607784Z /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] 2025-07-17T07:30:42.0608632Z 72 | #pragma unroll 2025-07-17T07:30:42.0608949Z | 2025-07-17T07:30:42.0609812Z /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] 2025-07-17T07:30:42.0610604Z 87 | #pragma unroll 2025-07-17T07:30:42.0610836Z | 2025-07-17T07:30:42.0611378Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1511, 2025-07-17T07:30:42.0612297Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T07:30:42.0613120Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T07:30:42.0613930Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T07:30:42.0614847Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T07:30:42.0615619Z from rng_extension.cpp:6: 2025-07-17T07:30:42.0616568Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T07:30:42.0617387Z 160 | #pragma unroll 2025-07-17T07:30:42.0617634Z | 2025-07-17T07:30:42.0618196Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.0619158Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.0620055Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.0620871Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.0621615Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.0622463Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.0623397Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.0624298Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.0625183Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.0626134Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.0627140Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.0627990Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0628533Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0629597Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.0631191Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.0632977Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.0634804Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.0636717Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.0638783Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.0645255Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.0656126Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.0662310Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.0663246Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.0663616Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.0670138Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.0677136Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.0678436Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.0678858Z | ^~~~~~~~ 2025-07-17T07:30:42.0679487Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.0680509Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.0681473Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.0682315Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.0683090Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.0683942Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.0685000Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.0686155Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.0687314Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.0688490Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.0689497Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.0690455Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.0691307Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0691879Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0692703Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.0693496Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.0693843Z | ^~~~ 2025-07-17T07:30:42.0694450Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.0695393Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.0696224Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.0697020Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.0697761Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.0698617Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.0699556Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.0700460Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.0701345Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.0702292Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.0703260Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.0704101Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0704638Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0705646Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.0707261Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.0709006Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.0710791Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.0712704Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.0714765Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.0721101Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.0731762Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.0737864Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.0738843Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.0739222Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.0745549Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.0752176Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.0753471Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.0753922Z | ^~~~~~~~ 2025-07-17T07:30:42.0754553Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.0755582Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.0756480Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.0757301Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.0758066Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.0758902Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.0759970Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.0761128Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.0762289Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.0763406Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.0764425Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.0765376Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.0766227Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0766775Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0767648Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.0768398Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.0768743Z | ^~~~ 2025-07-17T07:30:42.0769309Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.0770166Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.0770986Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.0771956Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.0772714Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.0773568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.0774577Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.0775548Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.0776430Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.0777385Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.0778368Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.0779271Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0779825Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0780897Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.0782488Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.0784219Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.0785997Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.0787931Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.0790004Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.0795957Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.0805931Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.0811750Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.0812706Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.0813095Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.0819165Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.0825501Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.0826800Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.0827223Z | ^~~~~~~~ 2025-07-17T07:30:42.0827886Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.0828891Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.0829784Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.0830605Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.0831370Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.0832198Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.0833270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.0834427Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.0835613Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.0836735Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.0837756Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.0838710Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.0839569Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0840144Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0840997Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.0841746Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.0842082Z | ^~~~ 2025-07-17T07:30:42.0842653Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.0843516Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.0844342Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.0845129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.0845888Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.0846736Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.0847669Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.0848930Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.0850521Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.0851866Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.0852846Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.0853763Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0854513Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0856163Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.0857783Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.0859573Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.0861611Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.0863538Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.0865695Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.0871728Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.0881934Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.0887793Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.0888738Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.0889126Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.0895127Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.0901587Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.0902941Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.0903362Z | ^~~~~~~~ 2025-07-17T07:30:42.0904010Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.0905026Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.0905921Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.0906735Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.0907503Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.0908347Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.0909422Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.0910570Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.0911727Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.0912855Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.0913876Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.0914832Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.0915679Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0916270Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0917107Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.0917838Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.0918183Z | ^~~~ 2025-07-17T07:30:42.0918768Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.0919625Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.0920438Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.0921237Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.0921998Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.0922878Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.0923799Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.0924704Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.0925601Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.0926549Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.0927506Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.0928392Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0928986Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0929983Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.0931552Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.0933294Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.0935083Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.0937081Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.0939147Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.0945091Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.0955050Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.0960830Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.0961772Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.0962157Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.0968107Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.0974595Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.0975943Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.0976421Z | ^~~~~~~~ 2025-07-17T07:30:42.0977055Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.0978063Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.0978945Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.0979768Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.0980538Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.0981386Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.0982446Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.0983639Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.0984807Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.0985943Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.0986954Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.0987900Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.0988806Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.0989368Z from rng_extension.cpp:1: 2025-07-17T07:30:42.0990237Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.0990988Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.0991334Z | ^~~~ 2025-07-17T07:30:42.0991903Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.0992756Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.0993574Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.0994371Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.0995135Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.0995979Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.0996907Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.0997810Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.0998705Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.0999638Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1000607Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1001485Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1002047Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1003030Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1004602Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1006334Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1008119Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1010065Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1012125Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1018428Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1029069Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1035045Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1035980Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1036349Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1042689Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1049396Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1050684Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1051101Z | ^~~~~~~~ 2025-07-17T07:30:42.1051727Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1052726Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1053611Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1054432Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1055259Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1056117Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1057175Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1058331Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1059496Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1060628Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1061640Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1062629Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1063487Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1064040Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1064855Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1065597Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1065943Z | ^~~~ 2025-07-17T07:30:42.1066503Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1067358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1068202Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1069039Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1069777Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1070622Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1071552Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1072653Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1073548Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1074570Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1075587Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1076445Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1076995Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1078005Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1079585Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1081338Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1083128Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1085029Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1087087Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1093402Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1104178Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1110235Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1111173Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1111543Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1117932Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1124566Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1125869Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1126285Z | ^~~~~~~~ 2025-07-17T07:30:42.1126908Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1127903Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1128804Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1129640Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1130448Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1131280Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1132358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1133512Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1134670Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1135897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1136938Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1137887Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1138736Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1139279Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1140110Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1140850Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1141197Z | ^~~~ 2025-07-17T07:30:42.1141761Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1142633Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1143457Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1144258Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1144998Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1145843Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1146771Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1147699Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1148590Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1149528Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1150492Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1151338Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1151882Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1152874Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1154450Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1156221Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1158004Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1159905Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1161982Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1167871Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1178171Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1183943Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1184859Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1185243Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1191195Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1197506Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1198809Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1199228Z | ^~~~~~~~ 2025-07-17T07:30:42.1199841Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1200851Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1201761Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1202598Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1203354Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1204199Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1205273Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1206420Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1207567Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1208747Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1221422Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1222412Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1223262Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1223833Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1224743Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1225497Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1225845Z | ^~~~ 2025-07-17T07:30:42.1226430Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1227398Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1228231Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1229022Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1229781Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1230631Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1231566Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1232497Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1233426Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1234381Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1235352Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1236191Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1236751Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1237777Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1239369Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1241100Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1242888Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1244791Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1246888Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1252784Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1262940Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1268713Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1269656Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1270043Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1276211Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1282486Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1283779Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1284188Z | ^~~~~~~~ 2025-07-17T07:30:42.1284820Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1285827Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1286736Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1287599Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1288375Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1289227Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1290298Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1291437Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1292632Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1293795Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1294823Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1295835Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1296687Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1297250Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1298095Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1298837Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1299185Z | ^~~~ 2025-07-17T07:30:42.1299768Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1300628Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1301450Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1302253Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1303009Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1303847Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1304782Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1305712Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1306626Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1307557Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1308535Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1309394Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1309954Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1310936Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1312523Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1314295Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1316074Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1317973Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1320110Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1325978Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1336015Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1341711Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1342653Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1343041Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1349091Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1355569Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1356858Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1357278Z | ^~~~~~~~ 2025-07-17T07:30:42.1357892Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1358897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1359798Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1360628Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1361385Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1362227Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1363294Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1364438Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1365621Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1366745Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1367761Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1368705Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1369543Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1370100Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1370924Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1371660Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1372154Z | ^~~~ 2025-07-17T07:30:42.1372741Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1373676Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1374480Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1375341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1376096Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1376936Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1377898Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1378842Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1379745Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1380694Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1381649Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1382504Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1383063Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1384081Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1385663Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1387394Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1389184Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1391089Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1393199Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1399064Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1409076Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1414830Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1415846Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1416235Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1422281Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1428632Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1429927Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1430330Z | ^~~~~~~~ 2025-07-17T07:30:42.1430958Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1432012Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1432946Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1433767Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1434533Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1435382Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1436452Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1437594Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1438747Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1439876Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1440900Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1441829Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1442682Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1443238Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1444070Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1444802Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1445183Z | ^~~~ 2025-07-17T07:30:42.1445768Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1446616Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1447437Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1448234Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1448982Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1449817Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1450753Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1451688Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1452584Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1453518Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1454482Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1455423Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1455989Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1457023Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1458628Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1460376Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1462167Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1464062Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1466130Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1472301Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1482348Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1488132Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1489665Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1490044Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1496181Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1502545Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1503844Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1504262Z | ^~~~~~~~ 2025-07-17T07:30:42.1504892Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1505940Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1506854Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1507675Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1508443Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1509288Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1512949Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1514151Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1515324Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1516493Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1517492Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1518449Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1519299Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1519878Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1520751Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1521541Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1521893Z | ^~~~ 2025-07-17T07:30:42.1522489Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1523357Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1524181Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1524977Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1525714Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1526559Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1527486Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1528389Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1529271Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1530216Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1531179Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1532027Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1532572Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1533575Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1535240Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1536985Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1538757Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1540739Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1542837Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1549069Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1559655Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1565624Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1566556Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1566940Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1573468Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1580243Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1581587Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1582000Z | ^~~~~~~~ 2025-07-17T07:30:42.1582663Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1583668Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1584557Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1585377Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1586137Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1586971Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1588044Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1589196Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1590346Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1591457Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1592476Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1593426Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1594270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1594811Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1595642Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1596382Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1596716Z | ^~~~ 2025-07-17T07:30:42.1597289Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1598149Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1598960Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1599781Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1600532Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1601404Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1602326Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1603255Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1604188Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1605128Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1606097Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1606965Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1607549Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1608558Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1610129Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1611873Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1613645Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1615621Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1617678Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1624114Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1634989Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1641096Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1642010Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1642389Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1648845Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1655593Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1656895Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1657313Z | ^~~~~~~~ 2025-07-17T07:30:42.1657931Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1658941Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1659883Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1660709Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1661470Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1662343Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1663415Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1664568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1665721Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1666849Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1667905Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1668896Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1669753Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1670294Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1671129Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1672051Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1672393Z | ^~~~ 2025-07-17T07:30:42.1672975Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1673844Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1674670Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1675462Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1676214Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1677053Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1677986Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1678876Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1679774Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1680722Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1681692Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1682532Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1683085Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1684086Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1685745Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1687494Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1689304Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1691210Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1693266Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1699737Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1710463Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1716493Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1717443Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1717824Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1724204Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1730923Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1732219Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1732638Z | ^~~~~~~~ 2025-07-17T07:30:42.1733260Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1734273Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1735250Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1736080Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1736828Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1737667Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1738735Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1739885Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1741045Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1742166Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1743179Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1744125Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1744970Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1745505Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1746372Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1747118Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1747451Z | ^~~~ 2025-07-17T07:30:42.1748053Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1748907Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1749721Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1750503Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1751254Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1752095Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1753062Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1753973Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1754865Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1755809Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1756774Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1757612Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1758167Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1759170Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1760752Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1762483Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1764247Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1766145Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1768195Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1774942Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1785875Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1792017Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1792963Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1793350Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1799777Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1806508Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1807814Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1808235Z | ^~~~~~~~ 2025-07-17T07:30:42.1808884Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1809897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1810799Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1811625Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1812384Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1813242Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1814342Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1815603Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1816754Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1817879Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1818899Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1819852Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1820710Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1821254Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1822094Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1822837Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1823169Z | ^~~~ 2025-07-17T07:30:42.1823743Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1824603Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1825421Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1826203Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1826954Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1827804Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1828730Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1829609Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1830501Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1831481Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1832457Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1833297Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1833880Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1834870Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1836447Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1838185Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1839988Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1841926Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1843978Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1850290Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1861295Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1867458Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1868393Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1868775Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1877651Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1885092Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1886409Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1886828Z | ^~~~~~~~ 2025-07-17T07:30:42.1887443Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1888457Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1889360Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1890185Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1890937Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1891782Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1892848Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1893997Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1895305Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1896453Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1897470Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1898466Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1899306Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1899864Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1900709Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1901455Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1901789Z | ^~~~ 2025-07-17T07:30:42.1902370Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1903262Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1904123Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1904909Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1905660Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1906502Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1907433Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1908327Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1909226Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1910178Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1911147Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1911980Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1912535Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1913532Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1915106Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1916834Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1918630Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1920528Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1922622Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.1929037Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.1939951Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.1946063Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.1947013Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.1947404Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.1953856Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.1960600Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.1961908Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.1962331Z | ^~~~~~~~ 2025-07-17T07:30:42.1962953Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.1963996Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.1964931Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.1965753Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.1966513Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.1967359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.1968431Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.1969580Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.1970724Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.1971977Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.1972997Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.1973948Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.1974784Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1975390Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1976227Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.1976970Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.1977305Z | ^~~~ 2025-07-17T07:30:42.1977884Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.1978741Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.1979556Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.1980341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.1981174Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.1982022Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.1982958Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.1983886Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.1984785Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.1985729Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.1986692Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.1987526Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.1988131Z from rng_extension.cpp:1: 2025-07-17T07:30:42.1989121Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.1990738Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.1992458Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.1994241Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.1996148Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.1998211Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.2004721Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.2015598Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.2021795Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.2022764Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.2023148Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.2029553Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.2036175Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.2037468Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.2037887Z | ^~~~~~~~ 2025-07-17T07:30:42.2038503Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.2039515Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.2040404Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.2041227Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.2042011Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.2042855Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.2043926Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.2045097Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.2046236Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.2047354Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.2048382Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.2049359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.2050194Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2050778Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2051607Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.2052347Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.2052681Z | ^~~~ 2025-07-17T07:30:42.2053254Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.2054114Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.2054937Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.2055824Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.2056595Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.2057446Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.2058378Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.2059267Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.2060162Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.2061108Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.2062077Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.2062916Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2063470Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2064461Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.2066030Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.2067802Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.2069611Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.2071552Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.2073750Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.2080134Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.2090924Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.2097094Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.2098037Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.2098424Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.2105056Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.2111870Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.2113174Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.2113602Z | ^~~~~~~~ 2025-07-17T07:30:42.2114221Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.2115226Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.2116132Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.2116960Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.2117720Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.2118568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.2119630Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.2120776Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.2121914Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.2123040Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.2124052Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.2125001Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.2125837Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2126394Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2127221Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.2127961Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.2128325Z | ^~~~ 2025-07-17T07:30:42.2128911Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.2129771Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.2130628Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.2131409Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.2132160Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.2133006Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.2133936Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.2134850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.2135839Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.2136814Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.2137780Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.2138616Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2139173Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2140172Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.2141750Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.2143481Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.2145264Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.2147164Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.2149215Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.2155636Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.2166517Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.2172827Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.2173771Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.2174158Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.2180666Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.2187356Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.2188663Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.2189158Z | ^~~~~~~~ 2025-07-17T07:30:42.2189783Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.2190794Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.2191732Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.2192558Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.2193313Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.2194156Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.2195221Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.2196416Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.2197595Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.2198721Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.2199736Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.2200680Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.2201512Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2202068Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2202900Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.2203646Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.2203985Z | ^~~~ 2025-07-17T07:30:42.2204561Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.2205429Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.2206253Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.2207034Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.2207788Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.2208638Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.2209575Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.2210462Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.2211358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.2212299Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.2213265Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.2214137Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2214698Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2215766Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.2217399Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.2219119Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.2220918Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.2222856Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.2224944Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.2230849Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.2240913Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.2246636Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.2247587Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.2248012Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.2253989Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.2260427Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.2261729Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.2262132Z | ^~~~~~~~ 2025-07-17T07:30:42.2262765Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.2263783Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.2264680Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.2265484Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.2266252Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.2267100Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.2268160Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.2269301Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.2270456Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.2271577Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.2272813Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.2273751Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.2274685Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2275250Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2276106Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.2276877Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.2277223Z | ^~~~ 2025-07-17T07:30:42.2277803Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.2278655Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.2279479Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.2280273Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.2281028Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.2281896Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.2282857Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.2283756Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.2284651Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.2285582Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.2286554Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.2287406Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2287961Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2288959Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.2290532Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.2291481Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.2292405Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.2293479Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.2294574Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.2300019Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.2306150Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.2306994Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.2307165Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.2307282Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.2313493Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.2314810Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.2315047Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.2315134Z | ^~~~~~~~ 2025-07-17T07:30:42.2315616Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.2316035Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.2316397Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.2316744Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.2317040Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.2317505Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.2318055Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.2318570Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.2319091Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.2319561Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.2319985Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.2320398Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.2320731Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2320844Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2321443Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.2321589Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.2321679Z | ^~~~ 2025-07-17T07:30:42.2322065Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.2322424Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.2322770Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.2323098Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.2323406Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.2323821Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.2324219Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.2324602Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.2325020Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.2325458Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.2325867Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.2326217Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2326330Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2327095Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.2327955Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.2328901Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.2329880Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.2330947Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.2332043Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.2337440Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.2343566Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.2344411Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.2344580Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.2344696Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.2350944Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.2352273Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.2352494Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.2352581Z | ^~~~~~~~ 2025-07-17T07:30:42.2353068Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.2353491Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.2353847Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.2354206Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.2354504Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.2354938Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.2355548Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.2356135Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.2356675Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.2357151Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.2357609Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.2358017Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.2358330Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2358455Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2359058Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.2359210Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.2359331Z | ^~~~ 2025-07-17T07:30:42.2359717Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.2360104Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.2360452Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.2360798Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.2361100Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.2361517Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.2361919Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.2362306Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.2362708Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.2363146Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.2363555Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.2363885Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2363998Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2364775Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.2365641Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.2366582Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.2367517Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.2368615Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.2369718Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.2375433Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.2381579Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.2382413Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.2382571Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.2382700Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.2388980Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.2390283Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.2390508Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.2390627Z | ^~~~~~~~ 2025-07-17T07:30:42.2391103Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.2391565Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.2391920Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.2392279Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.2392575Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.2393000Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.2393537Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.2394043Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.2394587Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.2395065Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.2395495Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.2395903Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.2396224Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2396356Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2396956Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.2397095Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.2397204Z | ^~~~ 2025-07-17T07:30:42.2397591Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.2397947Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.2398291Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.2398640Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.2398956Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.2399371Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.2399798Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.2400182Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.2400564Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.2401015Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.2401424Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.2401777Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2401890Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2402686Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.2403559Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.2404493Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.2405429Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.2406490Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.2407599Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.2412516Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.2418244Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.2419124Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.2419313Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.2419442Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.2425220Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.2426509Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.2426713Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.2426816Z | ^~~~~~~~ 2025-07-17T07:30:42.2427278Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.2427705Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.2428070Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.2428415Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.2428725Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.2429183Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.2429708Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.2430223Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.2430779Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.2431263Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.2431677Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.2432100Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.2432445Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2432557Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2433194Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.2433332Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.2433423Z | ^~~~ 2025-07-17T07:30:42.2433824Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:30:42.2434167Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:30:42.2434525Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:30:42.2434850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:30:42.2435156Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:30:42.2435590Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:30:42.2435983Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:30:42.2436382Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:30:42.2436764Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:30:42.2437199Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:30:42.2437617Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:30:42.2437935Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2438062Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2438816Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:30:42.2439680Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:30:42.2440625Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:30:42.2441571Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:30:42.2442642Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:30:42.2443767Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:30:42.2449117Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:30:42.2455290Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:30:42.2456123Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:30:42.2456280Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:30:42.2456392Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:30:42.2462646Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:30:42.2464011Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:30:42.2464218Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:30:42.2464318Z | ^~~~~~~~ 2025-07-17T07:30:42.2464787Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:30:42.2465203Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:30:42.2465567Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:30:42.2465914Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:30:42.2466227Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:30:42.2466657Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:30:42.2467178Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:30:42.2467693Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:30:42.2468222Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:30:42.2468711Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:30:42.2469126Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:30:42.2469534Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:30:42.2469864Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:30:42.2469978Z from rng_extension.cpp:1: 2025-07-17T07:30:42.2470586Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:30:42.2470725Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:30:42.2470816Z | ^~~~ 2025-07-17T07:30:42.2474176Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:42.5937860Z running install_lib 2025-07-17T07:30:42.6026702Z 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 2025-07-17T07:30:42.6107049Z 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 2025-07-17T07:30:42.6188610Z 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 2025-07-17T07:30:42.6274040Z running install_egg_info 2025-07-17T07:30:42.6467737Z running egg_info 2025-07-17T07:30:42.6545277Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-07-17T07:30:42.6548477Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-07-17T07:30:42.6556173Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-07-17T07:30:42.6567677Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-07-17T07:30:42.6658098Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T07:30:42.6666419Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T07:30:42.6677758Z 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) 2025-07-17T07:30:42.6679267Z 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 2025-07-17T07:30:42.6685485Z running install_scripts 2025-07-17T07:30:45.4890167Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:30:45.4891767Z import pkg_resources 2025-07-17T07:30:45.5155602Z 2025-07-17T07:30:45.5155724Z Running tests... 2025-07-17T07:30:45.5156086Z ---------------------------------------------------------------------- 2025-07-17T07:30:45.5290987Z . 2025-07-17T07:30:45.5291318Z ---------------------------------------------------------------------- 2025-07-17T07:30:45.5291698Z Ran 1 test in 0.014s 2025-07-17T07:30:45.5291872Z 2025-07-17T07:30:45.5291954Z OK 2025-07-17T07:30:45.5292082Z 2025-07-17T07:30:45.5292188Z Generating XML reports... 2025-07-17T07:30:46.1091146Z Running test_cpp_extensions_aot_ninja 1/1 ... [2025-07-17 07:30:46.108833] 2025-07-17T07:30:48.3681025Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:30:48.3682583Z import pkg_resources 2025-07-17T07:30:48.4353123Z running install 2025-07-17T07:30:48.4355623Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T07:30:48.4356431Z !! 2025-07-17T07:30:48.4356560Z 2025-07-17T07:30:48.4356686Z ******************************************************************************** 2025-07-17T07:30:48.4357083Z Please avoid running ``setup.py`` directly. 2025-07-17T07:30:48.4357552Z Instead, use pypa/build, pypa/installer or other 2025-07-17T07:30:48.4357931Z standards-based tools. 2025-07-17T07:30:48.4358137Z 2025-07-17T07:30:48.4358368Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T07:30:48.4358865Z or your builds will no longer be supported. 2025-07-17T07:30:48.4359111Z 2025-07-17T07:30:48.4359406Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T07:30:48.4359939Z ******************************************************************************** 2025-07-17T07:30:48.4360196Z 2025-07-17T07:30:48.4360276Z !! 2025-07-17T07:30:48.4360507Z self.initialize_options() 2025-07-17T07:30:48.4490612Z running build 2025-07-17T07:30:48.4490863Z running build_py 2025-07-17T07:30:48.4576325Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-07-17T07:30:48.4578834Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-07-17T07:30:48.4582321Z running build_ext 2025-07-17T07:30:48.4914297Z building 'torch_test_cpp_extension.cpp' extension 2025-07-17T07:30:48.4916038Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313 2025-07-17T07:30:49.2835402Z [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/share/python_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 -std=c++17 2025-07-17T07:30:49.2939132Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:49.7027961Z building 'torch_test_cpp_extension.maia' extension 2025-07-17T07:30:50.4514180Z [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/share/python_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 -std=c++17 2025-07-17T07:30:50.4567925Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:50.8572761Z building 'torch_test_cpp_extension.rng' extension 2025-07-17T07:30:51.6997102Z [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/share/python_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 -std=c++17 2025-07-17T07:30:51.7051543Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:52.1502978Z running install_lib 2025-07-17T07:30:52.1592323Z 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 2025-07-17T07:30:52.1636064Z 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 2025-07-17T07:30:52.1680850Z 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 2025-07-17T07:30:52.1728497Z running install_egg_info 2025-07-17T07:30:52.1924472Z running egg_info 2025-07-17T07:30:52.2002058Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-07-17T07:30:52.2005290Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-07-17T07:30:52.2007415Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-07-17T07:30:52.2009628Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-07-17T07:30:52.2092586Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T07:30:52.2101238Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T07:30:52.2102936Z 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) 2025-07-17T07:30:52.2104593Z 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 2025-07-17T07:30:52.2110982Z running install_scripts 2025-07-17T07:30:54.1527098Z running install 2025-07-17T07:30:54.1528596Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T07:30:54.1529405Z !! 2025-07-17T07:30:54.1529529Z 2025-07-17T07:30:54.1529733Z ******************************************************************************** 2025-07-17T07:30:54.1530114Z Please avoid running ``setup.py`` directly. 2025-07-17T07:30:54.1530526Z Instead, use pypa/build, pypa/installer or other 2025-07-17T07:30:54.1530903Z standards-based tools. 2025-07-17T07:30:54.1531091Z 2025-07-17T07:30:54.1531334Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T07:30:54.1531814Z or your builds will no longer be supported. 2025-07-17T07:30:54.1532072Z 2025-07-17T07:30:54.1532385Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T07:30:54.1532933Z ******************************************************************************** 2025-07-17T07:30:54.1533179Z 2025-07-17T07:30:54.1533270Z !! 2025-07-17T07:30:54.1533477Z self.initialize_options() 2025-07-17T07:30:54.1661664Z running build 2025-07-17T07:30:54.1661926Z running build_ext 2025-07-17T07:30:54.1995141Z building 'no_python_abi_suffix_test' extension 2025-07-17T07:30:54.1997612Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313 2025-07-17T07:30:54.3431862Z [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/share/python_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 -std=c++17 2025-07-17T07:30:54.3476738Z creating build/lib.linux-x86_64-cpython-313 2025-07-17T07:30:54.3480539Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:30:54.4481614Z running install_lib 2025-07-17T07:30:54.4573073Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-07-17T07:30:54.4576627Z 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 2025-07-17T07:30:54.4581641Z running install_egg_info 2025-07-17T07:30:54.4774576Z running egg_info 2025-07-17T07:30:54.4850426Z creating no_python_abi_suffix_test.egg-info 2025-07-17T07:30:54.4851335Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2025-07-17T07:30:54.4854590Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2025-07-17T07:30:54.4856883Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2025-07-17T07:30:54.4857988Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T07:30:54.4938897Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T07:30:54.4945710Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T07:30:54.4947148Z 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 2025-07-17T07:30:54.4952322Z running install_scripts 2025-07-17T07:30:54.8462996Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:30:54.8465919Z 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'] ... [2025-07-17 07:30:54.846374] 2025-07-17T07:30:57.8430088Z 2025-07-17T07:30:57.8431070Z 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_64a7628aee4e979d_.log 2025-07-17T07:30:57.8439265Z Running 21 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library 2025-07-17T07:30:57.8446788Z 2025-07-17T07:30:57.8447041Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2025-07-17 07:30:57.843324] 2025-07-17T07:31:00.1012481Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:31:00.1014290Z import pkg_resources 2025-07-17T07:31:00.1695864Z running install 2025-07-17T07:31:00.1698184Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T07:31:00.1698980Z !! 2025-07-17T07:31:00.1699116Z 2025-07-17T07:31:00.1699265Z ******************************************************************************** 2025-07-17T07:31:00.1699646Z Please avoid running ``setup.py`` directly. 2025-07-17T07:31:00.1700191Z Instead, use pypa/build, pypa/installer or other 2025-07-17T07:31:00.1700575Z standards-based tools. 2025-07-17T07:31:00.1700772Z 2025-07-17T07:31:00.1701074Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T07:31:00.1701553Z or your builds will no longer be supported. 2025-07-17T07:31:00.1701808Z 2025-07-17T07:31:00.1702110Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T07:31:00.1702652Z ******************************************************************************** 2025-07-17T07:31:00.1702895Z 2025-07-17T07:31:00.1702975Z !! 2025-07-17T07:31:00.1703194Z self.initialize_options() 2025-07-17T07:31:00.1834364Z running build 2025-07-17T07:31:00.1834656Z running build_py 2025-07-17T07:31:00.1920488Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-07-17T07:31:00.1922245Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-07-17T07:31:00.1925514Z running build_ext 2025-07-17T07:31:00.1939417Z building 'torch_test_cpp_extension.cpp' extension 2025-07-17T07:31:00.1941093Z creating build/temp.linux-x86_64-cpython-313 2025-07-17T07:31:00.1945732Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -std=c++17 2025-07-17T07:31:01.2468154Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-07-17T07:31:01.2469373Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-07-17T07:31:01.2470252Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2025-07-17T07:31:01.2470813Z from extension.cpp:1: 2025-07-17T07:31:01.2472344Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-07-17T07:31:01.2473229Z extension.cpp:45:53: required from here 2025-07-17T07:31:01.2474865Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1539:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-07-17T07:31:01.2476138Z 1539 | class class_ : public detail::generic_type { 2025-07-17T07:31:01.2476491Z | ^~~~~~ 2025-07-17T07:31:01.2478178Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-07-17T07:31:01.2479633Z extension.cpp:45:53: required from here 2025-07-17T07:31:01.2481997Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:1599:28: warning: ‘pybind11::class_::class_<>(pybind11::handle, const char*)::’ declared with greater visibility than the type of its field ‘pybind11::class_::class_<>(pybind11::handle, const char*)::::’ [-Wattributes] 2025-07-17T07:31:01.2484042Z 1599 | with_internals([&](internals &internals) { 2025-07-17T07:31:01.2484428Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:01.2485014Z 1600 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-07-17T07:31:01.2485583Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:01.2486042Z 1601 | : internals.registered_types_cpp; 2025-07-17T07:31:01.2486467Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:01.2486894Z 1602 | instances[std::type_index(typeid(type_alias))] 2025-07-17T07:31:01.2487291Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:01.2487696Z 1603 | = instances[std::type_index(typeid(type))]; 2025-07-17T07:31:01.2488095Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:01.2488415Z 1604 | }); 2025-07-17T07:31:01.2488671Z | ~ 2025-07-17T07:31:01.2492004Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:31:01.7939909Z building 'torch_test_cpp_extension.maia' extension 2025-07-17T07:31:01.7944629Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -std=c++17 2025-07-17T07:31:02.8839490Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:31:03.3935942Z building 'torch_test_cpp_extension.rng' extension 2025-07-17T07:31:03.3940711Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -std=c++17 2025-07-17T07:31:04.6140942Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T07:31:04.6142544Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T07:31:04.6143767Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T07:31:04.6145146Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T07:31:04.6146254Z from rng_extension.cpp:6: 2025-07-17T07:31:04.6148020Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1458: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T07:31:04.6149304Z 1458 | #pragma unroll 2025-07-17T07:31:04.6149653Z | 2025-07-17T07:31:04.6150365Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-07-17T07:31:04.6151517Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1510, 2025-07-17T07:31:04.6152866Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T07:31:04.6154187Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T07:31:04.6155339Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T07:31:04.6156472Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T07:31:04.6157393Z from rng_extension.cpp:6: 2025-07-17T07:31:04.6158715Z /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] 2025-07-17T07:31:04.6159598Z 59 | #pragma unroll 2025-07-17T07:31:04.6159831Z | 2025-07-17T07:31:04.6160665Z /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] 2025-07-17T07:31:04.6161460Z 72 | #pragma unroll 2025-07-17T07:31:04.6161690Z | 2025-07-17T07:31:04.6162652Z /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] 2025-07-17T07:31:04.6163458Z 87 | #pragma unroll 2025-07-17T07:31:04.6163707Z | 2025-07-17T07:31:04.6164233Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1511, 2025-07-17T07:31:04.6165216Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-07-17T07:31:04.6166041Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-07-17T07:31:04.6166843Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-07-17T07:31:04.6167814Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-07-17T07:31:04.6168520Z from rng_extension.cpp:6: 2025-07-17T07:31:04.6169492Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-07-17T07:31:04.6170375Z 160 | #pragma unroll 2025-07-17T07:31:04.6170605Z | 2025-07-17T07:31:04.6171125Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6172235Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6173065Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6173869Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6174610Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6175539Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6176478Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6177383Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6178277Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6179229Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6180200Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6181058Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6181605Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6182684Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6184273Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6186053Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6187843Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6189791Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6191882Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6198367Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6209223Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6215467Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6216398Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6216788Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6223210Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6229937Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6231236Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6231689Z | ^~~~~~~~ 2025-07-17T07:31:04.6232317Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6233351Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6234255Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6235082Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6235853Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6236690Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6237761Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6238905Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6240066Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6241184Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6242198Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6243154Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6244003Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6244552Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6245391Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6246142Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6246490Z | ^~~~ 2025-07-17T07:31:04.6247058Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6247919Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6248738Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6249585Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6250332Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6251182Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6252148Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6253044Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6253925Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6254870Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6255925Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6256814Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6257367Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6258393Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6259970Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6261696Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6263485Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6265388Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6267435Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6273855Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6284611Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6290791Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6291715Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6292096Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6298445Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6305067Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6306367Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6306784Z | ^~~~~~~~ 2025-07-17T07:31:04.6307411Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6308408Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6309309Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6310166Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6310931Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6311769Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6312873Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6314021Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6315173Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6316284Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6317306Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6318289Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6319167Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6319714Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6320551Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6321301Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6321639Z | ^~~~ 2025-07-17T07:31:04.6322215Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6323079Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6323907Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6324697Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6325462Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6326315Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6327249Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6328135Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6329042Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6329996Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6330972Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6331811Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6332373Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6333360Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6334930Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6336798Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6338587Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6340522Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6342578Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6348475Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6360113Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6366113Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6367089Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6367471Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6373759Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6380249Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6381566Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6381982Z | ^~~~~~~~ 2025-07-17T07:31:04.6382601Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6383616Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6384511Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6385326Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6386094Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6386934Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6387998Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6389138Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6390297Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6391421Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6392435Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6393385Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6394279Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6394844Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6395683Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6396418Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6396767Z | ^~~~ 2025-07-17T07:31:04.6397343Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6398244Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6399078Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6399908Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6400664Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6401510Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6402424Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6403321Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6404223Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6405172Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6406125Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6406975Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6407530Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6408517Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6410074Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6411805Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6413596Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6415571Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6417632Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6423608Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6433628Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6439366Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6440304Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6440688Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6446637Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6452962Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6454276Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6454698Z | ^~~~~~~~ 2025-07-17T07:31:04.6455394Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6456412Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6457293Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6458120Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6458935Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6459807Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6460864Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6462007Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6463160Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6464301Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6465313Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6466270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6467124Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6467683Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6468510Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6469265Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6469614Z | ^~~~ 2025-07-17T07:31:04.6470193Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6471046Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6472039Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6472853Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6473617Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6474453Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6475387Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6476295Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6477253Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6478200Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6479218Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6480074Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6480630Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6481620Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6483202Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6484972Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6486798Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6488708Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6490764Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6496706Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6506739Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6512498Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6513437Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6513807Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6519836Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6526145Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6527447Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6527868Z | ^~~~~~~~ 2025-07-17T07:31:04.6528497Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6529492Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6530387Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6531214Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6531979Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6532823Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6533894Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6535036Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6536263Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6537407Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6538427Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6539413Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6540263Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6540809Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6541641Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6542380Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6542729Z | ^~~~ 2025-07-17T07:31:04.6543293Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6544153Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6545003Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6545815Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6546565Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6547403Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6548331Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6549217Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6550114Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6551063Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6552035Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6552882Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6553422Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6554408Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6555987Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6557719Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6559512Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6561402Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6563483Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6569746Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6580817Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6586827Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6587760Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6588151Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6594455Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6601108Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6602409Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6602826Z | ^~~~~~~~ 2025-07-17T07:31:04.6603439Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6604455Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6605402Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6606255Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6607012Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6607933Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6608997Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6610144Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6611304Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6612420Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6613445Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6614396Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6615324Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6615875Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6616723Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6617474Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6617816Z | ^~~~ 2025-07-17T07:31:04.6618392Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6619263Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6620087Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6620875Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6621629Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6622474Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6623446Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6624333Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6625270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6626223Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6627199Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6628041Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6628600Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6629597Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6631237Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6632981Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6634746Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6636654Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6638706Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6644970Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6655665Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6661742Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6662712Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6663103Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6669504Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6676326Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6677627Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6678041Z | ^~~~~~~~ 2025-07-17T07:31:04.6678668Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6679683Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6680582Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6681408Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6682164Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6683006Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6684176Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6685340Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6686539Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6687673Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6688706Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6689661Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6690504Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6691224Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6692074Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6692823Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6693207Z | ^~~~ 2025-07-17T07:31:04.6693783Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6694644Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6695533Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6696322Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6697086Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6697938Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6698882Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6699774Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6700675Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6701616Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6702585Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6703424Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6703981Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6704981Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6706559Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6708276Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6710105Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6712023Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6714117Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6720061Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6730091Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6735861Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6736806Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6737196Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6743225Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6749570Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6750875Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6751309Z | ^~~~~~~~ 2025-07-17T07:31:04.6751932Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6752971Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6753887Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6754695Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6755464Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6756309Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6757374Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6758508Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6759673Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6760804Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6761816Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6762757Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6763614Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6764182Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6765023Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6765765Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6766115Z | ^~~~ 2025-07-17T07:31:04.6766693Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6767535Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6768357Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6769188Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6769953Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6770790Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6771916Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6772828Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6773728Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6774654Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6775702Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6776620Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6777178Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6778246Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6779828Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6781567Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6783363Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6785273Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6787328Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6793240Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6803291Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6809081Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6810023Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6810428Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6816459Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6822825Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6824117Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6824539Z | ^~~~~~~~ 2025-07-17T07:31:04.6825168Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6826181Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6827071Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6827898Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6828665Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6829530Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6830608Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6831758Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6832943Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6834078Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6835078Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6836026Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6836880Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6837467Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6838283Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6839054Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6839402Z | ^~~~ 2025-07-17T07:31:04.6839966Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6840829Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6841830Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6842639Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6843381Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6844233Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6845168Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6846061Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6846943Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6847887Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6848860Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6849707Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6850251Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6851247Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6852822Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6854560Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6856480Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6858477Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6860583Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6866523Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6876711Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6882453Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6883381Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6883765Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6889815Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6896276Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6897574Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6898058Z | ^~~~~~~~ 2025-07-17T07:31:04.6911170Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6912341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6913247Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6914073Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6914841Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6915677Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6916757Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6917910Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6919069Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6920201Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6921204Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6922165Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6923019Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6923585Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6924492Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6925247Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6925592Z | ^~~~ 2025-07-17T07:31:04.6926157Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.6927020Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.6927843Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.6928684Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.6929430Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.6930276Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.6931291Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.6932191Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.6933077Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.6934018Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.6934995Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.6935971Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6936522Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6937586Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.6939176Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.6940917Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.6942692Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.6944610Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.6946664Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.6952585Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.6962630Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.6968353Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.6969317Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.6969691Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.6975910Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.6982280Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.6983573Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.6983990Z | ^~~~~~~~ 2025-07-17T07:31:04.6984620Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.6985629Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.6986503Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.6987324Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.6988088Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.6988998Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.6990052Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.6991241Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.6992392Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.6993514Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.6994516Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.6995464Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.6996352Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.6996908Z from rng_extension.cpp:1: 2025-07-17T07:31:04.6997770Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.6998517Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.6998863Z | ^~~~ 2025-07-17T07:31:04.6999424Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7000287Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7001112Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7001911Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7002654Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7003503Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7004433Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7005329Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7006215Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7007168Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7008144Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7009000Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7009550Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7010543Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7012115Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7013892Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7015833Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7019039Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7022582Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7032856Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7049678Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7060693Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7062405Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7063074Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7070382Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7077000Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7078387Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7078811Z | ^~~~~~~~ 2025-07-17T07:31:04.7079486Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7080489Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7081384Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7082210Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7082979Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7083813Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7084890Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7086048Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7087210Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7088326Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7089339Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7090291Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7091143Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7091692Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7092525Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7093272Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7093606Z | ^~~~ 2025-07-17T07:31:04.7094183Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7095043Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7095986Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7096773Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7097533Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7098424Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7099353Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7100239Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7101145Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7102100Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7103107Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7103947Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7104535Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7105539Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7107112Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7108936Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7110720Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7112634Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7114697Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7121014Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7131688Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7137854Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7138796Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7139180Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7145449Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7152074Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7153379Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7153796Z | ^~~~~~~~ 2025-07-17T07:31:04.7154425Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7155427Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7156331Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7157212Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7157990Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7158828Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7159929Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7161082Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7162238Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7163357Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7164405Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7165383Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7166240Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7166790Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7167629Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7168382Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7168729Z | ^~~~ 2025-07-17T07:31:04.7169291Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7170162Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7170993Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7171970Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7172735Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7173578Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7174510Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7175501Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7176389Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7177341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7178310Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7179157Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7179702Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7180699Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7182366Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7184109Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7185944Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7187829Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7189890Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7196282Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7207049Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7213148Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7214068Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7214483Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7220975Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7228627Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7229924Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7230342Z | ^~~~~~~~ 2025-07-17T07:31:04.7230976Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7231972Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7232874Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7233698Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7234460Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7235287Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7236354Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7237503Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7238664Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7239779Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7240793Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7241737Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7242582Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7243127Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7244001Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7244754Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7245101Z | ^~~~ 2025-07-17T07:31:04.7245665Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7246565Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7247389Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7248185Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7248915Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7249763Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7250716Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7251612Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7252537Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7253483Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7254448Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7255374Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7255921Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7256917Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7258497Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7260229Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7262002Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7263897Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7265955Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7272567Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7283387Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7289535Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7290454Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7290844Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7297322Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7304060Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7305367Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7305790Z | ^~~~~~~~ 2025-07-17T07:31:04.7306419Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7307442Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7308344Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7309167Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7309938Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7310778Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7311876Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7313056Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7314216Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7315331Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7316357Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7317311Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7318166Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7318709Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7319539Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7320286Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7320630Z | ^~~~ 2025-07-17T07:31:04.7321194Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7322052Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7322875Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7323675Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7324412Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7325263Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7326186Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7327081Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7327962Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7328930Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7329895Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7330744Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7331314Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7332295Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7333868Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7335676Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7337467Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7339434Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7341489Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7347875Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7358692Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7364986Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7365907Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7366288Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7372942Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7379689Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7380986Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7381407Z | ^~~~~~~~ 2025-07-17T07:31:04.7382033Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7383028Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7383935Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7384762Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7385531Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7386366Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7387428Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7388572Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7389723Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7390869Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7391887Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7392879Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7393733Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7394277Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7395105Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7395855Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7396201Z | ^~~~ 2025-07-17T07:31:04.7396764Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7397658Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7398478Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7399297Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7400048Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7400893Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7401819Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7402710Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7403600Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7404547Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7405515Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7406365Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7406905Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7407891Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7409463Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7411192Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7412968Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7414853Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7417024Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7423363Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7434196Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7440312Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7441242Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7441629Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7448091Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7454760Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7456125Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7456551Z | ^~~~~~~~ 2025-07-17T07:31:04.7457187Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7458223Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7459148Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7459976Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7460745Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7461582Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7462658Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7463817Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7464984Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7466108Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7467129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7468085Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7468942Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7469488Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7470317Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7471066Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7471411Z | ^~~~ 2025-07-17T07:31:04.7472118Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7472990Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7473810Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7474592Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7475339Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7476245Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7477179Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7478116Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7479014Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7479961Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7480928Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7481774Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7482336Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7483373Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7484984Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7486719Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7488500Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7490380Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7492437Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7498908Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7509780Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7515950Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7516903Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7517292Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7523701Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7530397Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7531696Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7532114Z | ^~~~~~~~ 2025-07-17T07:31:04.7532746Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7533741Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7534637Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7535499Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7536258Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7537137Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7538205Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7539386Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7540540Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7541651Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7542669Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7543622Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7544502Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7545047Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7545907Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7546657Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7546989Z | ^~~~ 2025-07-17T07:31:04.7547560Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7548423Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7549245Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7550032Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7550778Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7551628Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7552554Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7553445Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7554341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7555291Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7556262Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7557102Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7557657Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7558641Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7560210Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7561977Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7563748Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7565678Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7567724Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7574272Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7585141Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7591270Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7592198Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7592583Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7599047Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7605824Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7607133Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7607560Z | ^~~~~~~~ 2025-07-17T07:31:04.7608191Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7609193Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7610204Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7611037Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7611807Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7612653Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7613723Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7614870Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7616086Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7617209Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7618217Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7619167Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7620012Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7620558Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7621398Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7622139Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7622468Z | ^~~~ 2025-07-17T07:31:04.7623092Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7623960Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7624821Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7625607Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7626357Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7627198Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7628128Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7629016Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7629943Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7630919Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7631890Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7632726Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7633280Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7634266Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7635841Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7637587Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7639358Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7641249Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7643307Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7649723Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7660653Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7666830Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7667753Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7668143Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7674714Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7681340Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7682645Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7683065Z | ^~~~~~~~ 2025-07-17T07:31:04.7683766Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7684786Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7685729Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7686554Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7687306Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7688151Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7689223Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7690371Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7691610Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7692728Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7693744Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7694688Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7695617Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7696163Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7696995Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7697738Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7698075Z | ^~~~ 2025-07-17T07:31:04.7698653Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7699519Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7700340Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7701128Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7701884Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7702730Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7703662Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7704547Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7705441Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7706378Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7707346Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7708224Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7708786Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7709773Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7711388Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7713120Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7714905Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7716806Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7718926Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7725306Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7736106Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7742171Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7743129Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7743517Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7749924Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7756619Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7757918Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7758342Z | ^~~~~~~~ 2025-07-17T07:31:04.7758964Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7759981Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7760878Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7761701Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7762461Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7763307Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7764380Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7765529Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7766685Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7767797Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7768846Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7769796Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7770645Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7771219Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7772189Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7772940Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7773273Z | ^~~~ 2025-07-17T07:31:04.7773852Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7774717Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7775622Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7776478Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7777270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7778114Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7779044Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7779929Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7780831Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7781772Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7782739Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7783578Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7784136Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7785130Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7786699Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7788428Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7790198Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7792100Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7794150Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7800120Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7810192Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7816059Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7817000Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7817390Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7823385Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7829739Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7831044Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7831483Z | ^~~~~~~~ 2025-07-17T07:31:04.7832111Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7833120Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7834007Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7834821Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7835594Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7836480Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7837574Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7838717Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7839872Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7840994Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7842004Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7842940Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7843794Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7844350Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7845184Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7845915Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7846262Z | ^~~~ 2025-07-17T07:31:04.7846841Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7847712Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7848061Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7848385Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7848706Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7849125Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7849521Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7849906Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7850327Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7850768Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7851178Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7851538Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7851651Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7852410Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7853284Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7854223Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7855292Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7856365Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7857467Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7862910Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7869040Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7869920Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7870076Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7870202Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7876545Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7877847Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7878070Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7878159Z | ^~~~~~~~ 2025-07-17T07:31:04.7878630Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7879066Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7879420Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7879771Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7880167Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7880590Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7881126Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7881631Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7882236Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7882749Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7883205Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7883628Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7883947Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7884079Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7884683Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7884827Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7884934Z | ^~~~ 2025-07-17T07:31:04.7885323Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7885674Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7886035Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7886355Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7886670Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7887086Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7887475Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7887873Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7888258Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7888708Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7889114Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7889432Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7889556Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7890310Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7891186Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7892160Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7893091Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7894170Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7895337Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7901052Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7907760Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7908705Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7908867Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7908992Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7915254Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7916559Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7916767Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7916862Z | ^~~~~~~~ 2025-07-17T07:31:04.7917337Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7917771Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7918129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7918472Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7918779Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7919197Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7919735Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7920240Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7920768Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7921249Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7921665Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7922090Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7922438Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7922556Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7923172Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7923342Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7923434Z | ^~~~ 2025-07-17T07:31:04.7923832Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7924176Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7924537Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7924857Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7925163Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7925619Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7926036Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7926433Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7926815Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7927247Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7927667Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7927979Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7928105Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7928857Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7929727Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7930657Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7931589Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7932646Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7933752Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7939234Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7945320Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7946190Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7946346Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7946461Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7952613Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7953918Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7954161Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7954260Z | ^~~~~~~~ 2025-07-17T07:31:04.7954726Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7955146Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7955507Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7955855Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7956159Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7956609Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7957157Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7957675Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7958197Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7958680Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7959097Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7959506Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7959838Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7959950Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7960561Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7960696Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7960789Z | ^~~~ 2025-07-17T07:31:04.7961188Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7961538Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7961897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7962219Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7962524Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7962951Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7963338Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.7963733Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.7964146Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.7964584Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.7965005Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.7965348Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7965471Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7966229Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.7967087Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.7968040Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.7969026Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.7970093Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.7971175Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.7976319Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.7982007Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.7982863Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.7983031Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.7983142Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.7988966Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.7990286Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.7990514Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.7990601Z | ^~~~~~~~ 2025-07-17T07:31:04.7991083Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.7991502Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.7991848Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.7992210Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.7992505Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.7992936Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.7993461Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.7993967Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.7994505Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.7995009Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.7995439Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.7995843Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.7996197Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.7996324Z from rng_extension.cpp:1: 2025-07-17T07:31:04.7996926Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.7997075Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.7997168Z | ^~~~ 2025-07-17T07:31:04.7997555Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-07-17T07:31:04.7997914Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-07-17T07:31:04.7998289Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-07-17T07:31:04.7998650Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-07-17T07:31:04.7998952Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-07-17T07:31:04.7999365Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-07-17T07:31:04.7999762Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-07-17T07:31:04.8000144Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-07-17T07:31:04.8000539Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-07-17T07:31:04.8000975Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-07-17T07:31:04.8001388Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-07-17T07:31:04.8001711Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.8001823Z from rng_extension.cpp:1: 2025-07-17T07:31:04.8002590Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-07-17T07:31:04.8003452Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-07-17T07:31:04.8004401Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-07-17T07:31:04.8005331Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-07-17T07:31:04.8006384Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-07-17T07:31:04.8007514Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-07-17T07:31:04.8012832Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-07-17T07:31:04.8019046Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-07-17T07:31:04.8019887Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-07-17T07:31:04.8020059Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-07-17T07:31:04.8020173Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-07-17T07:31:04.8026438Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-07-17T07:31:04.8027741Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-07-17T07:31:04.8027961Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-07-17T07:31:04.8028049Z | ^~~~~~~~ 2025-07-17T07:31:04.8028537Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-07-17T07:31:04.8028958Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-07-17T07:31:04.8029338Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-07-17T07:31:04.8029729Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-07-17T07:31:04.8030027Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-07-17T07:31:04.8030461Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-07-17T07:31:04.8030980Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-07-17T07:31:04.8031489Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-07-17T07:31:04.8032028Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-07-17T07:31:04.8032507Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-07-17T07:31:04.8032936Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-07-17T07:31:04.8033344Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-07-17T07:31:04.8033660Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-07-17T07:31:04.8033792Z from rng_extension.cpp:1: 2025-07-17T07:31:04.8034395Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-07-17T07:31:04.8034551Z 413 | PtrVector data(base, base + ntensor); 2025-07-17T07:31:04.8034648Z | ^~~~ 2025-07-17T07:31:04.8037840Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:31:05.1450247Z running install_lib 2025-07-17T07:31:05.1540530Z 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 2025-07-17T07:31:05.1612459Z 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 2025-07-17T07:31:05.1686030Z 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 2025-07-17T07:31:05.1764605Z running install_egg_info 2025-07-17T07:31:05.1961442Z running egg_info 2025-07-17T07:31:05.2039843Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-07-17T07:31:05.2043218Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-07-17T07:31:05.2045344Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-07-17T07:31:05.2047657Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-07-17T07:31:05.2132791Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T07:31:05.2141378Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-07-17T07:31:05.2143104Z 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) 2025-07-17T07:31:05.2144750Z 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 2025-07-17T07:31:05.2151135Z running install_scripts 2025-07-17T07:31:07.1364825Z running install 2025-07-17T07:31:07.1366318Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-07-17T07:31:07.1367118Z !! 2025-07-17T07:31:07.1367283Z 2025-07-17T07:31:07.1367406Z ******************************************************************************** 2025-07-17T07:31:07.1367798Z Please avoid running ``setup.py`` directly. 2025-07-17T07:31:07.1368211Z Instead, use pypa/build, pypa/installer or other 2025-07-17T07:31:07.1368590Z standards-based tools. 2025-07-17T07:31:07.1368789Z 2025-07-17T07:31:07.1369017Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-07-17T07:31:07.1369507Z or your builds will no longer be supported. 2025-07-17T07:31:07.1369754Z 2025-07-17T07:31:07.1370062Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-07-17T07:31:07.1370597Z ******************************************************************************** 2025-07-17T07:31:07.1370854Z 2025-07-17T07:31:07.1370933Z !! 2025-07-17T07:31:07.1371155Z self.initialize_options() 2025-07-17T07:31:07.1500290Z running build 2025-07-17T07:31:07.1500541Z running build_ext 2025-07-17T07:31:07.1830549Z building 'no_python_abi_suffix_test' extension 2025-07-17T07:31:07.2426499Z ninja: no work to do. 2025-07-17T07:31:07.2471552Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_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 -shared -Wl,--allow-shlib-undefined -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,--allow-shlib-undefined -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 2025-07-17T07:31:07.3464400Z running install_lib 2025-07-17T07:31:07.3556399Z 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 2025-07-17T07:31:07.3561558Z running install_egg_info 2025-07-17T07:31:07.3755489Z running egg_info 2025-07-17T07:31:07.3833245Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2025-07-17T07:31:07.3836658Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2025-07-17T07:31:07.3849655Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2025-07-17T07:31:07.3939969Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T07:31:07.3947723Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-07-17T07:31:07.3958869Z 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) 2025-07-17T07:31:07.3960221Z 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 2025-07-17T07:31:07.3965603Z running install_scripts 2025-07-17T07:31:07.7488979Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:07.7492104Z 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'] ... [2025-07-17 07:31:07.748947] 2025-07-17T07:31:10.7677462Z 2025-07-17T07:31:10.7678623Z 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_a76ed50a3937dcfe_.log 2025-07-17T07:31:10.7693994Z Running 21 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_no_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library 2025-07-17T07:31:10.7701943Z 2025-07-17T07:31:10.7702206Z Running inductor/test_cudagraph_trees 1/1 ... [2025-07-17 07:31:10.768041] 2025-07-17T07:31:10.7702681Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:10.7704004Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cudagraph_trees.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:31:10.768332] 2025-07-17T07:31:16.9112468Z 2025-07-17T07:31:16.9113457Z inductor/test_cudagraph_trees 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudagraph_trees_1.1_f80b2c2782ea242a_.log 2025-07-17T07:31:16.9114483Z 2025-07-17T07:31:16.9115999Z Running dynamo/test_activation_checkpointing 1/1 ... [2025-07-17 07:31:16.911455] 2025-07-17T07:31:16.9116521Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:16.9119483Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:31:16.911752] 2025-07-17T07:31:23.0757537Z 2025-07-17T07:31:23.0758586Z dynamo/test_activation_checkpointing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_activation_checkpointing_1.1_03e905170a42a2c9_.log 2025-07-17T07:31:23.0759730Z Running 0 items in this shard: 2025-07-17T07:31:23.0759924Z 2025-07-17T07:31:23.0761583Z Running inductor/test_torchinductor_codegen_dynamic_shapes 2/3 ... [2025-07-17 07:31:23.075996] 2025-07-17T07:31:23.0762233Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:23.0764942Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'serial', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:31:23.076294] 2025-07-17T07:31:29.9495099Z 2025-07-17T07:31:29.9496336Z inductor/test_torchinductor_codegen_dynamic_shapes 2/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_2.3_082f3f8549003f5e_.log 2025-07-17T07:31:29.9497427Z Running 0 items in this shard: 2025-07-17T07:31:29.9497644Z 2025-07-17T07:31:29.9498977Z Running functorch/test_aotdispatch 5/6 ... [2025-07-17 07:31:29.949745] 2025-07-17T07:31:29.9499444Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:29.9503015Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'serial', '--shard-id=5', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:31:29.950078] 2025-07-17T07:31:34.7202183Z 2025-07-17T07:31:34.7203184Z functorch/test_aotdispatch 5/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_5.6_5468bdd23c6e1802_.log 2025-07-17T07:31:34.7204056Z Running 0 items in this shard: 2025-07-17T07:31:34.7204251Z 2025-07-17T07:31:34.7206225Z Running functorch/test_aotdispatch 6/6 ... [2025-07-17 07:31:34.720471] 2025-07-17T07:31:34.7206721Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:34.7209962Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:31:34.720786] 2025-07-17T07:31:39.4910308Z 2025-07-17T07:31:39.4911388Z functorch/test_aotdispatch 6/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_6.6_2dc7f5752ee47fa1_.log 2025-07-17T07:31:39.4912269Z Running 0 items in this shard: 2025-07-17T07:31:39.4912466Z 2025-07-17T07:31:39.4914358Z Running test_foreach 1/1 ... [2025-07-17 07:31:39.491281] 2025-07-17T07:31:39.4914747Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:39.4918411Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_foreach.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'] ... [2025-07-17 07:31:39.491612] 2025-07-17T07:31:47.4160668Z 2025-07-17T07:31:47.4161555Z test_foreach 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_foreach_1.1_cd4d3f2e3bcfc8bc_.log 2025-07-17T07:31:47.4162522Z Running 0 items in this shard: 2025-07-17T07:31:47.4162730Z 2025-07-17T07:31:47.4164663Z Running dynamo/cpython/3_13/test_generator_stop 1/1 ... [2025-07-17 07:31:47.416301] 2025-07-17T07:31:47.4165179Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:47.4168411Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_generator_stop.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'] ... [2025-07-17 07:31:47.416637] 2025-07-17T07:31:50.4349459Z 2025-07-17T07:31:50.4351078Z dynamo/cpython/3_13/test_generator_stop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_generator_stop_1.1_bafafff56cfd99d2_.log 2025-07-17T07:31:50.4353029Z Running 0 items in this shard: 2025-07-17T07:31:50.4353314Z 2025-07-17T07:31:50.4354992Z Running dynamo/cpython/3_13/test_exception_variations 1/1 ... [2025-07-17 07:31:50.435326] 2025-07-17T07:31:50.4356025Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:50.4360944Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_exception_variations.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'] ... [2025-07-17 07:31:50.435776] 2025-07-17T07:31:53.4540539Z 2025-07-17T07:31:53.4541714Z dynamo/cpython/3_13/test_exception_variations 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_exception_variations_1.1_5cfa6c53d7c46c40_.log 2025-07-17T07:31:53.4542774Z Running 0 items in this shard: 2025-07-17T07:31:53.4542978Z 2025-07-17T07:31:53.4544242Z Running dynamo/cpython/3_13/test_int_literal 1/1 ... [2025-07-17 07:31:53.454269] 2025-07-17T07:31:53.4544738Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:53.4547973Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_int_literal.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'] ... [2025-07-17 07:31:53.454606] 2025-07-17T07:31:56.4226633Z 2025-07-17T07:31:56.4227686Z dynamo/cpython/3_13/test_int_literal 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_int_literal_1.1_5b3a41bc0ea10e4d_.log 2025-07-17T07:31:56.4228626Z Running 0 items in this shard: 2025-07-17T07:31:56.4228832Z 2025-07-17T07:31:56.4230420Z Running dynamo/cpython/3_13/test_with 1/1 ... [2025-07-17 07:31:56.422894] 2025-07-17T07:31:56.4230899Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:56.4234245Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_with.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'] ... [2025-07-17 07:31:56.423222] 2025-07-17T07:31:59.3913083Z 2025-07-17T07:31:59.3914060Z dynamo/cpython/3_13/test_with 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_with_1.1_1a4a891b65e4e0ff_.log 2025-07-17T07:31:59.3914942Z Running 0 items in this shard: 2025-07-17T07:31:59.3915149Z 2025-07-17T07:31:59.3916932Z Running dynamo/cpython/3_13/test_numeric_tower 1/1 ... [2025-07-17 07:31:59.391541] 2025-07-17T07:31:59.3917439Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:31:59.3920662Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_numeric_tower.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'] ... [2025-07-17 07:31:59.391854] 2025-07-17T07:32:02.4601015Z 2025-07-17T07:32:02.4602265Z dynamo/cpython/3_13/test_numeric_tower 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_numeric_tower_1.1_1224d5ba65512e74_.log 2025-07-17T07:32:02.4603222Z Running 0 items in this shard: 2025-07-17T07:32:02.4603431Z 2025-07-17T07:32:02.4605084Z Running dynamo/cpython/3_13/test_ordered_dict 1/1 ... [2025-07-17 07:32:02.460344] 2025-07-17T07:32:02.4605589Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:02.4608912Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_ordered_dict.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'] ... [2025-07-17 07:32:02.460674] 2025-07-17T07:32:05.4788122Z 2025-07-17T07:32:05.4789169Z dynamo/cpython/3_13/test_ordered_dict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_ordered_dict_1.1_0a857ec78256266f_.log 2025-07-17T07:32:05.4790124Z Running 0 items in this shard: 2025-07-17T07:32:05.4790563Z 2025-07-17T07:32:05.4792052Z Running dynamo/cpython/3_13/test_baseexception 1/1 ... [2025-07-17 07:32:05.479005] 2025-07-17T07:32:05.4792592Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:05.4795613Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_baseexception.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'] ... [2025-07-17 07:32:05.479341] 2025-07-17T07:32:08.4476131Z 2025-07-17T07:32:08.4477236Z dynamo/cpython/3_13/test_baseexception 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_baseexception_1.1_9d6a1d65774b3c67_.log 2025-07-17T07:32:08.4478210Z Running 0 items in this shard: 2025-07-17T07:32:08.4478404Z 2025-07-17T07:32:08.4479595Z Running dynamo/cpython/3_13/test_int 1/1 ... [2025-07-17 07:32:08.447800] 2025-07-17T07:32:08.4480063Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:08.4483338Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_int.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'] ... [2025-07-17 07:32:08.448122] 2025-07-17T07:32:11.4162033Z 2025-07-17T07:32:11.4163030Z dynamo/cpython/3_13/test_int 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_int_1.1_bd82eee3e0e79f5e_.log 2025-07-17T07:32:11.4163904Z Running 0 items in this shard: 2025-07-17T07:32:11.4164110Z 2025-07-17T07:32:11.4166065Z Running dynamo/cpython/3_13/test_itertools 1/1 ... [2025-07-17 07:32:11.416456] 2025-07-17T07:32:11.4166568Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:11.4169904Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_itertools.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'] ... [2025-07-17 07:32:11.416787] 2025-07-17T07:32:14.4349357Z 2025-07-17T07:32:14.4350390Z dynamo/cpython/3_13/test_itertools 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_itertools_1.1_b45460c921c4ea0f_.log 2025-07-17T07:32:14.4351328Z Running 0 items in this shard: 2025-07-17T07:32:14.4351523Z 2025-07-17T07:32:14.4353433Z Running dynamo/cpython/3_13/test_userlist 1/1 ... [2025-07-17 07:32:14.435180] 2025-07-17T07:32:14.4353924Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:14.4357171Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_userlist.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'] ... [2025-07-17 07:32:14.435490] 2025-07-17T07:32:17.3533808Z 2025-07-17T07:32:17.3534865Z dynamo/cpython/3_13/test_userlist 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_userlist_1.1_e67c9b345d79b738_.log 2025-07-17T07:32:17.3535854Z Running 0 items in this shard: 2025-07-17T07:32:17.3536048Z 2025-07-17T07:32:17.3537945Z Running dynamo/cpython/3_13/test_cmath 1/1 ... [2025-07-17 07:32:17.353644] 2025-07-17T07:32:17.3538419Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:17.3541612Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_cmath.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'] ... [2025-07-17 07:32:17.353954] 2025-07-17T07:32:20.3218983Z 2025-07-17T07:32:20.3219856Z dynamo/cpython/3_13/test_cmath 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_cmath_1.1_12f702ab1a3b1e17_.log 2025-07-17T07:32:20.3220907Z Running 0 items in this shard: 2025-07-17T07:32:20.3221102Z 2025-07-17T07:32:20.3223074Z Running dynamo/cpython/3_13/test_math 1/1 ... [2025-07-17 07:32:20.322156] 2025-07-17T07:32:20.3223529Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:20.3226646Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_math.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'] ... [2025-07-17 07:32:20.322467] 2025-07-17T07:32:23.2904307Z 2025-07-17T07:32:23.2905353Z dynamo/cpython/3_13/test_math 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_math_1.1_df7068236c1e6258_.log 2025-07-17T07:32:23.2906229Z Running 0 items in this shard: 2025-07-17T07:32:23.2906436Z 2025-07-17T07:32:23.2908302Z Running dynamo/cpython/3_13/test_operator 1/1 ... [2025-07-17 07:32:23.290661] 2025-07-17T07:32:23.2908801Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:23.2911955Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_operator.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'] ... [2025-07-17 07:32:23.290972] 2025-07-17T07:32:26.2589448Z 2025-07-17T07:32:26.2591044Z dynamo/cpython/3_13/test_operator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_operator_1.1_88f4c701b7b8a091_.log 2025-07-17T07:32:26.2592513Z Running 0 items in this shard: 2025-07-17T07:32:26.2592815Z 2025-07-17T07:32:26.2593619Z Running dynamo/cpython/3_13/test_userdict 1/1 ... [2025-07-17 07:32:26.259187] 2025-07-17T07:32:26.2594383Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:26.2597489Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_userdict.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'] ... [2025-07-17 07:32:26.259503] 2025-07-17T07:32:29.1774871Z 2025-07-17T07:32:29.1775899Z dynamo/cpython/3_13/test_userdict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_userdict_1.1_0c6807a8c614d49e_.log 2025-07-17T07:32:29.1776815Z Running 0 items in this shard: 2025-07-17T07:32:29.1777006Z 2025-07-17T07:32:29.1778720Z Running dynamo/cpython/3_13/test_set 1/1 ... [2025-07-17 07:32:29.177716] 2025-07-17T07:32:29.1779410Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:29.1782237Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_set.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'] ... [2025-07-17 07:32:29.178026] 2025-07-17T07:32:32.1961009Z 2025-07-17T07:32:32.1961968Z dynamo/cpython/3_13/test_set 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_set_1.1_6468b6920c642f00_.log 2025-07-17T07:32:32.1962832Z Running 0 items in this shard: 2025-07-17T07:32:32.1963027Z 2025-07-17T07:32:32.1964977Z Running dynamo/cpython/3_13/test_iter 1/1 ... [2025-07-17 07:32:32.196344] 2025-07-17T07:32:32.1965454Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:32.1968548Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_iter.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'] ... [2025-07-17 07:32:32.196655] 2025-07-17T07:32:35.1645547Z 2025-07-17T07:32:35.1646729Z dynamo/cpython/3_13/test_iter 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_iter_1.1_65fd2df974f23bf1_.log 2025-07-17T07:32:35.1647644Z Running 0 items in this shard: 2025-07-17T07:32:35.1647835Z 2025-07-17T07:32:35.1649459Z Running dynamo/cpython/3_13/test_bool 1/1 ... [2025-07-17 07:32:35.164794] 2025-07-17T07:32:35.1649932Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:35.1653099Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_bool.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'] ... [2025-07-17 07:32:35.165111] 2025-07-17T07:32:38.1331763Z 2025-07-17T07:32:38.1332789Z dynamo/cpython/3_13/test_bool 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_bool_1.1_b565afd04f53cc7d_.log 2025-07-17T07:32:38.1333673Z Running 0 items in this shard: 2025-07-17T07:32:38.1333878Z 2025-07-17T07:32:38.1335696Z Running dynamo/cpython/3_13/test_sort 1/1 ... [2025-07-17 07:32:38.133419] 2025-07-17T07:32:38.1336180Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:38.1339371Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_sort.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'] ... [2025-07-17 07:32:38.133750] 2025-07-17T07:32:41.0516522Z 2025-07-17T07:32:41.0517496Z dynamo/cpython/3_13/test_sort 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_sort_1.1_d82ee51ad94e6e01_.log 2025-07-17T07:32:41.0518405Z Running 0 items in this shard: 2025-07-17T07:32:41.0518621Z 2025-07-17T07:32:41.0520397Z Running dynamo/cpython/3_13/test_float 1/1 ... [2025-07-17 07:32:41.051896] 2025-07-17T07:32:41.0520873Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:41.0523927Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_float.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'] ... [2025-07-17 07:32:41.052205] 2025-07-17T07:32:44.0201712Z 2025-07-17T07:32:44.0202702Z dynamo/cpython/3_13/test_float 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_float_1.1_bb1c70047ec56f6b_.log 2025-07-17T07:32:44.0203607Z Running 0 items in this shard: 2025-07-17T07:32:44.0203802Z 2025-07-17T07:32:44.0205612Z Running dynamo/cpython/3_13/test_dict 1/1 ... [2025-07-17 07:32:44.020419] 2025-07-17T07:32:44.0206283Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:44.0209118Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_dict.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'] ... [2025-07-17 07:32:44.020720] 2025-07-17T07:32:46.9886434Z 2025-07-17T07:32:46.9887433Z dynamo/cpython/3_13/test_dict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_dict_1.1_356f293c9a97307b_.log 2025-07-17T07:32:46.9888300Z Running 0 items in this shard: 2025-07-17T07:32:46.9888510Z 2025-07-17T07:32:46.9890372Z Running dynamo/cpython/3_13/test_complex 1/1 ... [2025-07-17 07:32:46.988883] 2025-07-17T07:32:46.9890858Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:46.9893743Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_complex.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'] ... [2025-07-17 07:32:46.989175] 2025-07-17T07:32:49.9571699Z 2025-07-17T07:32:49.9573043Z dynamo/cpython/3_13/test_complex 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_complex_1.1_8ef27eaa776479c9_.log 2025-07-17T07:32:49.9573971Z Running 0 items in this shard: 2025-07-17T07:32:49.9574165Z 2025-07-17T07:32:49.9575784Z Running dynamo/cpython/3_13/test_tuple 1/1 ... [2025-07-17 07:32:49.957424] 2025-07-17T07:32:49.9576263Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:49.9579259Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_tuple.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'] ... [2025-07-17 07:32:49.957735] 2025-07-17T07:32:52.8756957Z 2025-07-17T07:32:52.8757958Z dynamo/cpython/3_13/test_tuple 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_tuple_1.1_a472e6bd2751e24d_.log 2025-07-17T07:32:52.8758849Z Running 0 items in this shard: 2025-07-17T07:32:52.8759058Z 2025-07-17T07:32:52.8760528Z Running test_package 1/1 ... [2025-07-17 07:32:52.875901] 2025-07-17T07:32:52.8760930Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:52.8763713Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_package.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'] ... [2025-07-17 07:32:52.876190] 2025-07-17T07:32:56.0443867Z 2025-07-17T07:32:56.0444657Z test_package 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_package_1.1_2d19f1165868ac77_.log 2025-07-17T07:32:56.0445372Z Running 0 items in this shard: 2025-07-17T07:32:56.0445565Z 2025-07-17T07:32:56.0447743Z Running inductor/test_provenance_tracing 1/1 ... [2025-07-17 07:32:56.044632] 2025-07-17T07:32:56.0448244Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:32:56.0451237Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_provenance_tracing.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'] ... [2025-07-17 07:32:56.044924] 2025-07-17T07:33:02.3672428Z 2025-07-17T07:33:02.3673478Z inductor/test_provenance_tracing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_provenance_tracing_1.1_ca38ea0bff5d052a_.log 2025-07-17T07:33:02.3674403Z Running 0 items in this shard: 2025-07-17T07:33:02.3674598Z 2025-07-17T07:33:02.3675952Z Running test_decomp 1/10 ... [2025-07-17 07:33:02.367456] 2025-07-17T07:33:02.3676346Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:33:02.3680277Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'serial', '--shard-id=1', '--num-shards=10', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:33:02.367762] 2025-07-17T07:33:07.7888680Z 2025-07-17T07:33:07.7889536Z test_decomp 1/10 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_1.10_d8650e96fa9015c4_.log 2025-07-17T07:33:07.7890255Z Running 0 items in this shard: 2025-07-17T07:33:07.7890453Z 2025-07-17T07:33:07.7892572Z Running test_ops 2/6 ... [2025-07-17 07:33:07.789115] 2025-07-17T07:33:07.7892942Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:33:07.7896475Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'serial', '--shard-id=2', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:33:07.789455] 2025-07-17T07:33:20.7702926Z 2025-07-17T07:33:20.7703661Z test_ops 2/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_2.6_f15c049a72444b84_.log 2025-07-17T07:33:20.7704544Z Running 0 items in this shard: 2025-07-17T07:33:20.7704736Z 2025-07-17T07:33:20.7706596Z Running test_ops 3/6 ... [2025-07-17 07:33:20.770518] 2025-07-17T07:33:20.7707072Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:33:20.7710303Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'serial', '--shard-id=3', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:33:20.770828] 2025-07-17T07:33:33.8515661Z 2025-07-17T07:33:33.8516451Z test_ops 3/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_3.6_ff6079ba024fe6ec_.log 2025-07-17T07:33:33.8517128Z Running 0 items in this shard: 2025-07-17T07:33:33.8517334Z 2025-07-17T07:33:33.8519523Z Running test_ops 4/6 ... [2025-07-17 07:33:33.851803] 2025-07-17T07:33:33.8519918Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:33:33.8522975Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'serial', '--shard-id=4', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:33:33.852115] 2025-07-17T07:33:46.8827406Z 2025-07-17T07:33:46.8828234Z test_ops 4/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_4.6_4e83687c435ab873_.log 2025-07-17T07:33:46.8828921Z Running 0 items in this shard: 2025-07-17T07:33:46.8829126Z 2025-07-17T07:33:46.8831061Z Running inductor/test_ck_backend 1/1 ... [2025-07-17 07:33:46.882963] 2025-07-17T07:33:46.8831507Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:33:46.8834527Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_ck_backend.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:33:46.883264] 2025-07-17T07:33:52.9801016Z 2025-07-17T07:33:52.9801943Z inductor/test_ck_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_ck_backend_1.1_aa49294537eff3f2_.log 2025-07-17T07:33:52.9802675Z 2025-07-17T07:33:52.9804549Z Running typing/test_python_operators 1/1 ... [2025-07-17 07:33:52.980306] 2025-07-17T07:33:52.9805005Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:33:52.9808031Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'typing/test_python_operators.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'] ... [2025-07-17 07:33:52.980609] 2025-07-17T07:33:55.9486528Z 2025-07-17T07:33:55.9487690Z typing/test_python_operators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/typing.test_python_operators_1.1_220933ce269ec87b_.log 2025-07-17T07:33:55.9488578Z Running 0 items in this shard: 2025-07-17T07:33:55.9488783Z 2025-07-17T07:33:55.9490464Z Running torch_np/test_nep50_examples 1/1 ... [2025-07-17 07:33:55.948889] 2025-07-17T07:33:55.9491048Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:33:55.9493770Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_nep50_examples.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'] ... [2025-07-17 07:33:55.949186] 2025-07-17T07:33:59.0173793Z 2025-07-17T07:33:59.0174728Z torch_np/test_nep50_examples 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_nep50_examples_1.1_980281033bf938e3_.log 2025-07-17T07:33:59.0175677Z Running 0 items in this shard: 2025-07-17T07:33:59.0175870Z 2025-07-17T07:33:59.0177674Z Running inductor/test_best_config 1/1 ... [2025-07-17 07:33:59.017625] 2025-07-17T07:33:59.0178294Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:33:59.0181291Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_best_config.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:33:59.017926] 2025-07-17T07:34:05.1199705Z 2025-07-17T07:34:05.1200709Z inductor/test_best_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_best_config_1.1_76302403f4917152_.log 2025-07-17T07:34:05.1201449Z 2025-07-17T07:34:05.1203331Z Running inductor/test_compile_worker 1/1 ... [2025-07-17 07:34:05.120179] 2025-07-17T07:34:05.1203812Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:05.1206691Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_compile_worker.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'] ... [2025-07-17 07:34:05.120480] 2025-07-17T07:34:11.3427397Z 2025-07-17T07:34:11.3428405Z inductor/test_compile_worker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_compile_worker_1.1_b68393187a4ab398_.log 2025-07-17T07:34:11.3429282Z Running 0 items in this shard: 2025-07-17T07:34:11.3429479Z 2025-07-17T07:34:11.3430979Z Running test_ops_jit 1/2 ... [2025-07-17 07:34:11.342964] 2025-07-17T07:34:11.3431386Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:11.3434657Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_jit.py', '-m', 'serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:34:11.343287] 2025-07-17T07:34:15.4627257Z 2025-07-17T07:34:15.4628121Z test_ops_jit 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_jit_1.2_c1c6a2e40e05df2f_.log 2025-07-17T07:34:15.4628847Z Running 0 items in this shard: 2025-07-17T07:34:15.4629054Z 2025-07-17T07:34:15.4630872Z Running test_ops_jit 2/2 ... [2025-07-17 07:34:15.462949] 2025-07-17T07:34:15.4631282Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:15.4634203Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_jit.py', '-m', 'serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:34:15.463242] 2025-07-17T07:34:19.4825631Z 2025-07-17T07:34:19.4826467Z test_ops_jit 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_jit_2.2_c99d2278cbc513fe_.log 2025-07-17T07:34:19.4827167Z Running 0 items in this shard: 2025-07-17T07:34:19.4827376Z 2025-07-17T07:34:19.4829380Z Running dynamo/test_skip_guard_eval_unsafe 1/1 ... [2025-07-17 07:34:19.482776] 2025-07-17T07:34:19.4829888Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:19.4832565Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_skip_guard_eval_unsafe.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'] ... [2025-07-17 07:34:19.483066] 2025-07-17T07:34:22.6011693Z 2025-07-17T07:34:22.6012703Z dynamo/test_skip_guard_eval_unsafe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_guard_eval_unsafe_1.1_089158bac891f4af_.log 2025-07-17T07:34:22.6013617Z Running 0 items in this shard: 2025-07-17T07:34:22.6013810Z 2025-07-17T07:34:22.6015467Z Running functorch/test_ac_logging 1/1 ... [2025-07-17 07:34:22.601401] 2025-07-17T07:34:22.6015926Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:22.6018892Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac_logging.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:34:22.601702] 2025-07-17T07:34:25.5696452Z 2025-07-17T07:34:25.5697653Z functorch/test_ac_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_logging_1.1_1170c1aa486fd249_.log 2025-07-17T07:34:25.5698525Z Running 0 items in this shard: 2025-07-17T07:34:25.5698716Z 2025-07-17T07:34:25.5700355Z Running test_custom_ops 1/1 ... [2025-07-17 07:34:25.569892] 2025-07-17T07:34:25.5700756Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:25.5703703Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_custom_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'] ... [2025-07-17 07:34:25.570189] 2025-07-17T07:34:29.3892426Z 2025-07-17T07:34:29.3893353Z test_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_custom_ops_1.1_a5ceaae88b267d83_.log 2025-07-17T07:34:29.3894146Z Running 0 items in this shard: 2025-07-17T07:34:29.3894365Z 2025-07-17T07:34:29.3897920Z Running test_ao_sparsity 1/1 ... [2025-07-17 07:34:29.389619] 2025-07-17T07:34:29.3898377Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:29.3902034Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ao_sparsity.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'] ... [2025-07-17 07:34:29.389993] 2025-07-17T07:34:32.7084681Z 2025-07-17T07:34:32.7085538Z test_ao_sparsity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ao_sparsity_1.1_88e11368737c0b14_.log 2025-07-17T07:34:32.7086307Z Running 0 items in this shard: 2025-07-17T07:34:32.7086498Z 2025-07-17T07:34:32.7088277Z Running torch_np/test_binary_ufuncs 1/1 ... [2025-07-17 07:34:32.708678] 2025-07-17T07:34:32.7088790Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:32.7091696Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_binary_ufuncs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:34:32.708971] 2025-07-17T07:34:35.7771452Z 2025-07-17T07:34:35.7772536Z torch_np/test_binary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_binary_ufuncs_1.1_fb6fb79b70e203df_.log 2025-07-17T07:34:35.7773391Z Running 0 items in this shard: 2025-07-17T07:34:35.7773580Z 2025-07-17T07:34:35.7775357Z Running test_hop_infra 1/1 ... [2025-07-17 07:34:35.777389] 2025-07-17T07:34:35.7775763Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:35.7779045Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hop_infra.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'] ... [2025-07-17 07:34:35.777694] 2025-07-17T07:34:39.2463392Z 2025-07-17T07:34:39.2464339Z test_hop_infra 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hop_infra_1.1_f3313c61ff4e4080_.log 2025-07-17T07:34:39.2465079Z Running 0 items in this shard: 2025-07-17T07:34:39.2465270Z 2025-07-17T07:34:39.2467107Z Running torch_np/test_unary_ufuncs 1/1 ... [2025-07-17 07:34:39.246567] 2025-07-17T07:34:39.2467560Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:39.2470501Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_unary_ufuncs.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:34:39.246857] 2025-07-17T07:34:42.2148724Z 2025-07-17T07:34:42.2149637Z torch_np/test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_unary_ufuncs_1.1_b701ee2d9b3f65dc_.log 2025-07-17T07:34:42.2150499Z Running 0 items in this shard: 2025-07-17T07:34:42.2150910Z 2025-07-17T07:34:42.2152416Z Running dynamo/test_nops 1/1 ... [2025-07-17 07:34:42.215096] 2025-07-17T07:34:42.2152891Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:42.2155727Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:34:42.215384] 2025-07-17T07:34:45.3335359Z 2025-07-17T07:34:45.3336177Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_fa31827bbe281b51_.log 2025-07-17T07:34:45.3336940Z Running 0 items in this shard: 2025-07-17T07:34:45.3337139Z 2025-07-17T07:34:45.3339226Z Running inductor/test_move_constructors_to_cuda 1/1 ... [2025-07-17 07:34:45.333775] 2025-07-17T07:34:45.3339743Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:45.3342765Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_move_constructors_to_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:34:45.334068] 2025-07-17T07:34:51.3684040Z 2025-07-17T07:34:51.3685076Z inductor/test_move_constructors_to_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_move_constructors_to_cuda_1.1_c3c50844a7aa99d5_.log 2025-07-17T07:34:51.3685937Z 2025-07-17T07:34:51.3687651Z Running backends/xeon/test_launch 1/1 ... [2025-07-17 07:34:51.368616] 2025-07-17T07:34:51.3688137Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:51.3691081Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'backends/xeon/test_launch.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'] ... [2025-07-17 07:34:51.368910] 2025-07-17T07:34:54.3369352Z 2025-07-17T07:34:54.3370271Z backends/xeon/test_launch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/backends.xeon.test_launch_1.1_8575994790fb0779_.log 2025-07-17T07:34:54.3371119Z Running 0 items in this shard: 2025-07-17T07:34:54.3371334Z 2025-07-17T07:34:54.3373300Z Running inductor/test_triton_kernels 1/1 ... [2025-07-17 07:34:54.337159] 2025-07-17T07:34:54.3373775Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:34:54.3377159Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_triton_kernels.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'] ... [2025-07-17 07:34:54.337480] 2025-07-17T07:35:00.6096926Z 2025-07-17T07:35:00.6097852Z inductor/test_triton_kernels 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_triton_kernels_1.1_fa441d5e541a0268_.log 2025-07-17T07:35:00.6098971Z Running 0 items in this shard: 2025-07-17T07:35:00.6099162Z 2025-07-17T07:35:00.6100844Z Running inductor/test_async_compile 1/1 ... [2025-07-17 07:35:00.609939] 2025-07-17T07:35:00.6101293Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:35:00.6104412Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_async_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'] ... [2025-07-17 07:35:00.610248] 2025-07-17T07:35:06.8825711Z 2025-07-17T07:35:06.8826763Z inductor/test_async_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_async_compile_1.1_af01d1a8ea6d324b_.log 2025-07-17T07:35:06.8828356Z Running 0 items in this shard: 2025-07-17T07:35:06.8828568Z 2025-07-17T07:35:06.8830010Z Running torch_np/numpy_tests/linalg/test_linalg 1/1 ... [2025-07-17 07:35:06.882805] 2025-07-17T07:35:06.8830522Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:35:06.8833744Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/linalg/test_linalg.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'] ... [2025-07-17 07:35:06.883134] 2025-07-17T07:35:09.9011818Z 2025-07-17T07:35:09.9012905Z torch_np/numpy_tests/linalg/test_linalg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.linalg.test_linalg_1.1_fb04a90c90ae3350_.log 2025-07-17T07:35:09.9013879Z Running 0 items in this shard: 2025-07-17T07:35:09.9014083Z 2025-07-17T07:35:09.9015871Z Running dynamo/test_skip_non_tensor 1/1 ... [2025-07-17 07:35:09.901414] 2025-07-17T07:35:09.9016317Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:35:09.9019682Z 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'] ... [2025-07-17 07:35:09.901754] 2025-07-17T07:35:13.0200214Z 2025-07-17T07:35:13.0201486Z 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_fb7b8f5a6f386983_.log 2025-07-17T07:35:13.0202344Z Running 0 items in this shard: 2025-07-17T07:35:13.0202551Z 2025-07-17T07:35:13.0204420Z Running dynamo/test_interop 1/1 ... [2025-07-17 07:35:13.020274] 2025-07-17T07:35:13.0205117Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:35:13.0208452Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_interop.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:35:13.020604] 2025-07-17T07:35:16.1388483Z 2025-07-17T07:35:16.1389364Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_d8ce139c1413c031_.log 2025-07-17T07:35:16.1390164Z Running 0 items in this shard: 2025-07-17T07:35:16.1390356Z 2025-07-17T07:35:16.1393365Z Running inductor/test_torchinductor 1/2 ... [2025-07-17 07:35:16.139104] 2025-07-17T07:35:16.1393868Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:35:16.1397265Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor.py', '-m', 'serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:35:16.139443] 2025-07-17T07:35:40.3849745Z 2025-07-17T07:35:40.3850684Z inductor/test_torchinductor 1/2 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_1.2_dd875cfd64f1184a_.log 2025-07-17T07:35:40.3852083Z Running 1 items in this shard: test/inductor/test_torchinductor.py::CpuTests::test_large_block_sizes_cpu 2025-07-17T07:35:40.3852641Z 2025-07-17T07:35:40.3853897Z Running inductor/test_metrics 1/1 ... [2025-07-17 07:35:40.385223] 2025-07-17T07:35:40.3854334Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:35:40.3857734Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_metrics.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:35:40.385565] 2025-07-17T07:35:46.4470609Z 2025-07-17T07:35:46.4471498Z inductor/test_metrics 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_metrics_1.1_2ee789a021bd1f26_.log 2025-07-17T07:35:46.4472539Z 2025-07-17T07:35:46.4474445Z Running inductor/test_torchinductor_opinfo 3/26 ... [2025-07-17 07:35:46.447280] 2025-07-17T07:35:46.4475064Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:35:46.4477880Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=3', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:35:46.447588] 2025-07-17T07:35:54.5722327Z 2025-07-17T07:35:54.5723351Z inductor/test_torchinductor_opinfo 3/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_3.26_363755da68b2faca_.log 2025-07-17T07:35:54.5724321Z Running 0 items in this shard: 2025-07-17T07:35:54.5724527Z 2025-07-17T07:35:54.5726909Z Running inductor/test_torchinductor_opinfo 5/26 ... [2025-07-17 07:35:54.572471] 2025-07-17T07:35:54.5727418Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:35:54.5729732Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=5', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:35:54.572777] 2025-07-17T07:36:02.7473504Z 2025-07-17T07:36:02.7474552Z inductor/test_torchinductor_opinfo 5/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_5.26_1875880c2aecbff8_.log 2025-07-17T07:36:02.7475497Z Running 0 items in this shard: 2025-07-17T07:36:02.7475695Z 2025-07-17T07:36:02.7477489Z Running inductor/test_torchinductor_opinfo 7/26 ... [2025-07-17 07:36:02.747580] 2025-07-17T07:36:02.7478010Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:02.7481120Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=7', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:36:02.747909] 2025-07-17T07:36:10.9225250Z 2025-07-17T07:36:10.9226345Z inductor/test_torchinductor_opinfo 7/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_7.26_b83c580d2c254d81_.log 2025-07-17T07:36:10.9227276Z Running 0 items in this shard: 2025-07-17T07:36:10.9227480Z 2025-07-17T07:36:10.9228696Z Running inductor/test_torchinductor_opinfo 18/26 ... [2025-07-17 07:36:10.922732] 2025-07-17T07:36:10.9229199Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:10.9232737Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=18', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:36:10.923050] 2025-07-17T07:36:19.0976764Z 2025-07-17T07:36:19.0977849Z inductor/test_torchinductor_opinfo 18/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_18.26_c1384fdad024a90c_.log 2025-07-17T07:36:19.0979005Z Running 0 items in this shard: 2025-07-17T07:36:19.0979198Z 2025-07-17T07:36:19.0980963Z Running inductor/test_torchinductor_opinfo 20/26 ... [2025-07-17 07:36:19.097931] 2025-07-17T07:36:19.0981477Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:19.0984716Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=20', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:36:19.098255] 2025-07-17T07:36:27.1725984Z 2025-07-17T07:36:27.1727023Z inductor/test_torchinductor_opinfo 20/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_20.26_7f7ce78209b1128d_.log 2025-07-17T07:36:27.1728178Z Running 0 items in this shard: 2025-07-17T07:36:27.1728401Z 2025-07-17T07:36:27.1729973Z Running inductor/test_torchinductor_opinfo 22/26 ... [2025-07-17 07:36:27.172813] 2025-07-17T07:36:27.1730483Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:27.1733240Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'serial', '--shard-id=22', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:36:27.173116] 2025-07-17T07:36:35.2994799Z 2025-07-17T07:36:35.2995896Z inductor/test_torchinductor_opinfo 22/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_22.26_df5695c3d3cd0cd1_.log 2025-07-17T07:36:35.2996838Z Running 0 items in this shard: 2025-07-17T07:36:35.2997045Z 2025-07-17T07:36:35.3000468Z Running inductor/test_cuda_repro 1/1 ... [2025-07-17 07:36:35.299833] 2025-07-17T07:36:35.3000928Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:35.3004074Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cuda_repro.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:36:35.300183] 2025-07-17T07:36:41.3519137Z 2025-07-17T07:36:41.3520218Z inductor/test_cuda_repro 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cuda_repro_1.1_ec6d36ca584a809c_.log 2025-07-17T07:36:41.3520941Z 2025-07-17T07:36:41.3523766Z Running inductor/test_helion_kernels 1/1 ... [2025-07-17 07:36:41.352217] 2025-07-17T07:36:41.3524264Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:41.3527398Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_helion_kernels.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'] ... [2025-07-17 07:36:41.352541] 2025-07-17T07:36:47.5246083Z 2025-07-17T07:36:47.5247063Z inductor/test_helion_kernels 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_helion_kernels_1.1_b2aa13d2b357ea08_.log 2025-07-17T07:36:47.5247947Z Running 0 items in this shard: 2025-07-17T07:36:47.5248140Z 2025-07-17T07:36:47.5249880Z Running torch_np/test_random 1/1 ... [2025-07-17 07:36:47.524834] 2025-07-17T07:36:47.5250296Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:47.5254677Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_random.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'] ... [2025-07-17 07:36:47.525181] 2025-07-17T07:36:50.4932455Z 2025-07-17T07:36:50.4933338Z torch_np/test_random 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_random_1.1_1176589d9159fe2a_.log 2025-07-17T07:36:50.4934669Z Running 0 items in this shard: 2025-07-17T07:36:50.4934862Z 2025-07-17T07:36:50.4936203Z Running test_namedtensor 1/1 ... [2025-07-17 07:36:50.493480] 2025-07-17T07:36:50.4936606Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:50.4940103Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtensor.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'] ... [2025-07-17 07:36:50.493807] 2025-07-17T07:36:53.6623234Z 2025-07-17T07:36:53.6624111Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_b0a14ac6b00790e2_.log 2025-07-17T07:36:53.6625124Z Running 0 items in this shard: 2025-07-17T07:36:53.6625321Z 2025-07-17T07:36:53.6627047Z Running export/test_package 1/1 ... [2025-07-17 07:36:53.662547] 2025-07-17T07:36:53.6627470Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:53.6630941Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_package.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'] ... [2025-07-17 07:36:53.662878] 2025-07-17T07:36:56.6811807Z 2025-07-17T07:36:56.6812718Z export/test_package 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_package_1.1_d68cfbfb6d220555_.log 2025-07-17T07:36:56.6813519Z Running 0 items in this shard: 2025-07-17T07:36:56.6813730Z 2025-07-17T07:36:56.6815728Z Running dynamo/test_config 1/1 ... [2025-07-17 07:36:56.681415] 2025-07-17T07:36:56.6816210Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:36:56.6819785Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_config.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:36:56.681759] 2025-07-17T07:37:00.0004179Z 2025-07-17T07:37:00.0005104Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_f8cfeb6efe6ff52f_.log 2025-07-17T07:37:00.0005899Z Running 0 items in this shard: 2025-07-17T07:37:00.0006091Z 2025-07-17T07:37:00.0007987Z Running inductor/test_codecache 1/1 ... [2025-07-17 07:37:00.000664] 2025-07-17T07:37:00.0008430Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:00.0011990Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_codecache.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'] ... [2025-07-17 07:37:00.000987] 2025-07-17T07:37:06.5740336Z 2025-07-17T07:37:06.5741329Z inductor/test_codecache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_codecache_1.1_6243ad9efe15864e_.log 2025-07-17T07:37:06.5742158Z Running 0 items in this shard: 2025-07-17T07:37:06.5742365Z 2025-07-17T07:37:06.5744792Z Running torch_np/numpy_tests/core/test_scalarinherit 1/1 ... [2025-07-17 07:37:06.574301] 2025-07-17T07:37:06.5745380Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:06.5748759Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarinherit.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'] ... [2025-07-17 07:37:06.574647] 2025-07-17T07:37:09.5929062Z 2025-07-17T07:37:09.5930187Z torch_np/numpy_tests/core/test_scalarinherit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarinherit_1.1_6aee5cdb2768c684_.log 2025-07-17T07:37:09.5931218Z Running 0 items in this shard: 2025-07-17T07:37:09.5931488Z 2025-07-17T07:37:09.5933237Z Running export/test_upgrader 1/1 ... [2025-07-17 07:37:09.593166] 2025-07-17T07:37:09.5933657Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:09.5937199Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_upgrader.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'] ... [2025-07-17 07:37:09.593525] 2025-07-17T07:37:12.6117790Z 2025-07-17T07:37:12.6119158Z export/test_upgrader 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_upgrader_1.1_647fa0116cf757d6_.log 2025-07-17T07:37:12.6120601Z Running 0 items in this shard: 2025-07-17T07:37:12.6121195Z 2025-07-17T07:37:12.6123765Z Running dynamo/test_compile 1/1 ... [2025-07-17 07:37:12.612199] 2025-07-17T07:37:12.6124424Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:12.6129155Z 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'] ... [2025-07-17 07:37:12.612595] 2025-07-17T07:37:15.7310302Z 2025-07-17T07:37:15.7311684Z dynamo/test_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compile_1.1_321bb8b3e3f7b47b_.log 2025-07-17T07:37:15.7313126Z Running 0 items in this shard: 2025-07-17T07:37:15.7313462Z 2025-07-17T07:37:15.7315569Z Running dynamo/test_after_aot 1/1 ... [2025-07-17 07:37:15.731380] 2025-07-17T07:37:15.7316229Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:15.7320862Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_after_aot.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'] ... [2025-07-17 07:37:15.731787] 2025-07-17T07:37:18.8501133Z 2025-07-17T07:37:18.8502119Z dynamo/test_after_aot 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_after_aot_1.1_d941c6dc3815138f_.log 2025-07-17T07:37:18.8502925Z Running 0 items in this shard: 2025-07-17T07:37:18.8503134Z 2025-07-17T07:37:18.8505386Z Running test_hub 1/1 ... [2025-07-17 07:37:18.850399] 2025-07-17T07:37:18.8505772Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:18.8509288Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hub.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:37:18.850751] 2025-07-17T07:37:21.8188718Z 2025-07-17T07:37:21.8189855Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_e7bc8e9befdac8d3_.log 2025-07-17T07:37:21.8191069Z Running 0 items in this shard: 2025-07-17T07:37:21.8191435Z 2025-07-17T07:37:21.8194514Z Running dynamo/test_autograd_function 1/1 ... [2025-07-17 07:37:21.819268] 2025-07-17T07:37:21.8195254Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:21.8199726Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_autograd_function.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:37:21.819667] 2025-07-17T07:37:28.2424672Z 2025-07-17T07:37:28.2426061Z dynamo/test_autograd_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_autograd_function_1.1_38fc7a7337107afe_.log 2025-07-17T07:37:28.2427027Z Running 0 items in this shard: 2025-07-17T07:37:28.2427237Z 2025-07-17T07:37:28.2427779Z Running export/test_db 1/1 ... [2025-07-17 07:37:28.242636] 2025-07-17T07:37:28.2428173Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:28.2431540Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_db.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'] ... [2025-07-17 07:37:28.242962] 2025-07-17T07:37:31.2611844Z 2025-07-17T07:37:31.2612614Z export/test_db 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_db_1.1_bf4c87220a516232_.log 2025-07-17T07:37:31.2613350Z Running 0 items in this shard: 2025-07-17T07:37:31.2613540Z 2025-07-17T07:37:31.2615785Z Running test_optim 1/1 ... [2025-07-17 07:37:31.261422] 2025-07-17T07:37:31.2616176Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:31.2619433Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', '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'] ... [2025-07-17 07:37:31.261748] 2025-07-17T07:37:35.4313619Z 2025-07-17T07:37:35.4314661Z test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_optim_1.1_79a5e52b2d3e80f5_.log 2025-07-17T07:37:35.4315368Z Running 0 items in this shard: 2025-07-17T07:37:35.4315577Z 2025-07-17T07:37:35.4317568Z Running inductor/test_cooperative_reductions 1/1 ... [2025-07-17 07:37:35.431601] 2025-07-17T07:37:35.4318078Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:35.4321609Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cooperative_reductions.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:37:35.431948] 2025-07-17T07:37:42.0611993Z 2025-07-17T07:37:42.0613059Z inductor/test_cooperative_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cooperative_reductions_1.1_5316f10e5dd7ffc6_.log 2025-07-17T07:37:42.0613936Z 2025-07-17T07:37:42.0615576Z Running inductor/test_cpu_select_algorithm 1/16 ... [2025-07-17 07:37:42.061402] 2025-07-17T07:37:42.0616071Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:42.0619212Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=1', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:37:42.061725] 2025-07-17T07:37:49.2892438Z 2025-07-17T07:37:49.2893485Z inductor/test_cpu_select_algorithm 1/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_1.16_1a21d9f5d898493c_.log 2025-07-17T07:37:49.2894441Z Running 0 items in this shard: 2025-07-17T07:37:49.2894635Z 2025-07-17T07:37:49.2896708Z Running inductor/test_cpu_select_algorithm 5/16 ... [2025-07-17 07:37:49.289489] 2025-07-17T07:37:49.2897216Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:49.2900195Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=5', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:37:49.289810] 2025-07-17T07:37:56.2129002Z 2025-07-17T07:37:56.2130080Z inductor/test_cpu_select_algorithm 5/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_5.16_305db00b2c6ad296_.log 2025-07-17T07:37:56.2131028Z Running 0 items in this shard: 2025-07-17T07:37:56.2131226Z 2025-07-17T07:37:56.2133105Z Running inductor/test_cpu_select_algorithm 6/16 ... [2025-07-17 07:37:56.213132] 2025-07-17T07:37:56.2133596Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:37:56.2137034Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=6', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:37:56.213492] 2025-07-17T07:38:03.1866544Z 2025-07-17T07:38:03.1867583Z inductor/test_cpu_select_algorithm 6/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_6.16_50e7d4120a4e86a4_.log 2025-07-17T07:38:03.1868516Z Running 0 items in this shard: 2025-07-17T07:38:03.1868709Z 2025-07-17T07:38:03.1870434Z Running inductor/test_cpu_select_algorithm 16/16 ... [2025-07-17 07:38:03.186886] 2025-07-17T07:38:03.1870937Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:03.1874253Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'serial', '--shard-id=16', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:38:03.187219] 2025-07-17T07:38:10.1102887Z 2025-07-17T07:38:10.1104169Z inductor/test_cpu_select_algorithm 16/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_16.16_eee68112f60d3b42_.log 2025-07-17T07:38:10.1105113Z Running 0 items in this shard: 2025-07-17T07:38:10.1105317Z 2025-07-17T07:38:10.1106843Z Running inductor/test_halide 1/1 ... [2025-07-17 07:38:10.110533] 2025-07-17T07:38:10.1107282Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:10.1110958Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_halide.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'] ... [2025-07-17 07:38:10.110836] 2025-07-17T07:38:17.3685773Z 2025-07-17T07:38:17.3686702Z inductor/test_halide 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_halide_1.1_4b201963d192cbdd_.log 2025-07-17T07:38:17.3687410Z 2025-07-17T07:38:17.3689099Z Running inductor/test_inplacing_pass 1/1 ... [2025-07-17 07:38:17.368762] 2025-07-17T07:38:17.3689574Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:17.3692724Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inplacing_pass.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'] ... [2025-07-17 07:38:17.369074] 2025-07-17T07:38:24.2904566Z 2025-07-17T07:38:24.2905567Z inductor/test_inplacing_pass 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inplacing_pass_1.1_a808cb772265f715_.log 2025-07-17T07:38:24.2906364Z 2025-07-17T07:38:24.2908192Z Running inductor/test_perf 1/1 ... [2025-07-17 07:38:24.290661] 2025-07-17T07:38:24.2908675Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:24.2911889Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_perf.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'] ... [2025-07-17 07:38:24.290975] 2025-07-17T07:38:30.4546431Z 2025-07-17T07:38:30.4547361Z inductor/test_perf 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_perf_1.1_0e7226422af8a166_.log 2025-07-17T07:38:30.4548084Z 2025-07-17T07:38:30.4550195Z Running test_legacy_vmap 1/1 ... [2025-07-17 07:38:30.454883] 2025-07-17T07:38:30.4550675Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:30.4554275Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_legacy_vmap.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:38:30.455217] 2025-07-17T07:38:33.4733106Z 2025-07-17T07:38:33.4734681Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_452edde42aca772f_.log 2025-07-17T07:38:33.4736087Z Running 0 items in this shard: 2025-07-17T07:38:33.4736425Z 2025-07-17T07:38:33.4738687Z Running nn/test_multihead_attention 1/1 ... [2025-07-17 07:38:33.473675] 2025-07-17T07:38:33.4739410Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:33.4744202Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_multihead_attention.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'] ... [2025-07-17 07:38:33.474092] 2025-07-17T07:38:36.4421626Z 2025-07-17T07:38:36.4423342Z nn/test_multihead_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_multihead_attention_1.1_7b32db5108725ead_.log 2025-07-17T07:38:36.4424868Z Running 0 items in this shard: 2025-07-17T07:38:36.4425146Z 2025-07-17T07:38:36.4427430Z Running inductor/test_binary_folding 1/1 ... [2025-07-17 07:38:36.442510] 2025-07-17T07:38:36.4428190Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:36.4432597Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_binary_folding.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'] ... [2025-07-17 07:38:36.442941] 2025-07-17T07:38:43.2660717Z 2025-07-17T07:38:43.2661690Z inductor/test_binary_folding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_binary_folding_1.1_88851db27c76dbd2_.log 2025-07-17T07:38:43.2662567Z Running 0 items in this shard: 2025-07-17T07:38:43.2662770Z 2025-07-17T07:38:43.2664273Z Running inductor/test_torchinductor_codegen_config_overrides 1/1 ... [2025-07-17 07:38:43.266264] 2025-07-17T07:38:43.2664899Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:43.2668284Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_config_overrides.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'] ... [2025-07-17 07:38:43.266582] 2025-07-17T07:38:49.7394632Z 2025-07-17T07:38:49.7395878Z inductor/test_torchinductor_codegen_config_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_config_overrides_1.1_0e059f6c89bfa11b_.log 2025-07-17T07:38:49.7396973Z Running 0 items in this shard: 2025-07-17T07:38:49.7397162Z 2025-07-17T07:38:49.7398218Z Running torch_np/numpy_tests/core/test_numeric 1/1 ... [2025-07-17 07:38:49.739668] 2025-07-17T07:38:49.7398815Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:49.7401903Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numeric.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:38:49.739971] 2025-07-17T07:38:52.8582967Z 2025-07-17T07:38:52.8584081Z torch_np/numpy_tests/core/test_numeric 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_5f3308091057acb6_.log 2025-07-17T07:38:52.8585091Z Running 0 items in this shard: 2025-07-17T07:38:52.8585301Z 2025-07-17T07:38:52.8586686Z Running dynamo/test_backward_higher_order_ops 1/1 ... [2025-07-17 07:38:52.858509] 2025-07-17T07:38:52.8587250Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:52.8590586Z 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'] ... [2025-07-17 07:38:52.858815] 2025-07-17T07:38:56.0771535Z 2025-07-17T07:38:56.0772669Z 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_84f8aa0fb6ad8bb0_.log 2025-07-17T07:38:56.0773622Z Running 0 items in this shard: 2025-07-17T07:38:56.0773814Z 2025-07-17T07:38:56.0775675Z Running dynamo/test_optimizers 1/1 ... [2025-07-17 07:38:56.077410] 2025-07-17T07:38:56.0776167Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:56.0779378Z 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'] ... [2025-07-17 07:38:56.077725] 2025-07-17T07:38:59.2459972Z 2025-07-17T07:38:59.2460962Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_4482e503edeb404d_.log 2025-07-17T07:38:59.2462082Z Running 0 items in this shard: 2025-07-17T07:38:59.2462314Z 2025-07-17T07:38:59.2463535Z Running test_weak 1/1 ... [2025-07-17 07:38:59.246236] 2025-07-17T07:38:59.2463988Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:38:59.2467304Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_weak.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:38:59.246542] 2025-07-17T07:39:02.2146124Z 2025-07-17T07:39:02.2146987Z test_weak 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_weak_1.1_b50962d49cd76835_.log 2025-07-17T07:39:02.2147750Z Running 0 items in this shard: 2025-07-17T07:39:02.2147992Z 2025-07-17T07:39:02.2149712Z Running dynamo/test_graph_deduplication 1/1 ... [2025-07-17 07:39:02.214813] 2025-07-17T07:39:02.2150233Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:02.2153219Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_deduplication.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'] ... [2025-07-17 07:39:02.215115] 2025-07-17T07:39:05.3834348Z 2025-07-17T07:39:05.3835376Z dynamo/test_graph_deduplication 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_deduplication_1.1_397afce917523c9c_.log 2025-07-17T07:39:05.3836269Z Running 0 items in this shard: 2025-07-17T07:39:05.3836476Z 2025-07-17T07:39:05.3838129Z Running dynamo/test_sets 1/1 ... [2025-07-17 07:39:05.383653] 2025-07-17T07:39:05.3838599Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:05.3841674Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sets.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'] ... [2025-07-17 07:39:05.383957] 2025-07-17T07:39:08.5021622Z 2025-07-17T07:39:08.5022512Z dynamo/test_sets 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sets_1.1_f55547ec76a09f45_.log 2025-07-17T07:39:08.5023328Z Running 0 items in this shard: 2025-07-17T07:39:08.5023523Z 2025-07-17T07:39:08.5025399Z Running distributions/test_constraints 1/1 ... [2025-07-17 07:39:08.502387] 2025-07-17T07:39:08.5025874Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:08.5028996Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_constraints.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'] ... [2025-07-17 07:39:08.502685] 2025-07-17T07:39:11.4707511Z 2025-07-17T07:39:11.4708621Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_d279bbef179f8da8_.log 2025-07-17T07:39:11.4709785Z Running 0 items in this shard: 2025-07-17T07:39:11.4709996Z 2025-07-17T07:39:11.4711064Z Running test_tensorboard 1/1 ... [2025-07-17 07:39:11.470952] 2025-07-17T07:39:11.4711477Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:11.4714782Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorboard.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:39:11.471270] 2025-07-17T07:39:14.7897827Z 2025-07-17T07:39:14.7898649Z test_tensorboard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorboard_1.1_90c5602c2c23ab80_.log 2025-07-17T07:39:14.7899628Z Running 0 items in this shard: 2025-07-17T07:39:14.7899823Z 2025-07-17T07:39:14.7901600Z Running inductor/test_split_cat_fx_aten_passes 1/1 ... [2025-07-17 07:39:14.789999] 2025-07-17T07:39:14.7902105Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:14.7905110Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_split_cat_fx_aten_passes.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'] ... [2025-07-17 07:39:14.790315] 2025-07-17T07:39:21.0124862Z 2025-07-17T07:39:21.0125856Z inductor/test_split_cat_fx_aten_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_split_cat_fx_aten_passes_1.1_76ce2c347738984e_.log 2025-07-17T07:39:21.0126815Z Running 0 items in this shard: 2025-07-17T07:39:21.0127018Z 2025-07-17T07:39:21.0128957Z Running dynamo/test_structured_trace 1/1 ... [2025-07-17 07:39:21.012737] 2025-07-17T07:39:21.0129437Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:21.0132618Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_structured_trace.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:39:21.013055] 2025-07-17T07:39:27.2356807Z 2025-07-17T07:39:27.2357812Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_2d4379678a0c9668_.log 2025-07-17T07:39:27.2358676Z Running 0 items in this shard: 2025-07-17T07:39:27.2358886Z 2025-07-17T07:39:27.2360271Z Running profiler/test_cpp_thread 1/1 ... [2025-07-17 07:39:27.235876] 2025-07-17T07:39:27.2360746Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:27.2363710Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_cpp_thread.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'] ... [2025-07-17 07:39:27.236177] 2025-07-17T07:39:31.2556518Z 2025-07-17T07:39:31.2557464Z profiler/test_cpp_thread 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_cpp_thread_1.1_aea07d341ede28ce_.log 2025-07-17T07:39:31.2558313Z Running 0 items in this shard: 2025-07-17T07:39:31.2558507Z 2025-07-17T07:39:31.2559857Z Running dynamo/test_decorators 1/1 ... [2025-07-17 07:39:31.255848] 2025-07-17T07:39:31.2560355Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:31.2563724Z 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'] ... [2025-07-17 07:39:31.256160] 2025-07-17T07:39:34.3743068Z 2025-07-17T07:39:34.3743987Z dynamo/test_decorators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_decorators_1.1_a96b36a065b67c4d_.log 2025-07-17T07:39:34.3745060Z Running 0 items in this shard: 2025-07-17T07:39:34.3745256Z 2025-07-17T07:39:34.3746658Z Running dynamo/test_debug_utils 1/1 ... [2025-07-17 07:39:34.374526] 2025-07-17T07:39:34.3747090Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:34.3750035Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:39:34.374819] 2025-07-17T07:39:40.5475805Z 2025-07-17T07:39:40.5476619Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_58f74cdc4cd7b266_.log 2025-07-17T07:39:40.5477633Z Running 0 items in this shard: 2025-07-17T07:39:40.5477824Z 2025-07-17T07:39:40.5479479Z Running functorch/test_ac_knapsack 1/1 ... [2025-07-17 07:39:40.547789] 2025-07-17T07:39:40.5480078Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:40.5483434Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac_knapsack.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'] ... [2025-07-17 07:39:40.548131] 2025-07-17T07:39:43.6164733Z 2025-07-17T07:39:43.6165690Z functorch/test_ac_knapsack 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_knapsack_1.1_96205ba4d629779c_.log 2025-07-17T07:39:43.6166540Z Running 0 items in this shard: 2025-07-17T07:39:43.6166730Z 2025-07-17T07:39:43.6168146Z Running functorch/test_ac 1/1 ... [2025-07-17 07:39:43.616669] 2025-07-17T07:39:43.6168557Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:43.6172349Z 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'] ... [2025-07-17 07:39:43.617001] 2025-07-17T07:39:49.6623193Z 2025-07-17T07:39:49.6624085Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_c03f93469a7fe00c_.log 2025-07-17T07:39:49.6624728Z 2025-07-17T07:39:49.6627023Z Running dynamo/test_base_hop 1/1 ... [2025-07-17 07:39:49.662557] 2025-07-17T07:39:49.6627457Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:49.6631008Z 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'] ... [2025-07-17 07:39:49.662891] 2025-07-17T07:39:55.8851046Z 2025-07-17T07:39:55.8851942Z 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_ac81297c89669fc5_.log 2025-07-17T07:39:55.8852751Z Running 0 items in this shard: 2025-07-17T07:39:55.8852945Z 2025-07-17T07:39:55.8854755Z Running dynamo/test_modes 1/1 ... [2025-07-17 07:39:55.885334] 2025-07-17T07:39:55.8855160Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:39:55.8858398Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modes.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'] ... [2025-07-17 07:39:55.885657] 2025-07-17T07:40:02.1077861Z 2025-07-17T07:40:02.1079010Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_04349e278128d753_.log 2025-07-17T07:40:02.1079790Z Running 0 items in this shard: 2025-07-17T07:40:02.1080002Z 2025-07-17T07:40:02.1081996Z Running inductor/test_inductor_freezing 1/1 ... [2025-07-17 07:40:02.108049] 2025-07-17T07:40:02.1082664Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:02.1085814Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inductor_freezing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:40:02.108378] 2025-07-17T07:40:08.7310314Z 2025-07-17T07:40:08.7311868Z inductor/test_inductor_freezing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_freezing_1.1_9e76fba5ea29ecbe_.log 2025-07-17T07:40:08.7313508Z Running 0 items in this shard: 2025-07-17T07:40:08.7313777Z 2025-07-17T07:40:08.7316339Z Running dynamo/test_profiler 1/1 ... [2025-07-17 07:40:08.731382] 2025-07-17T07:40:08.7317273Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:08.7321057Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:40:08.731794] 2025-07-17T07:40:11.8500256Z 2025-07-17T07:40:11.8501188Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_a3464edbc29ec0d1_.log 2025-07-17T07:40:11.8501996Z Running 0 items in this shard: 2025-07-17T07:40:11.8502215Z 2025-07-17T07:40:11.8505091Z Running dynamo/test_sources 1/1 ... [2025-07-17 07:40:11.850352] 2025-07-17T07:40:11.8505764Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:11.8509131Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:40:11.850677] 2025-07-17T07:40:14.8186679Z 2025-07-17T07:40:14.8187840Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_b2aabfe10d662f65_.log 2025-07-17T07:40:14.8188634Z Running 0 items in this shard: 2025-07-17T07:40:14.8188826Z 2025-07-17T07:40:14.8190843Z Running inductor/test_config 1/1 ... [2025-07-17 07:40:14.818925] 2025-07-17T07:40:14.8191544Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:14.8194746Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_config.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:40:14.819242] 2025-07-17T07:40:21.0412186Z 2025-07-17T07:40:21.0413021Z inductor/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_config_1.1_91065b15eb8f15a2_.log 2025-07-17T07:40:21.0413831Z Running 0 items in this shard: 2025-07-17T07:40:21.0414029Z 2025-07-17T07:40:21.0416019Z Running test_datapipe 1/1 ... [2025-07-17 07:40:21.041463] 2025-07-17T07:40:21.0416416Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:21.0419951Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_datapipe.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'] ... [2025-07-17 07:40:21.041799] 2025-07-17T07:40:24.0598122Z 2025-07-17T07:40:24.0598956Z test_datapipe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_datapipe_1.1_ae67d5fc2349d770_.log 2025-07-17T07:40:24.0599695Z Running 0 items in this shard: 2025-07-17T07:40:24.0600092Z 2025-07-17T07:40:24.0602236Z Running functorch/test_logging 1/1 ... [2025-07-17 07:40:24.060066] 2025-07-17T07:40:24.0602683Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:24.0605730Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_logging.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:40:24.060386] 2025-07-17T07:40:27.1785917Z 2025-07-17T07:40:27.1786860Z functorch/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_logging_1.1_1a38062808df2d1a_.log 2025-07-17T07:40:27.1787691Z Running 0 items in this shard: 2025-07-17T07:40:27.1787885Z 2025-07-17T07:40:27.1789678Z Running dynamo/test_flat_apply 1/1 ... [2025-07-17 07:40:27.178824] 2025-07-17T07:40:27.1790119Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:27.1793580Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_flat_apply.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:40:27.179149] 2025-07-17T07:40:30.2973796Z 2025-07-17T07:40:30.2974608Z dynamo/test_flat_apply 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_flat_apply_1.1_438c34d1f5b9a200_.log 2025-07-17T07:40:30.2975486Z Running 0 items in this shard: 2025-07-17T07:40:30.2975679Z 2025-07-17T07:40:30.2977849Z Running torch_np/test_indexing 1/1 ... [2025-07-17 07:40:30.297637] 2025-07-17T07:40:30.2978271Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:30.2981516Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_indexing.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'] ... [2025-07-17 07:40:30.297955] 2025-07-17T07:40:33.2660004Z 2025-07-17T07:40:33.2660964Z torch_np/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_indexing_1.1_0c2c258888fd93e7_.log 2025-07-17T07:40:33.2661789Z Running 0 items in this shard: 2025-07-17T07:40:33.2661982Z 2025-07-17T07:40:33.2663754Z Running dynamo/test_deviceguard 1/1 ... [2025-07-17 07:40:33.266232] 2025-07-17T07:40:33.2664207Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:33.2667524Z 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'] ... [2025-07-17 07:40:33.266547] 2025-07-17T07:40:36.3847216Z 2025-07-17T07:40:36.3848153Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_ff4043272afcee63_.log 2025-07-17T07:40:36.3848988Z Running 0 items in this shard: 2025-07-17T07:40:36.3849205Z 2025-07-17T07:40:36.3850997Z Running test_content_store 1/1 ... [2025-07-17 07:40:36.384952] 2025-07-17T07:40:36.3851419Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:36.3854807Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_content_store.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'] ... [2025-07-17 07:40:36.385271] 2025-07-17T07:40:39.3532900Z 2025-07-17T07:40:39.3534099Z test_content_store 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_content_store_1.1_ffefbf25f78e2331_.log 2025-07-17T07:40:39.3535341Z Running 0 items in this shard: 2025-07-17T07:40:39.3535642Z 2025-07-17T07:40:39.3538962Z Running export/test_unflatten 1/1 ... [2025-07-17 07:40:39.353675] 2025-07-17T07:40:39.3539620Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:39.3542388Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_unflatten.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:40:39.354034] 2025-07-17T07:40:42.3219292Z 2025-07-17T07:40:42.3220184Z export/test_unflatten 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_unflatten_1.1_7d35bf2334b98064_.log 2025-07-17T07:40:42.3220988Z Running 0 items in this shard: 2025-07-17T07:40:42.3221198Z 2025-07-17T07:40:42.3222940Z Running lazy/test_step_closures 1/1 ... [2025-07-17 07:40:42.322151] 2025-07-17T07:40:42.3223381Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:42.3226274Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_step_closures.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:40:42.322441] 2025-07-17T07:40:45.2904491Z 2025-07-17T07:40:45.2905426Z lazy/test_step_closures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_step_closures_1.1_17b023ebb64e7301_.log 2025-07-17T07:40:45.2906566Z Running 0 items in this shard: 2025-07-17T07:40:45.2906766Z 2025-07-17T07:40:45.2907998Z Running inductor/test_fused_attention 1/1 ... [2025-07-17 07:40:45.290656] 2025-07-17T07:40:45.2908458Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:45.2911466Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fused_attention.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'] ... [2025-07-17 07:40:45.290953] 2025-07-17T07:40:51.4631312Z 2025-07-17T07:40:51.4632328Z inductor/test_fused_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fused_attention_1.1_822bba29acd97662_.log 2025-07-17T07:40:51.4633230Z Running 0 items in this shard: 2025-07-17T07:40:51.4633424Z 2025-07-17T07:40:51.4634957Z Running test_ops_gradients 1/2 ... [2025-07-17 07:40:51.463357] 2025-07-17T07:40:51.4635393Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:51.4638680Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_gradients.py', '-m', 'serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:40:51.463681] 2025-07-17T07:40:56.3341362Z 2025-07-17T07:40:56.3342241Z test_ops_gradients 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_gradients_1.2_c3d73467d19fc0b2_.log 2025-07-17T07:40:56.3343033Z Running 0 items in this shard: 2025-07-17T07:40:56.3343228Z 2025-07-17T07:40:56.3345294Z Running inductor/test_padding 1/1 ... [2025-07-17 07:40:56.334380] 2025-07-17T07:40:56.3345733Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:40:56.3348906Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_padding.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'] ... [2025-07-17 07:40:56.334696] 2025-07-17T07:41:02.4503757Z 2025-07-17T07:41:02.4504666Z inductor/test_padding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_padding_1.1_45e3c40dc73f4aab_.log 2025-07-17T07:41:02.4505381Z 2025-07-17T07:41:02.4507049Z Running dynamo/test_torchrec 1/1 ... [2025-07-17 07:41:02.450569] 2025-07-17T07:41:02.4507485Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:02.4511021Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_torchrec.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:41:02.450885] 2025-07-17T07:41:05.5389810Z 2025-07-17T07:41:05.5390698Z dynamo/test_torchrec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_torchrec_1.1_f6876b865d0d0777_.log 2025-07-17T07:41:05.5391730Z Running 0 items in this shard: 2025-07-17T07:41:05.5391923Z 2025-07-17T07:41:05.5393503Z Running torch_np/test_reductions 1/1 ... [2025-07-17 07:41:05.539204] 2025-07-17T07:41:05.5393935Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:05.5397127Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_reductions.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:41:05.539508] 2025-07-17T07:41:08.6076877Z 2025-07-17T07:41:08.6077868Z torch_np/test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_reductions_1.1_3b0e9c17d902e50a_.log 2025-07-17T07:41:08.6078933Z Running 0 items in this shard: 2025-07-17T07:41:08.6079126Z 2025-07-17T07:41:08.6080665Z Running profiler/test_profiler_tree 1/1 ... [2025-07-17 07:41:08.607925] 2025-07-17T07:41:08.6081227Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:08.6084292Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_profiler_tree.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'] ... [2025-07-17 07:41:08.608239] 2025-07-17T07:41:11.5262524Z 2025-07-17T07:41:11.5263486Z profiler/test_profiler_tree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_tree_1.1_36779647fd26548e_.log 2025-07-17T07:41:11.5264345Z Running 0 items in this shard: 2025-07-17T07:41:11.5264537Z 2025-07-17T07:41:11.5265565Z Running test_type_promotion 1/1 ... [2025-07-17 07:41:11.526422] 2025-07-17T07:41:11.5265998Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:11.5269188Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_type_promotion.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'] ... [2025-07-17 07:41:11.526736] 2025-07-17T07:41:14.5448499Z 2025-07-17T07:41:14.5449376Z test_type_promotion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_promotion_1.1_539e46dd7ccce21b_.log 2025-07-17T07:41:14.5450165Z Running 0 items in this shard: 2025-07-17T07:41:14.5450356Z 2025-07-17T07:41:14.5452167Z Running dynamo/test_python_autograd 1/1 ... [2025-07-17 07:41:14.545074] 2025-07-17T07:41:14.5452628Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:14.5456012Z 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'] ... [2025-07-17 07:41:14.545395] 2025-07-17T07:41:17.6637055Z 2025-07-17T07:41:17.6638038Z 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_986343e7502b1ab1_.log 2025-07-17T07:41:17.6638898Z Running 0 items in this shard: 2025-07-17T07:41:17.6639141Z 2025-07-17T07:41:17.6640204Z Running test_jit_autocast 1/1 ... [2025-07-17 07:41:17.663881] 2025-07-17T07:41:17.6640617Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:17.6644277Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_autocast.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'] ... [2025-07-17 07:41:17.664207] 2025-07-17T07:41:22.4846671Z 2025-07-17T07:41:22.4847541Z test_jit_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_autocast_1.1_8fb54fd869c556ed_.log 2025-07-17T07:41:22.4848728Z Running 0 items in this shard: 2025-07-17T07:41:22.4849200Z 2025-07-17T07:41:22.4850553Z Running profiler/test_record_function 1/1 ... [2025-07-17 07:41:22.484898] 2025-07-17T07:41:22.4851024Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:22.4854232Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_record_function.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:41:22.485224] 2025-07-17T07:41:25.4532553Z 2025-07-17T07:41:25.4533630Z profiler/test_record_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_record_function_1.1_d551ecd371fded29_.log 2025-07-17T07:41:25.4534601Z Running 0 items in this shard: 2025-07-17T07:41:25.4535103Z 2025-07-17T07:41:25.4535988Z Running test_prims 1/1 ... [2025-07-17 07:41:25.453468] 2025-07-17T07:41:25.4536380Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:25.4540322Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_prims.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'] ... [2025-07-17 07:41:25.453804] 2025-07-17T07:41:29.3730344Z 2025-07-17T07:41:29.3731193Z test_prims 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_prims_1.1_26f4335083c6771d_.log 2025-07-17T07:41:29.3731898Z Running 0 items in this shard: 2025-07-17T07:41:29.3732093Z 2025-07-17T07:41:29.3734077Z Running lazy/test_reuse_ir 1/1 ... [2025-07-17 07:41:29.373268] 2025-07-17T07:41:29.3734498Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:29.3737911Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_reuse_ir.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'] ... [2025-07-17 07:41:29.373602] 2025-07-17T07:41:32.3416451Z 2025-07-17T07:41:32.3417392Z lazy/test_reuse_ir 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_reuse_ir_1.1_f8d0d11fff9e2f44_.log 2025-07-17T07:41:32.3418240Z Running 0 items in this shard: 2025-07-17T07:41:32.3418480Z 2025-07-17T07:41:32.3420256Z Running profiler/test_execution_trace 1/1 ... [2025-07-17 07:41:32.341875] 2025-07-17T07:41:32.3420788Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:32.3424060Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_execution_trace.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:41:32.342194] 2025-07-17T07:41:35.3102457Z 2025-07-17T07:41:35.3103501Z profiler/test_execution_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_execution_trace_1.1_05ca4ff7e7954cb1_.log 2025-07-17T07:41:35.3104465Z Running 0 items in this shard: 2025-07-17T07:41:35.3104734Z 2025-07-17T07:41:35.3106475Z Running profiler/test_torch_tidy 1/1 ... [2025-07-17 07:41:35.310493] 2025-07-17T07:41:35.3106977Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:35.3110184Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_torch_tidy.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'] ... [2025-07-17 07:41:35.310804] 2025-07-17T07:41:38.2789368Z 2025-07-17T07:41:38.2790607Z profiler/test_torch_tidy 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_torch_tidy_1.1_36a352a02356b17b_.log 2025-07-17T07:41:38.2791537Z Running 0 items in this shard: 2025-07-17T07:41:38.2791731Z 2025-07-17T07:41:38.2793058Z Running test_rename_privateuse1_to_existing_device 1/1 ... [2025-07-17 07:41:38.279156] 2025-07-17T07:41:38.2793733Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:38.2796920Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_rename_privateuse1_to_existing_device.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'] ... [2025-07-17 07:41:38.279478] 2025-07-17T07:41:41.1849213Z 2025-07-17T07:41:41.1850289Z test_rename_privateuse1_to_existing_device 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_rename_privateuse1_to_existing_device_1.1_16ad904a5016a80c_.log 2025-07-17T07:41:41.1851269Z Running 0 items in this shard: 2025-07-17T07:41:41.1851462Z 2025-07-17T07:41:43.5812103Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:41:43.5813980Z import pkg_resources 2025-07-17T07:41:43.6214560Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:41:43.6216128Z import pkg_resources 2025-07-17T07:41:43.6285818Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:41:43.6287339Z import pkg_resources 2025-07-17T07:41:43.7047481Z Running inductor/test_cudagraph_trees 1/1 ... [2025-07-17 07:41:43.704429] 2025-07-17T07:41:43.7048009Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:43.7049963Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cudagraph_trees.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:41:43.704785] 2025-07-17T07:41:43.7462566Z Running dynamo/test_activation_checkpointing 1/1 ... [2025-07-17 07:41:43.745995] 2025-07-17T07:41:43.7463391Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:43.7466150Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:41:43.746339] 2025-07-17T07:41:43.7529461Z Running inductor/test_torchinductor_codegen_dynamic_shapes 2/3 ... [2025-07-17 07:41:43.752717] 2025-07-17T07:41:43.7530115Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:43.7532929Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_dynamic_shapes.py', '-m', 'not serial', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:41:43.753029] 2025-07-17T07:41:50.3424361Z 2025-07-17T07:41:50.3425425Z dynamo/test_activation_checkpointing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_activation_checkpointing_1.1_e11de999e0957f12_.log 2025-07-17T07:41:50.3426577Z Running 0 items in this shard: 2025-07-17T07:41:50.3426775Z 2025-07-17T07:41:51.6854623Z 2025-07-17T07:41:51.6855798Z inductor/test_cudagraph_trees 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cudagraph_trees_1.1_54867ab6d436d140_.log 2025-07-17T07:41:51.6856844Z 2025-07-17T07:41:53.5264215Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:41:53.5265777Z import pkg_resources 2025-07-17T07:41:53.6497433Z Running functorch/test_aotdispatch 5/6 ... [2025-07-17 07:41:53.649430] 2025-07-17T07:41:53.6497941Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:53.6500238Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'not serial', '--shard-id=5', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:41:53.649784] 2025-07-17T07:41:54.7471613Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:41:54.7473505Z import pkg_resources 2025-07-17T07:41:54.8698477Z Running functorch/test_aotdispatch 6/6 ... [2025-07-17 07:41:54.869540] 2025-07-17T07:41:54.8698979Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:41:54.8701255Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_aotdispatch.py', '-m', 'not serial', '--shard-id=6', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:41:54.869891] 2025-07-17T07:47:47.1055653Z 2025-07-17T07:47:47.1057447Z functorch/test_aotdispatch 5/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_5.6_ea64ed49aa435c09_.log 2025-07-17T07:47:47.1419159Z Running 334 items in this shard: test/functorch/test_aotdispatch.py::TestAOTAutograd::test_backward_mutation_metadata, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_custom_autograd, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_custom_tensor_metadata, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_dupe_arg, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_dupe_arg_torture, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_duplicated_arguments_on_tensor_overlap, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_hidden_from_autograd_aliasing, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_noncontiguous, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_output_view_metadata_mutate_multiple, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_list_codegen, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_multi_output, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_mutates_input_noncontiguous, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_nested_subclasses_non_nested_grad, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_output_aliases_intermediate_no_grad, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_view_and_inplace_view, test/functorch/test_aotdispatch.py::TestAOTExport::test_aot_export_predispatch_func_composite_implicit, test/functorch/test_aotdispatch.py::TestAOTExport::test_aot_export_predispatch_func_view, test/functorch/test_aotdispatch.py::TestAOTDispatch::test_aot_dispatch_output_requires_grad_in_no_grad_views, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_aot_module_simplified, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_grads_no_force_contiguous_nested_subclass, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_grads_no_force_contiguous_nested_tensor_tangent, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_lift_fresh_copy_in_graph, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_noncontig_nonmemformat_tangents_dynamic_shapes_False_test_subclasses_False_device_cpu, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_noncontig_nonmemformat_tangents_dynamic_shapes_False_test_subclasses_True_device_cpu, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_noncontig_nonmemformat_tangents_dynamic_shapes_True_test_subclasses_True_device_cpu, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_saved_tensors_hooks_params, test/functorch/test_aotdispatch.py::TestPythonKeyCPU::test_make_fx_functionalize_cpu, test/functorch/test_aotdispatch.py::TestPythonKeyCPU::test_nnc_pytrees_cpu, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_T_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive___rsub___cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive__native_batch_norm_legit_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_amin_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_aminmax_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_argwhere_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_atleast_2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_bucketize_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_cfloat_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_chalf_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_cross_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_diagonal_copy_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_dist_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_dot_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_empty_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_erfc_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_fft_ifft2_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_fft_irfft2_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_fill_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_flex_attention_backward_simple_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_flip_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_flipud_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_float_power_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_floor_divide_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_fmax_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_full_like_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_histc_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_hsplit_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_index_copy_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_index_select_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_invoke_quant_simple_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_isneginf_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_jiterator_4inputs_with_extra_args_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_jiterator_binary_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_kron_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_kthvalue_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_le_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_det_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_diagonal_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_lu_factor_ex_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_matrix_norm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_matrix_rank_hermitian_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_qr_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_svd_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_log_normal_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_logcumsumexp_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_logical_not_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_masked_std_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_masked_sum_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_median_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_mul_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nan_to_num_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nanmedian_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nansum_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_native_dropout_backward_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_new_empty_strided_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_adaptive_avg_pool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_avg_pool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_conv_transpose2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_gaussian_nll_loss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_grid_sample_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_hardshrink_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_instance_norm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_logsigmoid_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_max_pool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_max_unpool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_mse_loss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_pad_replicate_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_pad_replicate_negative_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_pdist_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_pixel_shuffle_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_pixel_unshuffle_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_scaled_dot_product_attention_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_unfold_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_nn_functional_upsample_nearest_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_norm_inf_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_normal_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_rad2deg_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_randint_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_randint_like_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_reshape_as_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_round_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_round_decimals_0_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_rsub_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_scalar_tensor_cpu_float32, 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test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Conv3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ConvTranspose1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ConvTranspose3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_CosineEmbeddingLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_GroupNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Hardshrink_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Hardswish_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_HuberLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LPPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LayerNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MultiheadAttention_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_TransformerEncoder_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ZeroPad3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_AdaptiveAvgPool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_BatchNorm3d_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Bilinear_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Conv1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Conv2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ConvTranspose2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_GLU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_GaussianNLLLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_InstanceNorm3d_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_InstanceNorm3d_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LPPool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MultiMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_PReLU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_RMSNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ReplicationPad3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Softmin_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ZeroPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_alias_of_intermediate_detach_backend_aot_eager_view_replay_for_aliased_outputs_True_dynamic_shapes_False, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_backward_mutation_data, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_compilation_context, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_composite_impl_compile, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_custom_autograd, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_embedding_bag_view_dynamic, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_aliases_and_output_alias, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_metadata, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_modifies_autograd_meta_of_aliases, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_output_view_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_requires_grad_no_grad_detach_mixed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_requires_grad_no_grad_inference_graph, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_set__nop, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_simple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_output_aliase_custom_autograd_function, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_output_view_metadata_mutate_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_output_view_simple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_invalid_dupe_fake, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_mark_outputs_dynamic_use_autograd_False, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_mem_leak_from_save_for_bw, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_module, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_multi_output_list, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_mutates_input_noncontiguous, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_nonidempotent_amp, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_input_multi_output_view_should_raise_autograd_error, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_and_returned, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_inplace_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_returned_multiple_times, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_multiple_inputs_get_correct_one, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_op_depending_on_symint, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_set__and_data_mutation_bad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_set__not_allowed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_view_and_inplace_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_backward_mutation_forward_inputs, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_batchnorm_inference, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_buffer_copied_in_graph, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_custom_tensor_metadata, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_alias_everything, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_aliases_other_input, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_false_aliasing, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_output_view_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_requires_grad_no_grad_inference_graph, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_return, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_simple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_inputs_overlapping_with_mutation_guard_base, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_invalid_dupe, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_multi_output, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_mutates_input_noncontiguous, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_nested_subclasses, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_nonidempotent_amp, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_input_multi_output_view_should_raise_autograd_error, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_and_returned_different_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_inplace_view_with_detach, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_intermediate_multi_output_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_subclass_metadata_mutation_req_grad_False 2025-07-17T07:47:47.1778334Z 2025-07-17T07:47:50.4599185Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:47:50.4602218Z import pkg_resources 2025-07-17T07:47:50.5811052Z Running test_foreach 1/1 ... [2025-07-17 07:47:50.580801] 2025-07-17T07:47:50.5811798Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:47:50.5815221Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_foreach.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'] ... [2025-07-17 07:47:50.581161] 2025-07-17T07:49:10.7400613Z 2025-07-17T07:49:10.7401709Z functorch/test_aotdispatch 6/6 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_aotdispatch_6.6_119d2ae22dd53628_.log 2025-07-17T07:49:10.7596104Z Running 352 items in this shard: test/functorch/test_aotdispatch.py::TestAOTAutograd::test_alias_of_intermediate_detach_backend_aot_eager_view_replay_for_aliased_outputs_False_dynamic_shapes_False, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_autocast_disable_guard, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_buffer_copied_in_graph, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_compilation_context, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_embedding_bag_view_dynamic, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_data_and_metadata_mutation, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_data_and_metadata_mutation_aliases_other_input, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_alias_everything, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_metadata, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_modifies_autograd_meta_of_aliases, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_requires_grad_no_grad_inference_graph, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_mutation_storage_resize_down_and_set_, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_input_output_view_mutate_multiple, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_no_grad_input_output, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_output_aliases_input_multi_output_view, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_output_aliases_intermediate_multi_output_view, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_outputs_are_aliased, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_real_weights_in_symbolic_mode, test/functorch/test_aotdispatch.py::TestAOTAutograd::test_set__and_data_mutation_bad, test/functorch/test_aotdispatch.py::TestAOTExport::test_aot_export_predispatch_composite_implicit_inplace, test/functorch/test_aotdispatch.py::TestAOTExport::test_aot_export_predispatch_func_simple, test/functorch/test_aotdispatch.py::TestAOTExport::test_aot_export_predispatch_with_autograd_op, test/functorch/test_aotdispatch.py::TestAOTExport::test_aot_export_unbacked_arg, test/functorch/test_aotdispatch.py::TestAOTExport::test_aot_export_with_torch_cond, test/functorch/test_aotdispatch.py::TestPartitioning::test_autocast, test/functorch/test_aotdispatch.py::TestPartitioning::test_contiguous, test/functorch/test_aotdispatch.py::TestAOTDispatch::test_aot_dispatch_output_alias, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_aot_module_simplified_fake_tensor_gm_raises, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_grads_no_force_contiguous_subclass, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_noncontig_nonmemformat_tangents_dynamic_shapes_True_test_subclasses_False_device_cpu, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_saved_tensors_hooks_base_saved_tensors_hooks_filtering_mode_all, test/functorch/test_aotdispatch.py::TestAOTModuleSimplified::test_wrong_guess_tangent_type, test/functorch/test_aotdispatch.py::TestPythonKeyCPU::test_make_fx_grad_cpu, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_H_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive___getitem___cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive___rdiv___cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive__chunk_cat_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive__segment_reduce_lengths_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_addcmul_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_allclose_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_angle_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_arange_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_argmax_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_argmin_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_atleast_1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_cartesian_prod_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_cholesky_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_combinations_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_conj_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_cummin_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_diag_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_diag_embed_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_div_trunc_rounding_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_einsum_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_empty_permuted_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_erfinv_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_fft_hfft2_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_fliplr_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_fmin_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_geometric_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_geqrf_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_gt_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_hstack_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_index_add_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_index_put_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_index_reduce_amax_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_index_reduce_prod_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_isclose_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_jiterator_unary_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_lerp_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_lgamma_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_cross_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_inv_ex_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_ldl_factor_ex_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_exhaustive_linalg_ldl_solve_cpu_float32, 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test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_nn_functional_softplus_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_normal_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_normal_number_mean_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_ones_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_polygamma_polygamma_n_1_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_prod_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_put_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_reshape_as_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_rsqrt_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_sign_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_signbit_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_hermite_polynomial_he_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_laguerre_polynomial_l_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_legendre_polynomial_p_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_special_spherical_bessel_j0_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_split_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_split_with_sizes_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_squeeze_copy_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_squeeze_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_std_unbiased_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_tan_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_tanh_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_tensor_split_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_transpose_copy_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_transpose_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_unsqueeze_copy_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionOpInfoCPU::test_aot_autograd_symbolic_exhaustive_zeros_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_AdaptiveMaxPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_AvgPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_AvgPool3d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_BatchNorm2d_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ConstantPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_ConvTranspose2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_GRU_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Hardtanh_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_HingeEmbeddingLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_LazyConvTranspose1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MaxPool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MultiLabelMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_MultiLabelSoftMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_PReLU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_RMSNorm_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_SELU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_SoftMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Softmax2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Softmax_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_Softshrink_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_module_exhaustive_nn_TransformerEncoderLayer_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_BatchNorm1d_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_CELU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_CircularPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_CrossEntropyLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ELU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Embedding_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_FractionalMaxPool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Hardtanh_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_HingeEmbeddingLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_InstanceNorm1d_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_InstanceNorm2d_eval_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_L1Loss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LPPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LSTM_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_LogSoftmax_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MSELoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MaxPool1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_MaxPool2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ReflectionPad1d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_ReflectionPad2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_SiLU_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_SoftMarginLoss_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Softmax2d_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Softplus_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_Softshrink_cpu_float32, test/functorch/test_aotdispatch.py::TestEagerFusionModuleInfoCPU::test_aot_autograd_symbolic_module_exhaustive_nn_TransformerEncoder_train_mode_cpu_float32, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_alias_of_intermediate_detach_backend_aot_eager_view_replay_for_aliased_outputs_False_dynamic_shapes_False, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_dupe_arg_torture, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_dynamic_shape_output_not_in_bw_graph, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_batchnorm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_hidden_from_autograd_aliasing, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_storage_resize_down_and_set_, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_input_mutation_storage_resize_up, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_invalid_dupe, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_nested_subclasses_non_nested_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_and_returned_flipped, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_aliases_intermediate_inplace_view_with_detach, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_output_dict, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_some_outputs_dont_require_grad_non_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_squeeze_mutation, test/functorch/test_aotdispatch.py::TestAOTAutogradWithDynamo::test_subclass_metadata_mutation_req_grad_False, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_alias_of_intermediate_detach_backend_inductor_view_replay_for_aliased_outputs_False_dynamic_shapes_False, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_alias_of_intermediate_detach_backend_inductor_view_replay_for_aliased_outputs_True_dynamic_shapes_True, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_backward_mutation_on_grad_out, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_backward_pass_autocast_off, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_batchnorm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_buffer_batch_norm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_dynamic_output_aliases_input_view_meta_replay, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_metadata_mutation_aliases, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_batchnorm, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_metadata, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_multiple, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_requires_grad_no_grad_detach_mixed, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_set__input_mutation, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_simple_with_none_and_nontensor, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_input_mutation_storage_resize_down, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_list_codegen, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_mark_activations_dynamic, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_mutations_in_bw_detached_from_tangent, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_nested_subclasses_complicated_inps, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_nested_subclasses_non_nested_grad, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_non_tensor_and_none_inputs, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_input_multi_output_view, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_multiple_inputs_get_correct_one, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_aliases_output_view_meta_replay, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_output_dict, test/functorch/test_aotdispatch.py::TestAOTAutogradWithCache::test_synthetic_base_base_attribute_is_none 2025-07-17T07:49:10.7785999Z 2025-07-17T07:49:13.8634363Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:13.8635938Z import pkg_resources 2025-07-17T07:49:13.9869984Z Running dynamo/cpython/3_13/test_generator_stop 1/1 ... [2025-07-17 07:49:13.986633] 2025-07-17T07:49:13.9870975Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:13.9873537Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_generator_stop.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'] ... [2025-07-17 07:49:13.986995] 2025-07-17T07:49:16.3204772Z 2025-07-17T07:49:16.3206139Z test_foreach 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_foreach_1.1_77c3466cabccc109_.log 2025-07-17T07:49:16.4841515Z Running 3577 items in this shard: test/test_foreach.py::TestForeachCPU::test_0dim_tensor_overload_cpu_ok_cpu, test/test_foreach.py::TestForeachCPU::test_0dim_tensor_overload_exception_cpu, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_bfloat16, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_complex128, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_complex64, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_float16, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_float32, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_float64, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_int16, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_int32, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_int64, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_int8, test/test_foreach.py::TestForeachCPU::test_add_scalar_with_empty_list_and_empty_tensor_cpu_uint8, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_abs_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_acos_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_add_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_addcdiv_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_addcmul_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_asin_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_atan_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_ceil_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_clamp_max_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_clamp_min_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_copy_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_cos_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_cosh_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_div_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_erf_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_erfc_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_exp_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_expm1_cpu_float32, test/test_foreach.py::TestForeachCPU::test_all_zero_size_tensors_do_not_launch_kernel__foreach_floor_cpu_float32, 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test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_tanh_cpu_uint8, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_bfloat16, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_bool, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_complex128, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_complex64, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_float16, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_float32, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_float64, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_int16, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_int32, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_int64, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_int8, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_trunc_cpu_uint8, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_bfloat16, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_bool, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_complex128, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_complex64, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_float16, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_float32, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_float64, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_int16, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_int32, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_int64, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_int8, test/test_foreach.py::TestForeachCPU::test_unary_op_tensors_on_different_devices__foreach_zero_cpu_uint8 2025-07-17T07:49:16.6304268Z 2025-07-17T07:49:17.2563940Z 2025-07-17T07:49:17.2565611Z dynamo/cpython/3_13/test_generator_stop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_generator_stop_1.1_805209f44a7f23a2_.log 2025-07-17T07:49:17.2568877Z Running 2 items in this shard: test/dynamo/cpython/3_13/test_generator_stop.py::TestPEP479::test_stopiteration_wrapping, test/dynamo/cpython/3_13/test_generator_stop.py::TestPEP479::test_stopiteration_wrapping_context 2025-07-17T07:49:17.2570618Z 2025-07-17T07:49:19.4442804Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:19.4444384Z import pkg_resources 2025-07-17T07:49:19.5678114Z Running dynamo/cpython/3_13/test_exception_variations 1/1 ... [2025-07-17 07:49:19.567450] 2025-07-17T07:49:19.5678701Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:19.5680573Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_exception_variations.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'] ... [2025-07-17 07:49:19.567798] 2025-07-17T07:49:20.3388783Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:20.3391667Z import pkg_resources 2025-07-17T07:49:20.4631961Z Running dynamo/cpython/3_13/test_int_literal 1/1 ... [2025-07-17 07:49:20.462807] 2025-07-17T07:49:20.4632938Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:20.4635190Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_int_literal.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'] ... [2025-07-17 07:49:20.463198] 2025-07-17T07:49:22.9366194Z 2025-07-17T07:49:22.9368024Z dynamo/cpython/3_13/test_exception_variations 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_exception_variations_1.1_4b0d0bd80ef942ba_.log 2025-07-17T07:49:22.9383617Z Running 15 items in this shard: test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_nested, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_nested_else, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_nested_exception_in_else, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_nested_exception_in_except, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_nested_exception_in_finally_no_exception, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_nested_exception_in_finally_with_exception, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_try_except, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_try_except_else, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_try_except_else_finally, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_try_except_else_finally_no_exception, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_try_except_else_no_exception, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_try_except_finally, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_try_except_finally_no_exception, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_try_except_no_exception, test/dynamo/cpython/3_13/test_exception_variations.py::ExceptTestCases::test_try_finally_no_exception 2025-07-17T07:49:22.9396907Z 2025-07-17T07:49:23.7322717Z 2025-07-17T07:49:23.7324333Z dynamo/cpython/3_13/test_int_literal 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_int_literal_1.1_4a9d845e91cd43f9_.log 2025-07-17T07:49:23.7330177Z Running 6 items in this shard: test/dynamo/cpython/3_13/test_int_literal.py::TestHexOctBin::test_bin_baseline, test/dynamo/cpython/3_13/test_int_literal.py::TestHexOctBin::test_bin_unsigned, test/dynamo/cpython/3_13/test_int_literal.py::TestHexOctBin::test_hex_baseline, test/dynamo/cpython/3_13/test_int_literal.py::TestHexOctBin::test_hex_unsigned, test/dynamo/cpython/3_13/test_int_literal.py::TestHexOctBin::test_oct_baseline, test/dynamo/cpython/3_13/test_int_literal.py::TestHexOctBin::test_oct_unsigned 2025-07-17T07:49:23.7334390Z 2025-07-17T07:49:26.2229831Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:26.2231473Z import pkg_resources 2025-07-17T07:49:26.3476989Z Running dynamo/cpython/3_13/test_with 1/1 ... [2025-07-17 07:49:26.347394] 2025-07-17T07:49:26.3477530Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:26.3479645Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_with.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'] ... [2025-07-17 07:49:26.347725] 2025-07-17T07:49:26.9952927Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:26.9955677Z import pkg_resources 2025-07-17T07:49:27.1192964Z Running dynamo/cpython/3_13/test_numeric_tower 1/1 ... [2025-07-17 07:49:27.118890] 2025-07-17T07:49:27.1193539Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:27.1194869Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_numeric_tower.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'] ... [2025-07-17 07:49:27.119223] 2025-07-17T07:49:29.8172588Z 2025-07-17T07:49:29.8173591Z dynamo/cpython/3_13/test_with 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_with_1.1_074e99d93b1692b5_.log 2025-07-17T07:49:29.8199031Z Running 51 items in this shard: test/dynamo/cpython/3_13/test_with.py::FailureTestCase::testAssignmentToNoneError, test/dynamo/cpython/3_13/test_with.py::FailureTestCase::testAssignmentToTupleContainingNoneError, test/dynamo/cpython/3_13/test_with.py::FailureTestCase::testAssignmentToTupleOnlyContainingNoneError, test/dynamo/cpython/3_13/test_with.py::FailureTestCase::testEnterAttributeError1, test/dynamo/cpython/3_13/test_with.py::FailureTestCase::testEnterAttributeError2, test/dynamo/cpython/3_13/test_with.py::FailureTestCase::testEnterThrows, test/dynamo/cpython/3_13/test_with.py::FailureTestCase::testExitAttributeError, test/dynamo/cpython/3_13/test_with.py::FailureTestCase::testExitThrows, test/dynamo/cpython/3_13/test_with.py::FailureTestCase::testNameError, test/dynamo/cpython/3_13/test_with.py::NonexceptionalTestCase::testBoundGenerator, test/dynamo/cpython/3_13/test_with.py::NonexceptionalTestCase::testInlineGeneratorBoundSyntax, test/dynamo/cpython/3_13/test_with.py::NonexceptionalTestCase::testInlineGeneratorBoundToDottedVariable, test/dynamo/cpython/3_13/test_with.py::NonexceptionalTestCase::testInlineGeneratorBoundToExistingVariable, test/dynamo/cpython/3_13/test_with.py::NonexceptionalTestCase::testInlineGeneratorSyntax, test/dynamo/cpython/3_13/test_with.py::NonexceptionalTestCase::testNestedSingleStatements, test/dynamo/cpython/3_13/test_with.py::NonexceptionalTestCase::testUnboundGenerator, test/dynamo/cpython/3_13/test_with.py::NestedNonexceptionalTestCase::testMultipleArgBound, test/dynamo/cpython/3_13/test_with.py::NestedNonexceptionalTestCase::testMultipleArgUnbound, test/dynamo/cpython/3_13/test_with.py::NestedNonexceptionalTestCase::testSingleArgBoundToMultipleElementTupleError, test/dynamo/cpython/3_13/test_with.py::NestedNonexceptionalTestCase::testSingleArgBoundToNonTuple, test/dynamo/cpython/3_13/test_with.py::NestedNonexceptionalTestCase::testSingleArgBoundToSingleElementParenthesizedList, test/dynamo/cpython/3_13/test_with.py::NestedNonexceptionalTestCase::testSingleArgInlineGeneratorSyntax, test/dynamo/cpython/3_13/test_with.py::NestedNonexceptionalTestCase::testSingleArgUnbound, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testErrorsInBool, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testExceptionNormalized, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testMultipleResourcesInSingleStatement, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testNestedExceptionAfterInnerStatement, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testNestedExceptionBeforeInnerStatement, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testNestedSingleStatements, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testRaisedGeneratorExit1, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testRaisedGeneratorExit2, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testRaisedStopIteration1, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testRaisedStopIteration2, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testRaisedStopIteration3, test/dynamo/cpython/3_13/test_with.py::ExceptionalTestCase::testSingleResource, test/dynamo/cpython/3_13/test_with.py::NonLocalFlowControlTestCase::testWithBreak, test/dynamo/cpython/3_13/test_with.py::NonLocalFlowControlTestCase::testWithContinue, test/dynamo/cpython/3_13/test_with.py::NonLocalFlowControlTestCase::testWithRaise, test/dynamo/cpython/3_13/test_with.py::NonLocalFlowControlTestCase::testWithReturn, test/dynamo/cpython/3_13/test_with.py::NonLocalFlowControlTestCase::testWithYield, test/dynamo/cpython/3_13/test_with.py::AssignmentTargetTestCase::testMultipleComplexTargets, test/dynamo/cpython/3_13/test_with.py::AssignmentTargetTestCase::testSingleComplexTarget, test/dynamo/cpython/3_13/test_with.py::AssignmentTargetTestCase::testWithExtendedTargets, test/dynamo/cpython/3_13/test_with.py::ExitSwallowsExceptionTestCase::testExitFalseDoesntSwallowException, test/dynamo/cpython/3_13/test_with.py::ExitSwallowsExceptionTestCase::testExitTrueSwallowsException, test/dynamo/cpython/3_13/test_with.py::NestedWith::testEnterReturnsTuple, test/dynamo/cpython/3_13/test_with.py::NestedWith::testExceptionInEnter, test/dynamo/cpython/3_13/test_with.py::NestedWith::testExceptionInExit, test/dynamo/cpython/3_13/test_with.py::NestedWith::testExceptionInExprList, test/dynamo/cpython/3_13/test_with.py::NestedWith::testExceptionLocation, test/dynamo/cpython/3_13/test_with.py::NestedWith::testNoExceptions 2025-07-17T07:49:29.8220744Z 2025-07-17T07:49:30.2006463Z Uploading artifacts took 0.38 seconds 2025-07-17T07:49:30.5390630Z 2025-07-17T07:49:30.5391761Z dynamo/cpython/3_13/test_numeric_tower 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_numeric_tower_1.1_b86f8fce7b445f48_.log 2025-07-17T07:49:30.5395904Z Running 9 items in this shard: test/dynamo/cpython/3_13/test_numeric_tower.py::HashTest::test_binary_floats, test/dynamo/cpython/3_13/test_numeric_tower.py::HashTest::test_bools, test/dynamo/cpython/3_13/test_numeric_tower.py::HashTest::test_complex, test/dynamo/cpython/3_13/test_numeric_tower.py::HashTest::test_decimals, test/dynamo/cpython/3_13/test_numeric_tower.py::HashTest::test_fractions, test/dynamo/cpython/3_13/test_numeric_tower.py::HashTest::test_hash_normalization, test/dynamo/cpython/3_13/test_numeric_tower.py::HashTest::test_integers, test/dynamo/cpython/3_13/test_numeric_tower.py::ComparisonTest::test_complex, test/dynamo/cpython/3_13/test_numeric_tower.py::ComparisonTest::test_mixed_comparisons 2025-07-17T07:49:30.5399454Z 2025-07-17T07:49:33.0388657Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:33.1628965Z import pkg_resources 2025-07-17T07:49:33.1629495Z Running dynamo/cpython/3_13/test_ordered_dict 1/1 ... [2025-07-17 07:49:33.162600] 2025-07-17T07:49:33.1630026Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:33.1631381Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_ordered_dict.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'] ... [2025-07-17 07:49:33.162919] 2025-07-17T07:49:33.7477060Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:33.7479952Z import pkg_resources 2025-07-17T07:49:33.8705313Z Running dynamo/cpython/3_13/test_baseexception 1/1 ... [2025-07-17 07:49:33.870179] 2025-07-17T07:49:33.8706302Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:33.8708556Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_baseexception.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'] ... [2025-07-17 07:49:33.870510] 2025-07-17T07:49:37.2898545Z 2025-07-17T07:49:37.2899664Z dynamo/cpython/3_13/test_baseexception 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_baseexception_1.1_5e10393c078516c7_.log 2025-07-17T07:49:37.2905203Z Running 11 items in this shard: test/dynamo/cpython/3_13/test_baseexception.py::ExceptionClassTests::test_builtins_new_style, test/dynamo/cpython/3_13/test_baseexception.py::ExceptionClassTests::test_inheritance, test/dynamo/cpython/3_13/test_baseexception.py::ExceptionClassTests::test_interface_multi_arg, test/dynamo/cpython/3_13/test_baseexception.py::ExceptionClassTests::test_interface_no_arg, test/dynamo/cpython/3_13/test_baseexception.py::ExceptionClassTests::test_interface_single_arg, test/dynamo/cpython/3_13/test_baseexception.py::ExceptionClassTests::test_setstate_refcount_no_crash, test/dynamo/cpython/3_13/test_baseexception.py::UsageTests::test_catch_BaseException_instance, test/dynamo/cpython/3_13/test_baseexception.py::UsageTests::test_catch_non_BaseException, test/dynamo/cpython/3_13/test_baseexception.py::UsageTests::test_catch_string, test/dynamo/cpython/3_13/test_baseexception.py::UsageTests::test_raise_new_style_non_exception, test/dynamo/cpython/3_13/test_baseexception.py::UsageTests::test_raise_string 2025-07-17T07:49:37.2910081Z 2025-07-17T07:49:40.4629132Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:40.4631457Z import pkg_resources 2025-07-17T07:49:40.5874048Z Running dynamo/cpython/3_13/test_int 1/1 ... [2025-07-17 07:49:40.586957] 2025-07-17T07:49:40.5874781Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:40.5876750Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_int.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'] ... [2025-07-17 07:49:40.587349] 2025-07-17T07:49:45.3079805Z 2025-07-17T07:49:45.3080781Z dynamo/cpython/3_13/test_int 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_int_1.1_914c5e2b94eb1356_.log 2025-07-17T07:49:45.3098931Z Running 46 items in this shard: test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_basic, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_error_message, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_int_base_bad_types, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_int_base_indexable, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_int_base_limits, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_int_memoryview, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_int_returns_int_subclass, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_int_subclass_with_index, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_int_subclass_with_int, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_intconversion, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_invalid_signs, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_issue31619, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_keyword_args, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_no_args, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_non_numeric_input_types, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_small_ints, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_string_float, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_underscores, test/dynamo/cpython/3_13/test_int.py::IntTestCases::test_unicode, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_denial_of_service_prevented_int_to_str, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_denial_of_service_prevented_str_to_int, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_disabled_limit, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_int_from_other_bases, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_int_max_str_digits_is_per_interpreter, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_max_str_digits, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_max_str_digits_edge_cases, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_power_of_two_bases_unlimited, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_sign_not_counted, test/dynamo/cpython/3_13/test_int.py::IntStrDigitLimitsTests::test_underscores_ignored, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_denial_of_service_prevented_int_to_str, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_denial_of_service_prevented_str_to_int, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_disabled_limit, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_int_from_other_bases, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_int_max_str_digits_is_per_interpreter, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_max_str_digits, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_max_str_digits_edge_cases, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_power_of_two_bases_unlimited, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_sign_not_counted, test/dynamo/cpython/3_13/test_int.py::IntSubclassStrDigitLimitsTests::test_underscores_ignored, test/dynamo/cpython/3_13/test_int.py::PyLongModuleTests::test_pylong_int_divmod, test/dynamo/cpython/3_13/test_int.py::PyLongModuleTests::test_pylong_int_to_decimal, test/dynamo/cpython/3_13/test_int.py::PyLongModuleTests::test_pylong_int_to_decimal_2, test/dynamo/cpython/3_13/test_int.py::PyLongModuleTests::test_pylong_misbehavior_error_path_from_str, test/dynamo/cpython/3_13/test_int.py::PyLongModuleTests::test_pylong_misbehavior_error_path_to_str, test/dynamo/cpython/3_13/test_int.py::PyLongModuleTests::test_pylong_roundtrip, test/dynamo/cpython/3_13/test_int.py::PyLongModuleTests::test_pylong_str_to_int 2025-07-17T07:49:45.3116229Z 2025-07-17T07:49:48.4358962Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:48.4360551Z import pkg_resources 2025-07-17T07:49:48.5608629Z Running dynamo/cpython/3_13/test_itertools 1/1 ... [2025-07-17 07:49:48.560561] 2025-07-17T07:49:48.5609152Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:48.5611519Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_itertools.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'] ... [2025-07-17 07:49:48.560917] 2025-07-17T07:49:51.2536404Z 2025-07-17T07:49:51.2537500Z dynamo/cpython/3_13/test_ordered_dict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_ordered_dict_1.1_91fe196fd7488361_.log 2025-07-17T07:49:51.2667813Z Running 295 items in this shard: test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_468, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_abc, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_copying, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_delitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_delitem_hash_collision, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_detect_deletion_during_iteration, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_dict_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_dict_delitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_dict_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_dict_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_dict_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_dict_setitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_dict_update, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_equality, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_free_after_iterating, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_fromkeys, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_highly_nested, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_highly_nested_subclass, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_init, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_init_calls, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_issue119004_attribute_error, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_issue24347, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_issue24348, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_issue24667, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_iterators, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_iterators_empty, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_merge_operator, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_move_to_end, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_move_to_end_issue25406, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_ordered_dict_items_result_gc, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_overridden_init, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_override_update, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_pickle_recursive, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_popitem_last, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_reduce_not_too_fat, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_reference_loop, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_reinsert, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_repr, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_repr_recursive, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_repr_recursive_values, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_setitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_sizeof, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_sorted_iterators, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_update, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_views, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictTests::test_yaml_linkage, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_abc, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_delitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_delitem_hash_collision, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_detect_deletion_during_iteration, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_highly_nested, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_highly_nested_subclass, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_init, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_override_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_reinsert, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_setitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonBuiltinDictTests::test_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_468, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_abc, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_copying, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_delitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_delitem_hash_collision, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_detect_deletion_during_iteration, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_dict_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_dict_delitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_dict_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_dict_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_dict_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_dict_setitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_dict_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_equality, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_free_after_iterating, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_fromkeys, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_highly_nested, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_highly_nested_subclass, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_init, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_init_calls, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_issue119004_change_linked_list_by_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_issue119004_change_linked_list_by_delete_key, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_issue119004_change_size_by_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_issue119004_change_size_by_delete_key, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_issue119004_change_size_by_delete_key_in_dict_eq, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_issue24347, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_issue24348, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_issue24667, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_iterators, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_iterators_empty, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_iterators_pickling, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_key_change_during_iteration, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_merge_operator, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_move_to_end, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_move_to_end_issue25406, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_ordered_dict_items_result_gc, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_overridden_init, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_override_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_pickle_recursive, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_popitem_last, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_reduce_not_too_fat, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_reference_loop, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_reinsert, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_repr, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_repr_recursive, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_repr_recursive_values, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_setitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_sizeof, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_sizeof_exact, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_sorted_iterators, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_views, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_weakref_list_is_not_traversed, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictTests::test_yaml_linkage, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_468, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_abc, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_copying, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_delitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_delitem_hash_collision, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_detect_deletion_during_iteration, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_dict_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_dict_delitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_dict_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_dict_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_dict_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_dict_setitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_dict_update, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_equality, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_free_after_iterating, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_fromkeys, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_highly_nested, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_highly_nested_subclass, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_init, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_init_calls, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_issue119004_attribute_error, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_issue24347, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_issue24348, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_issue24667, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_iterators, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_iterators_empty, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_merge_operator, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_move_to_end, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_move_to_end_issue25406, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_ordered_dict_items_result_gc, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_overridden_init, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_override_update, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_pickle_recursive, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_popitem_last, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_reduce_not_too_fat, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_reference_loop, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_reinsert, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_repr, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_repr_recursive, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_repr_recursive_values, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_setitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_sizeof, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_sorted_iterators, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_update, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_views, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictSubclassTests::test_yaml_linkage, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_468, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_abc, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_copying, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_delitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_delitem_hash_collision, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_detect_deletion_during_iteration, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_dict_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_dict_delitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_dict_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_dict_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_dict_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_dict_setitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_dict_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_equality, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_free_after_iterating, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_fromkeys, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_highly_nested, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_highly_nested_subclass, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_init, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_init_calls, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_issue119004_change_linked_list_by_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_issue119004_change_linked_list_by_delete_key, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_issue119004_change_size_by_clear, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_issue119004_change_size_by_delete_key, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_issue119004_change_size_by_delete_key_in_dict_eq, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_issue24347, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_issue24348, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_issue24667, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_iterators, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_iterators_empty, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_iterators_pickling, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_key_change_during_iteration, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_merge_operator, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_move_to_end, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_move_to_end_issue25406, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_ordered_dict_items_result_gc, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_overridden_init, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_override_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_pickle_recursive, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_popitem_last, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_reduce_not_too_fat, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_reference_loop, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_reinsert, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_repr, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_repr_recursive, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_repr_recursive_values, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_setitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_sizeof, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_sizeof_exact, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_sorted_iterators, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_views, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_weakref_list_is_not_traversed, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictSubclassTests::test_yaml_linkage, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonOrderedDictWithSlotsCopyingTests::test_copying, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonOrderedDictWithSlotsCopyingTests::test_copying, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_bool, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_constructor, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_get, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_getitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_items, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_keys, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_len, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_read, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_update, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_values, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonGeneralMappingTests::test_write, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_bool, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_constructor, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_get, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_getitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_items, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_keys, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_len, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_read, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_values, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonGeneralMappingTests::test_write, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_bool, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_constructor, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_get, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_getitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_items, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_keys, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_len, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_read, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_update, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_values, test/dynamo/cpython/3_13/test_ordered_dict.py::PurePythonSubclassMappingTests::test_write, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_bool, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_constructor, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_get, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_getitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_items, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_keys, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_len, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_read, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_setdefault, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_update, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_values, test/dynamo/cpython/3_13/test_ordered_dict.py::CPythonSubclassMappingTests::test_write, test/dynamo/cpython/3_13/test_ordered_dict.py::PySimpleLRUCacheTests::test_add_after_full, test/dynamo/cpython/3_13/test_ordered_dict.py::PySimpleLRUCacheTests::test_change_order_on_get, test/dynamo/cpython/3_13/test_ordered_dict.py::PySimpleLRUCacheTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::PySimpleLRUCacheTests::test_popitem, test/dynamo/cpython/3_13/test_ordered_dict.py::CSimpleLRUCacheTests::test_add_after_full, test/dynamo/cpython/3_13/test_ordered_dict.py::CSimpleLRUCacheTests::test_change_order_on_get, test/dynamo/cpython/3_13/test_ordered_dict.py::CSimpleLRUCacheTests::test_pop, test/dynamo/cpython/3_13/test_ordered_dict.py::CSimpleLRUCacheTests::test_popitem 2025-07-17T07:49:51.2793615Z 2025-07-17T07:49:54.3281240Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:54.3282838Z import pkg_resources 2025-07-17T07:49:54.4533892Z Running dynamo/cpython/3_13/test_userlist 1/1 ... [2025-07-17 07:49:54.453100] 2025-07-17T07:49:54.4534401Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:49:54.4536897Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_userlist.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'] ... [2025-07-17 07:49:54.453432] 2025-07-17T07:49:56.7369054Z 2025-07-17T07:49:56.7370137Z dynamo/cpython/3_13/test_itertools 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_itertools_1.1_f68c6a270e1ec02d_.log 2025-07-17T07:49:56.7417872Z Running 139 items in this shard: test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_StopIteration, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_accumulate, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_batched, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_bug_7244, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_chain, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_chain_from_iterable, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_chain_reducible, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_chain_setstate, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_combinations, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_combinations_overflow, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_combinations_result_gc, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_combinations_tuple_reuse, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_combinations_with_replacement, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_combinations_with_replacement_overflow, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_combinations_with_replacement_result_gc, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_combinations_with_replacement_tuple_reuse, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_combinatorics, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_compress, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_count, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_count_threading, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_count_with_step, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_count_with_step_threading, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_cycle, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_cycle_copy_pickle, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_cycle_setstate, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_cycle_unpickle_compat, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_dropwhile, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_filter, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_filterfalse, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_groupby, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_immutable_types, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_islice, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_map, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_pairwise, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_pairwise_reenter, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_pairwise_reenter2, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_pairwise_result_gc, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_permutations, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_permutations_overflow, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_permutations_result_gc, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_permutations_tuple_reuse, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_product, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_product_issue_25021, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_product_overflow, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_product_pickling, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_product_result_gc, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_product_tuple_reuse, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_repeat, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_repeat_with_negative_times, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_starmap, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_takewhile, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_tee, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_tee_concurrent, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_tee_dealloc_segfault, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_tee_del_backward, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_tee_reenter, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_zip, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_zip_longest_bad_iterable, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_zip_longest_pickling, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_zip_longest_result_gc, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_zip_longest_tuple_reuse, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_zip_tuple_reuse, test/dynamo/cpython/3_13/test_itertools.py::TestBasicOps::test_ziplongest, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_accumulate, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_accumulate_reducible, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_accumulate_reducible_none, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_chain, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_chain_from_iterable, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_combinations, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_combinations_with_replacement, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_compress, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_count, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_cycle, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_dropwhile, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_filter, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_filterfalse, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_groupby, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_islice, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_map, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_permutations, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_product, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_repeat, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_stapmap, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_takewhile, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_zip, test/dynamo/cpython/3_13/test_itertools.py::TestExamples::test_zip_longest, test/dynamo/cpython/3_13/test_itertools.py::TestPurePythonRoughEquivalents::test_batched_recipe, test/dynamo/cpython/3_13/test_itertools.py::TestPurePythonRoughEquivalents::test_islice_recipe, test/dynamo/cpython/3_13/test_itertools.py::TestPurePythonRoughEquivalents::test_tee_recipe, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_accumulate, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_batched, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_chain, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_chain_from_iterable, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_combinations, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_combinations_with_replacement, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_compress, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_count, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_cycle, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_dropwhile, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_filter, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_filterfalse, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_groupby, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_islice, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_issue2246, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_map, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_pairwise, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_permutations, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_product, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_repeat, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_starmap, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_takewhile, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_zip, test/dynamo/cpython/3_13/test_itertools.py::TestGC::test_zip_longest, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_accumulate, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_batched, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_chain, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_compress, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_cycle, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_dropwhile, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_filter, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_filterfalse, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_groupby, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_islice, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_map, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_pairwise, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_product, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_starmap, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_takewhile, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_tee, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_zip, test/dynamo/cpython/3_13/test_itertools.py::TestVariousIteratorArgs::test_ziplongest, test/dynamo/cpython/3_13/test_itertools.py::LengthTransparency::test_repeat, test/dynamo/cpython/3_13/test_itertools.py::LengthTransparency::test_repeat_with_negative_times, test/dynamo/cpython/3_13/test_itertools.py::RegressionTests::test_issue30347_1, test/dynamo/cpython/3_13/test_itertools.py::RegressionTests::test_issue30347_2, test/dynamo/cpython/3_13/test_itertools.py::RegressionTests::test_long_chain_of_empty_iterables, test/dynamo/cpython/3_13/test_itertools.py::RegressionTests::test_sf_793826, test/dynamo/cpython/3_13/test_itertools.py::RegressionTests::test_sf_950057, test/dynamo/cpython/3_13/test_itertools.py::SubclassWithKwargsTest::test_keywords_in_subclass 2025-07-17T07:49:56.7464542Z 2025-07-17T07:49:58.8743362Z 2025-07-17T07:49:58.8744425Z dynamo/cpython/3_13/test_userlist 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_userlist_1.1_a8b182ee72d1a026_.log 2025-07-17T07:49:58.8762602Z Running 51 items in this shard: test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_add_specials, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_addmul, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_append, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_bigrepeat, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_clear, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_constructor_exception_handling, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_constructors, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_contains, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_contains_fake, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_contains_order, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_copy, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_count, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_delitem, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_delslice, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_exhausted_iterator, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_extend, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_extendedslicing, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_free_after_iterating, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_getitem, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_getitem_error, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_getitemoverwriteiter, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_getslice, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_iadd, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_imul, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_index, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_init, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_insert, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_len, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_minmax, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_mixedadd, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_mixedcmp, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_pickle, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_pop, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_radd_specials, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_remove, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_repeat, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_repr, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_repr_deep, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_reverse, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_reversed, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_set_subscript, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_setitem, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_setitem_error, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_setslice, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_slice, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_slice_assign_iterator, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_slice_type, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_sort, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_subscript, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_truth, test/dynamo/cpython/3_13/test_userlist.py::UserListTest::test_userlist_copy 2025-07-17T07:49:58.8779044Z 2025-07-17T07:49:59.8817949Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:49:59.8820999Z import pkg_resources 2025-07-17T07:50:00.0604954Z Running dynamo/cpython/3_13/test_cmath 1/1 ... [2025-07-17 07:50:00.060131] 2025-07-17T07:50:00.0605882Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:00.0608836Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_cmath.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'] ... [2025-07-17 07:50:00.060544] 2025-07-17T07:50:01.9696796Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:01.9698393Z import pkg_resources 2025-07-17T07:50:02.0948614Z Running dynamo/cpython/3_13/test_math 1/1 ... [2025-07-17 07:50:02.094566] 2025-07-17T07:50:02.0949096Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:02.0951517Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_math.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'] ... [2025-07-17 07:50:02.094932] 2025-07-17T07:50:03.5299750Z 2025-07-17T07:50:03.5300712Z dynamo/cpython/3_13/test_cmath 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_cmath_1.1_31f6ff00fcf82543_.log 2025-07-17T07:50:03.5311644Z Running 33 items in this shard: test/dynamo/cpython/3_13/test_cmath.py::CMathTests::testAtanSign, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::testAtanhSign, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::testTanhSign, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_abs, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_abs_overflows, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_cmath_matches_math, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_constants, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_infinity_and_nan_constants, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_input_type, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_isfinite, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_isinf, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_isnan, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_phase, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_polar, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_polar_errno, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_rect, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_specific_values, test/dynamo/cpython/3_13/test_cmath.py::CMathTests::test_user_object, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_asymmetry, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_complex_near_zero, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_complex_special, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_complex_values, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_decimals, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_eight_decimal_places, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_fractions, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_identical, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_identical_infinite, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_inf_ninf_nan, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_integers, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_near_zero, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_negative_tolerances, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_reject_complex_tolerances, test/dynamo/cpython/3_13/test_cmath.py::IsCloseTests::test_zero_tolerance 2025-07-17T07:50:03.5321784Z 2025-07-17T07:50:06.5697190Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:06.5700200Z import pkg_resources 2025-07-17T07:50:06.6972900Z Running dynamo/cpython/3_13/test_operator 1/1 ... [2025-07-17 07:50:06.696888] 2025-07-17T07:50:06.6973899Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:06.6978147Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_operator.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'] ... [2025-07-17 07:50:06.697468] 2025-07-17T07:50:10.2673953Z 2025-07-17T07:50:10.2675002Z dynamo/cpython/3_13/test_operator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_operator_1.1_dd80526e056110fc_.log 2025-07-17T07:50:10.2713103Z Running 106 items in this shard: test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test___all__, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_abs, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_add, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_attrgetter, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_attrgetter_signature, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_bitwise_and, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_bitwise_or, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_bitwise_xor, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_call, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_concat, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_contains, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_countOf, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_delitem, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_dunder_is_original, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_eq, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_floordiv, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_ge, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_getitem, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_gt, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_iconcat_without_getitem, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_index, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_indexOf, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_inplace, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_invert, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_is, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_is_not, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_itemgetter, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_itemgetter_signature, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_le, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_length_hint, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_lshift, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_lt, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_matmul, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_methodcaller, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_methodcaller_signature, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_mod, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_mul, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_ne, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_neg, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_not_, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_pos, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_pow, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_rshift, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_setitem, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_sub, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_truediv, test/dynamo/cpython/3_13/test_operator.py::PyOperatorTestCase::test_truth, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test___all__, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_abs, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_add, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_attrgetter, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_attrgetter_signature, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_bitwise_and, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_bitwise_or, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_bitwise_xor, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_call, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_concat, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_contains, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_countOf, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_delitem, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_dunder_is_original, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_eq, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_floordiv, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_ge, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_getitem, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_gt, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_iconcat_without_getitem, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_index, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_indexOf, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_inplace, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_invert, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_is, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_is_not, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_itemgetter, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_itemgetter_signature, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_le, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_length_hint, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_lshift, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_lt, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_matmul, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_methodcaller, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_methodcaller_signature, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_mod, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_mul, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_ne, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_neg, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_not_, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_pos, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_pow, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_rshift, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_setitem, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_sub, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_truediv, test/dynamo/cpython/3_13/test_operator.py::COperatorTestCase::test_truth, test/dynamo/cpython/3_13/test_operator.py::PyPyOperatorPickleTestCase::test_attrgetter, test/dynamo/cpython/3_13/test_operator.py::PyPyOperatorPickleTestCase::test_itemgetter, test/dynamo/cpython/3_13/test_operator.py::PyPyOperatorPickleTestCase::test_methodcaller, test/dynamo/cpython/3_13/test_operator.py::PyCOperatorPickleTestCase::test_attrgetter, test/dynamo/cpython/3_13/test_operator.py::PyCOperatorPickleTestCase::test_itemgetter, test/dynamo/cpython/3_13/test_operator.py::PyCOperatorPickleTestCase::test_methodcaller, test/dynamo/cpython/3_13/test_operator.py::CPyOperatorPickleTestCase::test_attrgetter, test/dynamo/cpython/3_13/test_operator.py::CPyOperatorPickleTestCase::test_itemgetter, test/dynamo/cpython/3_13/test_operator.py::CPyOperatorPickleTestCase::test_methodcaller, test/dynamo/cpython/3_13/test_operator.py::CCOperatorPickleTestCase::test_attrgetter, test/dynamo/cpython/3_13/test_operator.py::CCOperatorPickleTestCase::test_itemgetter, test/dynamo/cpython/3_13/test_operator.py::CCOperatorPickleTestCase::test_methodcaller 2025-07-17T07:50:10.2749655Z 2025-07-17T07:50:13.3416728Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:13.3418396Z import pkg_resources 2025-07-17T07:50:13.4630970Z Running dynamo/cpython/3_13/test_userdict 1/1 ... [2025-07-17 07:50:13.462778] 2025-07-17T07:50:13.4631480Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:13.4633551Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_userdict.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'] ... [2025-07-17 07:50:13.463113] 2025-07-17T07:50:16.7317753Z 2025-07-17T07:50:16.7319147Z dynamo/cpython/3_13/test_userdict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_userdict_1.1_6d83f27b7340bafe_.log 2025-07-17T07:50:16.7327630Z Running 25 items in this shard: test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_all, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_bool, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_clear, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_constructor, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_contains, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_copy, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_eq, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_fromkeys, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_get, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_getitem, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_init, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_items, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_keys, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_len, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_missing, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_mutatingiteration, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_pop, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_popitem, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_read, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_repr, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_repr_deep, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_setdefault, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_update, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_values, test/dynamo/cpython/3_13/test_userdict.py::UserDictTest::test_write 2025-07-17T07:50:16.7335569Z 2025-07-17T07:50:19.8224211Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:19.8225796Z import pkg_resources 2025-07-17T07:50:19.9442954Z Running dynamo/cpython/3_13/test_set 1/1 ... [2025-07-17 07:50:19.944033] 2025-07-17T07:50:19.9443483Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:19.9446084Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_set.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'] ... [2025-07-17 07:50:19.944371] 2025-07-17T07:50:27.9961527Z 2025-07-17T07:50:27.9962531Z dynamo/cpython/3_13/test_math 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_math_1.1_c626f10837f05a64_.log 2025-07-17T07:50:27.9986812Z Running 84 items in this shard: test/dynamo/cpython/3_13/test_math.py::MathTests::testAcos, test/dynamo/cpython/3_13/test_math.py::MathTests::testAcosh, test/dynamo/cpython/3_13/test_math.py::MathTests::testAsin, test/dynamo/cpython/3_13/test_math.py::MathTests::testAsinh, test/dynamo/cpython/3_13/test_math.py::MathTests::testAtan, test/dynamo/cpython/3_13/test_math.py::MathTests::testAtan2, test/dynamo/cpython/3_13/test_math.py::MathTests::testAtanh, test/dynamo/cpython/3_13/test_math.py::MathTests::testCbrt, test/dynamo/cpython/3_13/test_math.py::MathTests::testCeil, test/dynamo/cpython/3_13/test_math.py::MathTests::testComb, test/dynamo/cpython/3_13/test_math.py::MathTests::testConstants, test/dynamo/cpython/3_13/test_math.py::MathTests::testCopysign, test/dynamo/cpython/3_13/test_math.py::MathTests::testCos, test/dynamo/cpython/3_13/test_math.py::MathTests::testCosh, test/dynamo/cpython/3_13/test_math.py::MathTests::testDegrees, test/dynamo/cpython/3_13/test_math.py::MathTests::testDist, test/dynamo/cpython/3_13/test_math.py::MathTests::testExp, test/dynamo/cpython/3_13/test_math.py::MathTests::testExp2, test/dynamo/cpython/3_13/test_math.py::MathTests::testFabs, test/dynamo/cpython/3_13/test_math.py::MathTests::testFactorial, test/dynamo/cpython/3_13/test_math.py::MathTests::testFactorialHugeInputs, test/dynamo/cpython/3_13/test_math.py::MathTests::testFactorialNonIntegers, test/dynamo/cpython/3_13/test_math.py::MathTests::testFloor, test/dynamo/cpython/3_13/test_math.py::MathTests::testFmod, test/dynamo/cpython/3_13/test_math.py::MathTests::testFrexp, test/dynamo/cpython/3_13/test_math.py::MathTests::testFsum, test/dynamo/cpython/3_13/test_math.py::MathTests::testGcd, test/dynamo/cpython/3_13/test_math.py::MathTests::testHypot, test/dynamo/cpython/3_13/test_math.py::MathTests::testHypotAccuracy, test/dynamo/cpython/3_13/test_math.py::MathTests::testIsfinite, test/dynamo/cpython/3_13/test_math.py::MathTests::testIsinf, test/dynamo/cpython/3_13/test_math.py::MathTests::testIsnan, test/dynamo/cpython/3_13/test_math.py::MathTests::testIsqrt, test/dynamo/cpython/3_13/test_math.py::MathTests::testLdexp, test/dynamo/cpython/3_13/test_math.py::MathTests::testLog, test/dynamo/cpython/3_13/test_math.py::MathTests::testLog10, test/dynamo/cpython/3_13/test_math.py::MathTests::testLog1p, test/dynamo/cpython/3_13/test_math.py::MathTests::testLog2, test/dynamo/cpython/3_13/test_math.py::MathTests::testLog2Exact, test/dynamo/cpython/3_13/test_math.py::MathTests::testModf, test/dynamo/cpython/3_13/test_math.py::MathTests::testPerm, test/dynamo/cpython/3_13/test_math.py::MathTests::testPow, 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2025-07-17T07:50:29.5727513Z dynamo/cpython/3_13/test_set 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_set_1.1_9acc517509e86e82_.log 2025-07-17T07:50:29.5951362Z Running 623 items in this shard: test/dynamo/cpython/3_13/test_set.py::TestSet::test_add, test/dynamo/cpython/3_13/test_set.py::TestSet::test_and, test/dynamo/cpython/3_13/test_set.py::TestSet::test_badcmp, test/dynamo/cpython/3_13/test_set.py::TestSet::test_clear, test/dynamo/cpython/3_13/test_set.py::TestSet::test_constructor_identity, test/dynamo/cpython/3_13/test_set.py::TestSet::test_container_iterator, test/dynamo/cpython/3_13/test_set.py::TestSet::test_contains, test/dynamo/cpython/3_13/test_set.py::TestSet::test_copy, test/dynamo/cpython/3_13/test_set.py::TestSet::test_cyclical_repr, test/dynamo/cpython/3_13/test_set.py::TestSet::test_deepcopy, test/dynamo/cpython/3_13/test_set.py::TestSet::test_difference, 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test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_difference_update_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_difference_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_intersection_update_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_intersection_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_isdisjoint_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_issubset_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_issuperset_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_symmetric_difference_update_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_symmetric_difference_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_union_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestMethodsMutating_Set_List::test_update_with_mutation, test/dynamo/cpython/3_13/test_set.py::TestGraphs::test_cube, test/dynamo/cpython/3_13/test_set.py::TestGraphs::test_cuboctahedron 2025-07-17T07:50:29.6167409Z 2025-07-17T07:50:31.1358580Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:31.1361450Z import pkg_resources 2025-07-17T07:50:31.2593506Z Running dynamo/cpython/3_13/test_iter 1/1 ... [2025-07-17 07:50:31.258999] 2025-07-17T07:50:31.2594396Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:31.2596343Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_iter.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'] ... [2025-07-17 07:50:31.259328] 2025-07-17T07:50:32.6610539Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:32.6613589Z import pkg_resources 2025-07-17T07:50:32.7843494Z Running dynamo/cpython/3_13/test_bool 1/1 ... [2025-07-17 07:50:32.784006] 2025-07-17T07:50:32.7844400Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:32.7846522Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_bool.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'] ... [2025-07-17 07:50:32.784341] 2025-07-17T07:50:35.0281323Z 2025-07-17T07:50:35.0283121Z inductor/test_torchinductor_codegen_dynamic_shapes 2/3 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_dynamic_shapes_2.3_d478fdec2fbcb66f_.log 2025-07-17T07:50:35.0438952Z Running 256 items in this shard: test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test__dyn_quant_matmul_4bit_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool_errors_with_long_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_adaptive_avg_pool_with_output_size_0_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex6_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_add_complex_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_addmm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_support_out_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_aoti_eager_with_scalar_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_arange5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_argmax_argmin_with_nan_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d_backward2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_avg_pool2d_backward_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_baddbmm_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_batch_norm_2d_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bernoulli2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bfloat16_to_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bitwise3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_computed_offsets_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int16_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int16_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int32_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int32_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int64_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_int8_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_uint8_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_int_uint8_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_bucketize_nd_tiling_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_buffer_copied_in_graph_with_different_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_float_ndigits_pos_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_builtins_round_int_ndigits_zero_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_empty_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_empty_index_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_unbacked_empty_1d_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cat_upcasting_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_chunk_recompiles_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_clamp_type_promotion_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_compar_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_complex_memory_overlap_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_config_option_dont_assume_alignment_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_constant_pad_fill_dtype_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv2d_backward_channels_last_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv3d_channels_last_use_block_ptr_True_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_bn_fuse_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_functional_bn_fuse_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_conv_shape_check_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_convolution1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cos_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cummin_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_no_mask_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_cumsum_pattern_matcher_issue_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_fixed_layout_sequential_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_op_unbacked_symints_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_op_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_custom_scan_would_split_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_data_type_propogation_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_deterministic_codegen_on_graph_break_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_deterministic_codegen_with_suffix_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_device_assert_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_diagonal_copy_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div5_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_div8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dropout_deterministic_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float16_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float32_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float32_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_float64_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int16_uint8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_int16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int32_int64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_int64_int8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_dtypeview_uint8_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_elu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_embedding_bag_byte_unpack_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_embedding_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_empty_strided_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_expanded_reduction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_list_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fallback_mutable_op_no_mutated_tensors_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_flip_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fmod_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fmod_zero_dim_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d3_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fractional_max_pool2d4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_full_boolean_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_fuse_large_params_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_gelu_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_generate_rand_fp8_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_generated_code_has_alignment_assert_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_getitem_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_arange1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_argmax_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_misaligned_input_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_scalar_inputs_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_graph_partition_unbacked_symint_as_output_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_hardswish_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_horizonal_fusion1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_dynamic_shapes_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put2_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_index_put_fallback1_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_inductor_multiple_specializations_dynamic_shapes_cpu, 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test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_float16_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_float32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_float64_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unspec_inputs_int32_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_unsqueeze_inplace_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_upsample_cat_conv_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_var_correction_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_vectorized_ops_masked_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_vectorized_ops_masked_var_novec_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_as_real_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_view_detach_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views4_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_views7_dynamic_shapes_cpu, test/inductor/test_torchinductor_codegen_dynamic_shapes.py::DynamicShapesCodegenCpuTests::test_where_with_logical_op_dynamic_shapes_cpu 2025-07-17T07:50:35.0594022Z 2025-07-17T07:50:35.2290986Z 2025-07-17T07:50:35.2292065Z dynamo/cpython/3_13/test_iter 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_iter_1.1_1b16bd8c0a23a4ea_.log 2025-07-17T07:50:35.2309684Z Running 57 items in this shard: test/dynamo/cpython/3_13/test_iter.py::TestCase::test_3720, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_builtin_filter, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_builtin_list, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_builtin_map, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_builtin_max_min, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_builtin_tuple, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_builtin_zip, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_countOf, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_error_iter, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_exception_function, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_exception_locations, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_exception_sequence, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_extending_list_with_iterator_does_not_segfault, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_free_after_iterating, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_in_and_not_in, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_indexOf, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_basic, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_big_range, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_callable, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_class_for, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_class_iter, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_dict, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_empty, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_file, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_for_loop, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_function, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_function_concealing_reentrant_exhaustion, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_function_stop, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_idempotency, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_independence, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_neg_setstate, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_overflow, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_range, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_string, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_iter_tuple, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_mutating_seq_class_exhausted_iter, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_mutating_seq_class_iter_pickle, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_nested_comprehensions_for, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_nested_comprehensions_iter, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_new_style_iter_class, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_reduce_mutating_builtins_iter, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_ref_counting_behavior, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_seq_class_for, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_seq_class_iter, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_sinkstate_callable, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_sinkstate_dict, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_sinkstate_enumerate, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_sinkstate_list, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_sinkstate_range, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_sinkstate_sequence, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_sinkstate_string, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_sinkstate_tuple, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_sinkstate_yield, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_stop_sequence, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_unicode_join_endcase, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_unpack_iter, test/dynamo/cpython/3_13/test_iter.py::TestCase::test_writelines 2025-07-17T07:50:35.2326672Z 2025-07-17T07:50:36.2035675Z 2025-07-17T07:50:36.2037236Z dynamo/cpython/3_13/test_bool 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_bool_1.1_38c94d8fb17e92f1_.log 2025-07-17T07:50:36.2055676Z Running 31 items in this shard: test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_blocked, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_bool_called_at_least_once, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_bool_new, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_boolean, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_callable, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_complex, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_contains, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_convert, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_convert_to_bool, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_fileclosed, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_float, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_format, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_from_bytes, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_hasattr, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_int, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_interpreter_convert_to_bool_raises, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_isinstance, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_issubclass, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_keyword_args, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_marshal, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_math, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_operator, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_pickle, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_picklevalues, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_real_and_imag, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_repr, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_sane_len, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_str, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_string, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_subclass, test/dynamo/cpython/3_13/test_bool.py::BoolTest::test_types 2025-07-17T07:50:36.2071547Z 2025-07-17T07:50:38.2941539Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:38.2943128Z import pkg_resources 2025-07-17T07:50:38.4225411Z Running dynamo/cpython/3_13/test_sort 1/1 ... [2025-07-17 07:50:38.422203] 2025-07-17T07:50:38.4225978Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:38.4227806Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_sort.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'] ... [2025-07-17 07:50:38.422563] 2025-07-17T07:50:38.5568501Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:38.5570218Z import pkg_resources 2025-07-17T07:50:38.6819158Z Running dynamo/cpython/3_13/test_float 1/1 ... [2025-07-17 07:50:38.681586] 2025-07-17T07:50:38.6819688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:38.6821777Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_float.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'] ... [2025-07-17 07:50:38.681927] 2025-07-17T07:50:39.4553193Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:39.4554769Z import pkg_resources 2025-07-17T07:50:39.5885223Z Running dynamo/cpython/3_13/test_dict 1/1 ... [2025-07-17 07:50:39.588153] 2025-07-17T07:50:39.5885740Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:39.5888090Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_dict.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'] ... [2025-07-17 07:50:39.588545] 2025-07-17T07:50:41.9918060Z 2025-07-17T07:50:41.9919164Z dynamo/cpython/3_13/test_sort 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_sort_1.1_5c3537f76729b94d_.log 2025-07-17T07:50:41.9927866Z Running 21 items in this shard: test/dynamo/cpython/3_13/test_sort.py::TestBase::testStressfully, test/dynamo/cpython/3_13/test_sort.py::TestBase::test_small_stability, test/dynamo/cpython/3_13/test_sort.py::TestBugs::test_bug453523, test/dynamo/cpython/3_13/test_sort.py::TestBugs::test_undetected_mutation, test/dynamo/cpython/3_13/test_sort.py::TestDecorateSortUndecorate::test_baddecorator, test/dynamo/cpython/3_13/test_sort.py::TestDecorateSortUndecorate::test_decorated, test/dynamo/cpython/3_13/test_sort.py::TestDecorateSortUndecorate::test_key_with_exception, test/dynamo/cpython/3_13/test_sort.py::TestDecorateSortUndecorate::test_key_with_mutating_del, test/dynamo/cpython/3_13/test_sort.py::TestDecorateSortUndecorate::test_key_with_mutating_del_and_exception, test/dynamo/cpython/3_13/test_sort.py::TestDecorateSortUndecorate::test_key_with_mutation, test/dynamo/cpython/3_13/test_sort.py::TestDecorateSortUndecorate::test_reverse, test/dynamo/cpython/3_13/test_sort.py::TestDecorateSortUndecorate::test_reverse_stability, test/dynamo/cpython/3_13/test_sort.py::TestDecorateSortUndecorate::test_stability, test/dynamo/cpython/3_13/test_sort.py::TestOptimizedCompares::test_none_in_tuples, test/dynamo/cpython/3_13/test_sort.py::TestOptimizedCompares::test_not_all_tuples, test/dynamo/cpython/3_13/test_sort.py::TestOptimizedCompares::test_safe_object_compare, test/dynamo/cpython/3_13/test_sort.py::TestOptimizedCompares::test_unsafe_float_compare, test/dynamo/cpython/3_13/test_sort.py::TestOptimizedCompares::test_unsafe_latin_compare, test/dynamo/cpython/3_13/test_sort.py::TestOptimizedCompares::test_unsafe_long_compare, test/dynamo/cpython/3_13/test_sort.py::TestOptimizedCompares::test_unsafe_object_compare, test/dynamo/cpython/3_13/test_sort.py::TestOptimizedCompares::test_unsafe_tuple_compare 2025-07-17T07:50:41.9936056Z 2025-07-17T07:50:42.3514101Z 2025-07-17T07:50:42.3515849Z dynamo/cpython/3_13/test_float 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_float_1.1_b3f9b408cd4b3865_.log 2025-07-17T07:50:42.3550221Z Running 50 items in this shard: test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_error_message, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_float, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_float_ceil, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_float_containment, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_float_floor, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_float_memoryview, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_float_mod, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_float_pow, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_float_with_comma, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_floatasratio, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_floatconversion, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_hash, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_hash_nan, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_is_integer, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_keyword_args, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_keywords_in_subclass, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_noargs, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_non_numeric_input_types, test/dynamo/cpython/3_13/test_float.py::GeneralFloatCases::test_underscores, test/dynamo/cpython/3_13/test_float.py::FormatFunctionsTestCase::test_getformat, test/dynamo/cpython/3_13/test_float.py::IEEEFormatTestCase::test_double_specials_do_unpack, test/dynamo/cpython/3_13/test_float.py::IEEEFormatTestCase::test_float_specials_do_unpack, test/dynamo/cpython/3_13/test_float.py::IEEEFormatTestCase::test_serialized_float_rounding, test/dynamo/cpython/3_13/test_float.py::FormatTestCase::test_format, test/dynamo/cpython/3_13/test_float.py::FormatTestCase::test_format_testfile, test/dynamo/cpython/3_13/test_float.py::FormatTestCase::test_issue35560, test/dynamo/cpython/3_13/test_float.py::FormatTestCase::test_issue5864, test/dynamo/cpython/3_13/test_float.py::ReprTestCase::test_repr, test/dynamo/cpython/3_13/test_float.py::ReprTestCase::test_short_repr, test/dynamo/cpython/3_13/test_float.py::RoundTestCase::test_None_ndigits, test/dynamo/cpython/3_13/test_float.py::RoundTestCase::test_format_specials, test/dynamo/cpython/3_13/test_float.py::RoundTestCase::test_inf_nan, test/dynamo/cpython/3_13/test_float.py::RoundTestCase::test_inf_nan_ndigits, test/dynamo/cpython/3_13/test_float.py::RoundTestCase::test_large_n, test/dynamo/cpython/3_13/test_float.py::RoundTestCase::test_matches_float_format, test/dynamo/cpython/3_13/test_float.py::RoundTestCase::test_overflow, test/dynamo/cpython/3_13/test_float.py::RoundTestCase::test_previous_round_bugs, test/dynamo/cpython/3_13/test_float.py::RoundTestCase::test_small_n, test/dynamo/cpython/3_13/test_float.py::InfNanTest::test_inf_as_str, test/dynamo/cpython/3_13/test_float.py::InfNanTest::test_inf_from_str, test/dynamo/cpython/3_13/test_float.py::InfNanTest::test_inf_signs, test/dynamo/cpython/3_13/test_float.py::InfNanTest::test_nan_as_str, test/dynamo/cpython/3_13/test_float.py::InfNanTest::test_nan_from_str, test/dynamo/cpython/3_13/test_float.py::InfNanTest::test_nan_signs, test/dynamo/cpython/3_13/test_float.py::HexFloatTestCase::test_ends, test/dynamo/cpython/3_13/test_float.py::HexFloatTestCase::test_from_hex, test/dynamo/cpython/3_13/test_float.py::HexFloatTestCase::test_invalid_inputs, test/dynamo/cpython/3_13/test_float.py::HexFloatTestCase::test_roundtrip, test/dynamo/cpython/3_13/test_float.py::HexFloatTestCase::test_subclass, test/dynamo/cpython/3_13/test_float.py::HexFloatTestCase::test_whitespace 2025-07-17T07:50:42.3583546Z 2025-07-17T07:50:45.3642585Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:45.3644160Z import pkg_resources 2025-07-17T07:50:45.4914940Z Running dynamo/cpython/3_13/test_complex 1/1 ... [2025-07-17 07:50:45.491175] 2025-07-17T07:50:45.4915481Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:45.4917686Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_complex.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'] ... [2025-07-17 07:50:45.491536] 2025-07-17T07:50:45.6399975Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:45.6401551Z import pkg_resources 2025-07-17T07:50:45.7651166Z Running dynamo/cpython/3_13/test_tuple 1/1 ... [2025-07-17 07:50:45.764789] 2025-07-17T07:50:45.7651664Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:45.7653788Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/cpython/3_13/test_tuple.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'] ... [2025-07-17 07:50:45.765132] 2025-07-17T07:50:48.9605906Z 2025-07-17T07:50:48.9606939Z dynamo/cpython/3_13/test_complex 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_complex_1.1_c086c58cfc56e1df_.log 2025-07-17T07:50:48.9619137Z Running 35 items in this shard: test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test___complex__, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_abs, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_add, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_boolcontext, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_conjugate, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_constructor, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_constructor_from_string, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_constructor_negative_nans_from_string, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_constructor_special_numbers, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_divmod, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_divmod_zero_division, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_floordiv, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_floordiv_zero_division, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_format, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_getnewargs, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_hash, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_mod, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_mod_zero_division, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_mul, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_neg, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_negated_imaginary_literal, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_negative_zero_repr_str, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_overflow, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_plus_minus_0j, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_pos, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_pow, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_pow_with_small_integer_exponents, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_repr_roundtrip, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_repr_str, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_richcompare, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_richcompare_boundaries, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_sub, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_truediv, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_truediv_zero_division, test/dynamo/cpython/3_13/test_complex.py::ComplexTest::test_underscores 2025-07-17T07:50:48.9630422Z 2025-07-17T07:50:52.2088233Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:50:52.2089825Z import pkg_resources 2025-07-17T07:50:52.3340269Z Running test_package 1/1 ... [2025-07-17 07:50:52.333741] 2025-07-17T07:50:52.3340719Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:50:52.3343062Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_package.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'] ... [2025-07-17 07:50:52.334084] 2025-07-17T07:50:57.8060245Z 2025-07-17T07:50:57.8061172Z test_package 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_package_1.1_3bc94131a26b111b_.log 2025-07-17T07:50:57.8098676Z Running 137 items in this shard: test/test_package.py::TestAnalyze::test_trace_dependencies, test/test_package.py::TestDependencyAPI::test_allow_empty_with_error, test/test_package.py::TestDependencyAPI::test_broken_dependency, test/test_package.py::TestDependencyAPI::test_deny, test/test_package.py::TestDependencyAPI::test_deny_glob, test/test_package.py::TestDependencyAPI::test_extern, test/test_package.py::TestDependencyAPI::test_extern_glob, test/test_package.py::TestDependencyAPI::test_extern_glob_allow_empty, test/test_package.py::TestDependencyAPI::test_externing_c_extension, test/test_package.py::TestDependencyAPI::test_implicit_intern, test/test_package.py::TestDependencyAPI::test_intern_error, test/test_package.py::TestDependencyAPI::test_invalid_import, test/test_package.py::TestDependencyAPI::test_mock, test/test_package.py::TestDependencyAPI::test_mock_glob, test/test_package.py::TestDependencyAPI::test_mock_glob_allow_empty, test/test_package.py::TestDependencyAPI::test_pickle_mocked, test/test_package.py::TestDependencyAPI::test_pickle_mocked_all, test/test_package.py::TestDependencyAPI::test_repackage_mocked_module, test/test_package.py::TestDependencyHooks::test_extern_and_mock_hook, test/test_package.py::TestDependencyHooks::test_multiple_extern_hooks, test/test_package.py::TestDependencyHooks::test_multiple_mock_hooks, test/test_package.py::TestDependencyHooks::test_remove_hooks, test/test_package.py::TestDependencyHooks::test_single_hook, test/test_package.py::TestDiGraph::test_all_paths, test/test_package.py::TestDiGraph::test_contains, test/test_package.py::TestDiGraph::test_contains_non_hashable, test/test_package.py::TestDiGraph::test_edges, test/test_package.py::TestDiGraph::test_forward_closure, test/test_package.py::TestDiGraph::test_iter, test/test_package.py::TestDiGraph::test_node_attr_update, test/test_package.py::TestDiGraph::test_node_attrs, test/test_package.py::TestDiGraph::test_predecessor_not_in_graph, test/test_package.py::TestDiGraph::test_predecessors, test/test_package.py::TestDiGraph::test_successor_not_in_graph, test/test_package.py::TestDiGraph::test_successors, test/test_package.py::DirectoryReaderTest::test_importer_access, test/test_package.py::DirectoryReaderTest::test_loading_has_record, test/test_package.py::DirectoryReaderTest::test_loading_module, test/test_package.py::DirectoryReaderTest::test_loading_pickle, test/test_package.py::DirectoryReaderTest::test_package_resource_access, test/test_package.py::DirectoryReaderTest::test_resource_access_by_path, test/test_package.py::DirectoryReaderTest::test_resource_reader, test/test_package.py::DirectoryReaderTest::test_scriptobject_failure_message, test/test_package.py::TestGlobGroup::test_exclude, test/test_package.py::TestGlobGroup::test_exclude_from_all, test/test_package.py::TestGlobGroup::test_invalid_raw, test/test_package.py::TestGlobGroup::test_list_include_exclude, test/test_package.py::TestGlobGroup::test_one_star, test/test_package.py::TestGlobGroup::test_one_star_middle, test/test_package.py::TestGlobGroup::test_one_star_multiple_in_component, test/test_package.py::TestGlobGroup::test_one_star_partial, test/test_package.py::TestGlobGroup::test_one_star_partial_extension, test/test_package.py::TestGlobGroup::test_raw_two_star, test/test_package.py::TestGlobGroup::test_two_star, test/test_package.py::TestGlobGroup::test_two_star_end, test/test_package.py::TestGlobGroup::test_two_star_middle, test/test_package.py::TestGlobGroup::test_two_star_multiple, test/test_package.py::TestImporter::test_ordered_importer_basic, test/test_package.py::TestImporter::test_ordered_importer_whichmodule, test/test_package.py::TestImporter::test_package_importer_whichmodule_no_dunder_module, test/test_package.py::TestImporter::test_single_ordered_importer, test/test_package.py::TestImporter::test_sys_importer, test/test_package.py::TestImporter::test_sys_importer_roundtrip, test/test_package.py::TestLoadBCPackages::test_load_bc_packages_fx_module, test/test_package.py::TestLoadBCPackages::test_load_bc_packages_nn_module, test/test_package.py::TestLoadBCPackages::test_load_bc_packages_torchscript_module, test/test_package.py::TestMangling::test_demangle_base, test/test_package.py::TestMangling::test_demangler_multiple_manglers, test/test_package.py::TestMangling::test_is_mangled, test/test_package.py::TestMangling::test_mangle_empty_errors, test/test_package.py::TestMangling::test_mangle_prefix, test/test_package.py::TestMangling::test_mangler_is_consistent, test/test_package.py::TestMangling::test_package_mangler, test/test_package.py::TestMangling::test_roundtrip_mangling, test/test_package.py::TestMangling::test_unique_manglers, test/test_package.py::TestMangling::test_unique_module_names, test/test_package.py::TestMisc::test_dunder_package_present, test/test_package.py::TestMisc::test_dunder_package_works_from_package, test/test_package.py::TestMisc::test_exporter_content_lists, test/test_package.py::TestMisc::test_file_structure, test/test_package.py::TestMisc::test_file_structure_has_file, test/test_package.py::TestMisc::test_inspect_class, test/test_package.py::TestMisc::test_is_from_package, test/test_package.py::TestMisc::test_load_python_version_from_package, test/test_package.py::TestMisc::test_loaders_that_remap_files_work_ok, test/test_package.py::TestMisc::test_python_version, test/test_package.py::TestMisc::test_std_lib_sys_hackery_checks, test/test_package.py::ModelTest::test_model_save, test/test_package.py::ModelTest::test_resnet, test/test_package.py::ModelTest::test_script_resnet, test/test_package.py::TestPackageFX::test_package_fx_custom_tracer, test/test_package.py::TestPackageFX::test_package_fx_package, test/test_package.py::TestPackageFX::test_package_fx_simple, test/test_package.py::TestPackageFX::test_package_fx_with_imports, test/test_package.py::TestPackageFX::test_package_fx_wrap, test/test_package.py::TestPackageFX::test_package_gm_preserve_stack_trace, test/test_package.py::TestPackageFX::test_package_then_fx, test/test_package.py::TestPackageScript::test_different_package_interface, test/test_package.py::TestPackageScript::test_different_package_script_class, test/test_package.py::TestPackageScript::test_load_shared_scriptmodules, test/test_package.py::TestPackageScript::test_load_shared_tensors, test/test_package.py::TestPackageScript::test_load_shared_tensors_repackaged, test/test_package.py::TestPackageScript::test_mixing_packaged_and_inline_modules, test/test_package.py::TestPackageScript::test_mixing_packaged_and_inline_modules_shared_code, test/test_package.py::TestPackageScript::test_package_interface, test/test_package.py::TestPackageScript::test_package_script_class, test/test_package.py::TestPackageScript::test_package_script_class_referencing_self, test/test_package.py::TestPackageScript::test_save_eager_mods_sharing_scriptmodule, test/test_package.py::TestPackageScript::test_save_independent_scriptmodules, test/test_package.py::TestPackageScript::test_save_repeat_scriptmodules, test/test_package.py::TestPackageScript::test_save_scriptmodule, test/test_package.py::TestPackageScript::test_save_scriptmodule_file, test/test_package.py::TestPackageScript::test_save_scriptmodule_only_necessary_code, test/test_package.py::TestPackageScript::test_save_scriptmodule_with_submods, test/test_package.py::TestPackageScript::test_save_scriptmodules_in_container, test/test_package.py::TestPackageScript::test_save_scriptmodules_submod_redefinition, test/test_package.py::TestPackageScript::test_save_shared_tensors, test/test_package.py::TestPackageScript::test_saving_and_scripting_packaged_mod, test/test_package.py::TestPackageScript::test_scriptmodules_repeat_save, test/test_package.py::TestPackageScript::test_tensor_sharing_pickle, test/test_package.py::TestRepackage::test_repackage_import_indirectly_via_parent_module, test/test_package.py::TestResources::test_importer_access, test/test_package.py::TestResources::test_package_resource_access, test/test_package.py::TestResources::test_resource_access_by_path, test/test_package.py::TestResources::test_resource_reader, test/test_package.py::TestSaveLoad::test_bad_dunder_imports, test/test_package.py::TestSaveLoad::test_dunder_imports, test/test_package.py::TestSaveLoad::test_exporting_mismatched_code, test/test_package.py::TestSaveLoad::test_pickle, test/test_package.py::TestSaveLoad::test_pickle_long_name_with_protocol_4, test/test_package.py::TestSaveLoad::test_save_imported_module, test/test_package.py::TestSaveLoad::test_save_imported_module_using_package_importer, test/test_package.py::TestSaveLoad::test_save_load_fp8, test/test_package.py::TestSaveLoad::test_save_module, test/test_package.py::TestSaveLoad::test_save_module_binary, test/test_package.py::TestSaveLoad::test_saving_source, test/test_package.py::TestSaveLoad::test_saving_string 2025-07-17T07:50:57.8135353Z 2025-07-17T07:50:58.1288143Z 2025-07-17T07:50:58.1289265Z dynamo/cpython/3_13/test_dict 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_dict_1.1_5cc80ac6c8fc6b0e_.log 2025-07-17T07:50:58.1325166Z Running 112 items in this shard: test/dynamo/cpython/3_13/test_dict.py::DictTest::test_bad_key, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_bool, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_clear, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_constructor, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_container_iterator, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_contains, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_copy, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_copy_fuzz, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_copy_maintains_tracking, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_copy_noncompact, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_dict_contain_use_after_free, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_dict_copy_order, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_dict_items_result_gc, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_dict_items_result_gc_reversed, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_dictitems_contains_use_after_free, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_dictview_mixed_set_operations, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_dictview_set_operations_on_items, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_dictview_set_operations_on_keys, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_empty_presized_dict_in_freelist, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_eq, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_equal_operator_modifying_operand, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_errors_in_view_containment_check, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_free_after_iterating, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_fromkeys, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_fromkeys_operator_modifying_dict_operand, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_fromkeys_operator_modifying_set_operand, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_get, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_getitem, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_init_use_after_free, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_instance_dict_getattr_str_subclass, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_invalid_keyword_arguments, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_itemiterator_pickling, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_items, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_items_symmetric_difference, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_iterator_pickling, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_keys, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_keys_contained, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_len, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_literal_constructor, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_merge_and_mutate, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_merge_operator, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_missing, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_mutating_iteration, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_mutating_iteration_delete, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_mutating_iteration_delete_over_items, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_mutating_iteration_delete_over_values, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_mutating_lookup, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_object_set_item_single_instance_non_str_key, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_oob_indexing_dictiter_iternextitem, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_pop, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_popitem, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_reentrant_insertion, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_repr, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_repr_deep, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_resize1, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_resize2, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_reverse_iterator_for_empty_dict, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_reverse_iterator_for_shared_shared_dicts, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_reversed, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_reverseitemiterator_pickling, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_reverseiterator_pickling, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_reversevaluesiterator_pickling, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_setdefault, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_setdefault_atomic, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_setitem_atomic_at_resize, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_splittable_del, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_splittable_pop, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_splittable_pop_pending, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_splittable_popitem, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_splittable_setdefault, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_splittable_to_generic_combinedtable, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_splittable_update, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_store_evilattr, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_str_nonstr, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_string_keys_can_track_values, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_track_dynamic, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_track_literals, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_track_subtypes, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_tuple_keyerror, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_update, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_values, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_valuesiterator_pickling, test/dynamo/cpython/3_13/test_dict.py::DictTest::test_views_mapping, test/dynamo/cpython/3_13/test_dict.py::CAPITest::test_getitem_knownhash, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_bool, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_constructor, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_get, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_getitem, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_items, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_keys, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_len, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_pop, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_popitem, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_read, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_setdefault, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_update, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_values, test/dynamo/cpython/3_13/test_dict.py::GeneralMappingTests::test_write, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_bool, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_constructor, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_get, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_getitem, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_items, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_keys, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_len, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_pop, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_popitem, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_read, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_setdefault, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_update, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_values, test/dynamo/cpython/3_13/test_dict.py::SubclassMappingTests::test_write 2025-07-17T07:50:58.1359707Z 2025-07-17T07:51:01.0480861Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:51:01.0482469Z import pkg_resources 2025-07-17T07:51:01.1741204Z Running inductor/test_provenance_tracing 1/1 ... [2025-07-17 07:51:01.173749] 2025-07-17T07:51:01.1741749Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:51:01.1743384Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_provenance_tracing.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'] ... [2025-07-17 07:51:01.174092] 2025-07-17T07:51:01.3766493Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:51:01.3768076Z import pkg_resources 2025-07-17T07:51:01.5022619Z Running test_decomp 1/10 ... [2025-07-17 07:51:01.501933] 2025-07-17T07:51:01.5023040Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:51:01.5025476Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_decomp.py', '-m', 'not serial', '--shard-id=1', '--num-shards=10', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:51:01.502296] 2025-07-17T07:51:15.7102063Z 2025-07-17T07:51:15.7103799Z inductor/test_provenance_tracing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_provenance_tracing_1.1_f551b1c190d16785_.log 2025-07-17T07:51:15.7107313Z Running 5 items in this shard: test/inductor/test_provenance_tracing.py::TestProvenanceTracingArtifact::test_triton_kernel_to_post_grad_tracing_combo_kernel, test/inductor/test_provenance_tracing.py::TestProvenanceTracingArtifact::test_triton_kernel_to_post_grad_tracing_cpu, test/inductor/test_provenance_tracing.py::TestProvenanceTracingArtifact::test_triton_kernel_to_post_grad_tracing_cuda, test/inductor/test_provenance_tracing.py::TestProvenanceTracingArtifact::test_triton_kernel_to_post_grad_tracing_extern_kernel, test/inductor/test_provenance_tracing.py::TestProvenanceTracingNodeMapping::test_create_node_mapping 2025-07-17T07:51:15.7110147Z 2025-07-17T07:51:16.8228835Z 2025-07-17T07:51:16.8230652Z dynamo/cpython/3_13/test_tuple 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.cpython.3_13.test_tuple_1.1_a94d4d17fd38ba0b_.log 2025-07-17T07:51:16.8253076Z Running 36 items in this shard: test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_addmul, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_bigrepeat, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_bug7466, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_constructors, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_contains, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_contains_fake, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_contains_order, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_count, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_free_after_iterating, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_getitem, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_getitem_error, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_getitemoverwriteiter, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_getslice, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_hash_exact, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_hash_optional, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_iadd, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_imul, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_index, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_iterator_pickle, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_keyword_args, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_keywords_in_subclass, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_len, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_lexicographic_ordering, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_minmax, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_no_comdat_folding, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_pickle, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_repeat, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_repr, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_repr_large, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_reversed_pickle, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_subscript, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_track_dynamic, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_track_literals, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_track_subtypes, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_truth, test/dynamo/cpython/3_13/test_tuple.py::TupleTest::test_tupleresizebug 2025-07-17T07:51:16.8274811Z 2025-07-17T07:51:18.8638447Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:51:18.8641191Z import pkg_resources 2025-07-17T07:51:18.9867437Z Running test_ops 2/6 ... [2025-07-17 07:51:18.986403] 2025-07-17T07:51:18.9868125Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:51:18.9870277Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'not serial', '--shard-id=2', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:51:18.986742] 2025-07-17T07:51:19.9042766Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T07:51:19.9044342Z import pkg_resources 2025-07-17T07:51:20.0270791Z Running test_ops 3/6 ... [2025-07-17 07:51:20.026752] 2025-07-17T07:51:20.0271305Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T07:51:20.0273197Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'not serial', '--shard-id=3', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 07:51:20.027078] 2025-07-17T08:00:54.5606902Z 2025-07-17T08:00:54.5608110Z test_ops 3/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_3.6_4cdcd1139c9dbe97_.log 2025-07-17T08:00:54.9335089Z Running 5779 items in this shard: test/test_ops.py::TestCommonCPU::test_compare_cpu___getitem___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu___ror___cpu_int64, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_bfloat16_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_int_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_short_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_as_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_chunk_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_div_no_rounding_mode_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_eye_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_fft_ifftshift_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_hypot_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_linspace_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_log_normal_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_logsumexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_narrow_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_new_empty_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_new_zeros_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_alpha_dropout_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_reshape_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_sum_to_size_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_t_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_trace_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_tril_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_vsplit_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_addcmul_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_addr_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_atan2_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_atleast_1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_atleast_2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_atleast_3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_bucketize_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_char_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cholesky_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_copysign_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_corrcoef_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cummin_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cumprod_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_div_floor_rounding_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_dstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_expand_as_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_full_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_index_reduce_amin_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_istft_cpu_complex64, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_det_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_norm_subgradients_at_zero_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_svd_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_log_normal_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_logdet_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_mH_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_mT_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_masked_cumprod_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_mv_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_new_ones_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_alpha_dropout_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_ctc_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_max_unpool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_normal_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_pinverse_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_scalar_tensor_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_svd_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_svd_lowrank_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_to_sparse_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_topk_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_trace_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_trapz_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_unique_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_vdot_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_vsplit_cpu_float32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing___getitem___cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing__chunk_cat_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_as_strided_scatter_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_atleast_3d_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_cat_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_chunk_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_clone_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_fft_fftshift_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_isfinite_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_isreal_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_item_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_linalg_diagonal_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_log_softmax_with_dtype_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_mH_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_narrow_copy_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_new_zeros_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_repeat_interleave_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_reshape_as_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_reshape_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_roll_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_sgn_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_split_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_split_with_sizes_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_stack_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_sub_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_unsqueeze_copy_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_where_cpu_complex32, test/test_ops.py::TestCommonCPU::test_dtypes___rmatmul___cpu, test/test_ops.py::TestCommonCPU::test_dtypes___ror___cpu, test/test_ops.py::TestCommonCPU::test_dtypes___rpow___cpu, test/test_ops.py::TestCommonCPU::test_dtypes___rsub___cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_bfloat16_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_char_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_float_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_half_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_long_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_addr_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_arange_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_asinh_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_bitwise_and_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_clamp_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_conj_physical_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_constant_pad_nd_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_diag_embed_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_div_no_rounding_mode_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_dstack_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_empty_like_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_equal_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_erf_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_expand_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_fftshift_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_ifft2_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_ifftshift_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_frexp_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_ge_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_hypot_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_isfinite_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_lgamma_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_log_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_meshgrid_list_of_tensors_cpu, 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test/test_ops.py::TestCommonCPU::test_multiple_devices_addbmm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_all_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_all_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_allclose_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_argmax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_argwhere_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_bfloat16_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cdist_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_clone_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_combinations_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_constant_pad_nd_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_contiguous_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_copysign_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumprod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_diag_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_diff_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_dist_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_einsum_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_equal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_exp2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_exp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_expand_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_exponential_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_eye_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_irfftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_irfftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fliplr_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fmod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_grid_sampler_2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_gt_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_half_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_histogramdd_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_hstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_igammac_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_mean_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_isclose_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_isinf_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_binary_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_kron_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_ldexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ldexp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cholesky_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cond_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_det_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_diagonal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_eigh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_matrix_power_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_multi_dot_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linspace_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_and_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logit_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logspace_tensor_overload_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_cumsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_logaddexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_scatter_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_std_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_matmul_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_meshgrid_variadic_tensors_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_minimum_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mode_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mul_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nanmean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_neg_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_full_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_zeros_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_zeros_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cross_entropy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_gaussian_nll_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_glu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_normalize_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pairwise_distance_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softplus_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softsign_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_triplet_margin_with_distance_loss_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nonzero_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_norm_inf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_2_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_randint_like_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_ravel_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_real_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_real_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_reciprocal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_remainder_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_resize__cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_resize__cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_rot90_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_rot90_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_rsqrt_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_scalar_tensor_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_select_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_slice_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_slice_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_bessel_y0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_erfcx_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_hermite_polynomial_h_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_i0e_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_i1e_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_laguerre_polynomial_l_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_legendre_polynomial_p_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_log_ndtr_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_t_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_shifted_chebyshev_polynomial_v_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_spherical_bessel_j0_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_zeta_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sqrt_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_std_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_sub_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_t_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_take_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_tanh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_tile_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_transpose_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_trapz_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_unfold_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsafe_chunk_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsafe_split_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsafe_split_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_var_mean_unbiased_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_view_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_view_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_vstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_zero__cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_zeros_cpu_float32, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_H_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_T_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values___rand___cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values__chunk_cat_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_alias_copy_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_as_strided_scatter_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_asin_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_atan_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_block_diag_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_broadcast_tensors_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_cartesian_prod_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_cdouble_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_column_stack_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_cummax_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_deg2rad_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_diagonal_copy_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_diagonal_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_exp2_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_exp_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_ifft2_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fill_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fliplr_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_float_power_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_full_like_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_half_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_i0_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_isinf_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_linalg_diagonal_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_log10_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_log_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_logical_and_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_masked_scatter_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_meshgrid_variadic_tensors_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_movedim_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_new_full_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_new_zeros_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_permute_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_put_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_rad2deg_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_roll_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_rsqrt_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_short_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_sin_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_legendre_polynomial_p_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_modified_bessel_k0_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_shifted_chebyshev_polynomial_v_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_shifted_chebyshev_polynomial_w_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_sum_to_size_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_take_along_dim_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_to_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_torch_ops_aten__safe_softmax_default_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_transpose_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_tril_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_unbind_copy_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_unique_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_where_cpu_bool, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___getitem___cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rdiv___cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rmod___cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples___rsub___cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples__unsafe_masked_index_put_accumulate_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_acos_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_acosh_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_acosh_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_addmv_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_alias_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_all_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_argsort_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_argwhere_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_as_strided_partial_views_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_as_strided_partial_views_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atan2_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atan_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atan_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_atleast_1d_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_baddbmm_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_bfloat16_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_bitwise_and_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_bmm_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_bool_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_bool_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_broadcast_shapes_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_broadcast_tensors_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_broadcast_to_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_byte_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_cdouble_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_ceil_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_cfloat_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_char_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_char_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_chunk_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_contiguous_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_contiguous_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_copysign_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_cos_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_count_nonzero_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_cummin_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_cumprod_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_cumprod_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_cumsum_cpu_complex64, 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test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_conv_transpose1d_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_gelu_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_huber_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_local_response_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_local_response_norm_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_circular_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_constant_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_replicate_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pixel_unshuffle_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_rrelu_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_silu_complex_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_tanhshrink_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_tanhshrink_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_threshold_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_unfold_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_upsample_nearest_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nonzero_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nonzero_static_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_norm_fro_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_norm_inf_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_norm_nuc_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_normal_in_place_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_permute_copy_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_pinverse_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_pinverse_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_polar_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_polygamma_polygamma_n_1_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_polygamma_polygamma_n_2_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_polygamma_polygamma_n_4_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_quantile_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_rad2deg_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_randint_like_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_randn_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_reciprocal_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_remainder_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_repeat_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_repeat_interleave_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_reshape_as_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_resize__cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_resize_as__cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_roll_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_rsqrt_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_rsub_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_scalar_tensor_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_signbit_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sin_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_softmax_with_dtype_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sort_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_bessel_j1_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_chebyshev_polynomial_u_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_entr_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_ndtri_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_scaled_modified_bessel_k1_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_special_zeta_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_squeeze_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_stack_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sub_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sum_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_sum_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_svd_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_svd_lowrank_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_t_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_t_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_tensor_split_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_tensordot_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_tensordot_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_to_sparse_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_topk_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_topk_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_transpose_copy_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_tril_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_triu_indices_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_true_divide_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_true_divide_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_trunc_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_trunc_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_unbind_copy_cpu_complex64, 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test/test_ops.py::TestCommonCPU::test_numpy_ref_nn_functional_conv_transpose2d_cpu_float64, test/test_ops.py::TestCommonCPU::test_numpy_ref_nn_functional_conv_transpose3d_cpu_float64, test/test_ops.py::TestCommonCPU::test_numpy_ref_nn_functional_pairwise_distance_cpu_float64, test/test_ops.py::TestCommonCPU::test_numpy_ref_permute_cpu_complex128, test/test_ops.py::TestCommonCPU::test_numpy_ref_permute_cpu_int64, test/test_ops.py::TestCommonCPU::test_numpy_ref_repeat_cpu_int64, test/test_ops.py::TestCommonCPU::test_numpy_ref_signal_windows_bartlett_cpu_float64, test/test_ops.py::TestCommonCPU::test_numpy_ref_signal_windows_exponential_cpu_float64, test/test_ops.py::TestCommonCPU::test_numpy_ref_signal_windows_gaussian_cpu_float64, test/test_ops.py::TestCommonCPU::test_numpy_ref_signal_windows_kaiser_cpu_float64, test/test_ops.py::TestCommonCPU::test_numpy_ref_squeeze_multiple_cpu_complex128, test/test_ops.py::TestCommonCPU::test_numpy_ref_where_cpu_float64, 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test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_positive_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_qr_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_rad2deg_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_resize_as__cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_resolve_neg_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signal_windows_bartlett_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signal_windows_hamming_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signal_windows_hann_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_slice_scatter_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_bessel_j1_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_bessel_y1_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_erfcx_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_xlog1py_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_split_list_args_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_sqrt_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_std_unbiased_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_sum_to_size_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_svd_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_svd_lowrank_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_tanh_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_trapz_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unflatten_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unique_consecutive_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unsqueeze_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_var_mean_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_view_as_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_view_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_zeros_like_cpu_float32, test/test_ops.py::TestMathBitsCPU::test_conj_view___rsub___cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs__conversions_bfloat16_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs__conversions_long_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs__conversions_short_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_alias_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_atleast_3d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_count_nonzero_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_diagonal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_empty_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_empty_strided_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_expand_as_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_fftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_fftshift_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_hfft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_ifftshift_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_irfft2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_irfft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_irfftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_index_add_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_index_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_isfinite_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_isinf_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_linalg_diagonal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_linalg_vecdot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_logical_xor_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_pow_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_prod_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_ravel_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_special_softmax_with_dtype_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_squeeze_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_squeeze_multiple_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_std_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_std_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_tensor_split_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_tril_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_view_as_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_vstack_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_alias_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_all_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_allclose_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_as_strided_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_block_diag_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_byte_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_cholesky_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_diagflat_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_empty_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_equal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_exp2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_exp_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_expand_as_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_expand_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_fftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_ifft2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_flatten_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_float_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_geqrf_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_isclose_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_isreal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_4inputs_with_extra_args_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_cholesky_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_eigvals_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_eigvalsh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_inv_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_lu_factor_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_norm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_pinv_singular_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_slogdet_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_solve_ex_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_svdvals_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_tensorsolve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_vector_norm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_log10_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_log2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_logspace_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_select_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_sum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_meshgrid_variadic_tensors_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_mul_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_narrow_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_narrow_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_new_empty_strided_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_new_full_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv2d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv3d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_pixel_shuffle_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nonzero_static_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_ones_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_permute_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_qr_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_randn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_repeat_interleave_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_rsub_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_short_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_split_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_sqrt_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tanh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_trace_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_transpose_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_transpose_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_triangular_solve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unsqueeze_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unsqueeze_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_var_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_var_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_var_mean_unbiased_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_where_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_zeros_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___getitem___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__chunk_cat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_byte_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_short_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_acos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_acosh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_addcdiv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_addcmul_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_all_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_atleast_2d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cosh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_div_no_rounding_mode_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_dot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_expand_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_eye_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_hfft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_flatten_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_flip_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_index_select_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_svd_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_svdvals_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_log2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logical_not_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_meshgrid_list_of_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_new_empty_strided_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_new_zeros_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_ones_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_permute_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_reshape_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_roll_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_rsub_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sinh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_square_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_squeeze_multiple_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_tanh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_tensor_split_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_transpose_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_true_divide_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unbind_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unsqueeze_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__unsafe_masked_index_put_accumulate_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_acos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addmv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_all_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_as_strided_partial_views_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_asin_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_block_diag_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_broadcast_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_byte_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_chalf_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cholesky_solve_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cos_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cov_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_cross_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_div_no_rounding_mode_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_dsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_dstack_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_einsum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_empty_strided_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_expand_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_expand_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_flipud_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_gather_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_hsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_inner_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_int_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_jiterator_binary_return_by_ref_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_kron_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_lerp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_cond_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_diagonal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_inv_ex_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lstsq_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lstsq_grad_oriented_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lu_solve_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_slogdet_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_vander_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logspace_tensor_overload_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_lu_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_logsumexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_normalize_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_std_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_mul_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_conv2d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_conv_transpose1d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_l1_loss_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_pad_replicate_negative_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_real_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_repeat_interleave_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_reshape_as_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sin_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sqrt_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_square_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_svd_lowrank_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_tensor_split_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_trapz_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_var_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_vdot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_view_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_vsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_view_T_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__chunk_cat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__native_batch_norm_legit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_char_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_as_strided_partial_views_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_asinh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atan2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atleast_2d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_block_diag_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cauchy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_constant_pad_nd_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cumprod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_diagonal_scatter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_dsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_eq_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_fft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ihfft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_irfft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_irfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_rfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_frac_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_index_select_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_svdvals_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_vecdot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logaddexp2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_meshgrid_list_of_tensors_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_native_layer_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_neg_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_new_empty_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_new_ones_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_celu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_pixel_shuffle_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_prelu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_round_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_signbit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sinc_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_bessel_j0_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_i1e_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_log_softmax_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_logit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_multigammaln_mvlgamma_p_5_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_ndtr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_squeeze_multiple_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_stack_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_stft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_tan_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_true_divide_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_view_as_complex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_vsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__unsafe_masked_index_put_accumulate_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_addmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_block_diag_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_bmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_bucketize_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cauchy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_ceil_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_chalf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cholesky_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_complex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_count_nonzero_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cummin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cumsum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_diagonal_scatter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_empty_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_expm1_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_fft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_fftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_hfft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_hfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_ifft_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_ifftshift_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_flip_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fmax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_full_like_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_gather_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_geometric_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_histc_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_index_fill_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_inner_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_isposinf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_isreal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_le_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_det_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_diagonal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_eig_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_lu_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_matrix_power_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_matrix_rank_hermitian_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_norm_subgradients_at_zero_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_vecdot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_logspace_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_lu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_amax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_cumprod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_log_softmax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_prod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_softmin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_var_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_max_reduction_no_dim_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_meshgrid_list_of_tensors_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_minimum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_msort_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_mv_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_narrow_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_dropout3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_fractional_max_pool3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_gaussian_nll_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_instance_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_interpolate_linear_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_interpolate_nearest_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_local_response_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_logsigmoid_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_pool3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_unpool1d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_unpool3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_unpool3d_grad_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_pad_reflect_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_pad_replicate_negative_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_pixel_shuffle_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_rrelu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_selu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_normal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_ones_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_ormqr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_outer_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_randint_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_randn_like_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_repeat_interleave_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_resolve_neg_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_scalar_tensor_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_signal_windows_bartlett_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_signal_windows_cosine_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_signal_windows_general_cosine_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_sinh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_slice_scatter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_sparse_sampled_addmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_special_bessel_j0_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_special_legendre_polynomial_p_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_split_with_sizes_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_sqrt_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_tan_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_tanh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_triangular_solve_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_unfold_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_unsafe_chunk_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_unsafe_split_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_var_mean_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_vdot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_view_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_xlogy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_zero__cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_zeros_like_cpu_float64, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_T_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_bfloat16_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_bool_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_chalf_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_double_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_long_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_acosh__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_add__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_asinh__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_atan2__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_atan2_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_bitwise_left_shift_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_bitwise_or_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_bitwise_right_shift__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_ceil_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_clamp_min__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_conj_physical__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_copy_to_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_copysign__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_cos_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_cumprod__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_cumsum_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_diag_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_diagonal_scatter_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_dot_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_erfinv_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_exp2__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_exponential__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fft_fft2_cpu, 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test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_softplus_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_threshold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nonzero_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_normal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_randint_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_randn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_resolve_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_round_decimals_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_short_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_bessel_y0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_entr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_xlog1py_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_split_list_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_stack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sum_to_size_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_t_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_tensordot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_tile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_torch__scaled_mm_cpu_float8_e4m3fn, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_view_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_view_as_real_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_zeros_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_bincount_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_bitwise_or_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_bmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cdouble_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_ceil_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_clone_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_column_stack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_constant_pad_nd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cross_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp__native_batch_norm_legit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp__softmax_backward_data_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp__unsafe_masked_index_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_addmm_decomposed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_as_strided_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_asin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_atleast_2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_block_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_broadcast_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_clamp_max_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_clamp_min_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_clone_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cov_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cummax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_diagflat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_div_no_rounding_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_einsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_expand_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_rfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_floor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_frexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_gather_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_gradient_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_kron_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_kthvalue_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_qr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_vander_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_vecdot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_log_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_logaddexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_max_binary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_native_dropout_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_glu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_rms_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_norm_fro_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_round_decimals_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_i1e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_ndtri_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_square_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sum_to_size_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_t_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_to_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_trapz_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_tril_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unbind_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unfold_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unsafe_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_var_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rdiv___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rmod___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__softmax_backward_data_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__unsafe_masked_index_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__upsample_bilinear2d_aa_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addmv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_as_strided_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_as_strided_partial_views_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_asinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_atleast_2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_atleast_3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_clamp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_clamp_min_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_copysign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diag_embed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diagonal_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_expand_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_expand_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_irfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_rfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_frexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_hsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_hypot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_index_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_index_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_cond_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_cross_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lu_factor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_svdvals_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_minimum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_fractional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_hardsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_silu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_upsample_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_norm_inf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_norm_nuc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_permute_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_permute_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_repeat_interleave_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_round_decimals_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_rsub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sinc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_slice_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_squeeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trapz_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_trunc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unflatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_var_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cummax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diagflat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_dot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_dstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_empty_permuted_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_eq_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_erfinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_exp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_expand_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_fft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_rfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_rfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_float_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_full_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_gather_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_gcd_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_geometric_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_geqrf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_gradient_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_hypot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_imag_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_index_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_isinf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_jiterator_2inputs_2outputs_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_jiterator_unary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_le_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_eig_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_eigvals_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_inv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_ldl_factor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_ldl_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_matrix_rank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_slogdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_solve_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_log10_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logical_and_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logical_xor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_msort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_multinomial_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nan_to_num_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_narrow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_new_zeros_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_alpha_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_embedding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_gelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_mish_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_one_hot_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pad_replicate_negative_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_prelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_rrelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_selu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softplus_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softsign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_fro_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_inf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_normal_in_place_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_ones_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_pca_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_positive_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_randn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_repeat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_resize__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_resolve_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_rsqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_kaiser_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signbit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_slice_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_bessel_j1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_std_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_take_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_to_sparse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_topk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_trace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tril_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_triu_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_unbind_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_unflatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_view_as_real_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_view_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_where_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_zero__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_zeros_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_T_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___getitem___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rmatmul___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__softmax_backward_data_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__unsafe_masked_index_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__unsafe_masked_index_put_accumulate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addmv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_angle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_any_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_as_strided_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_atleast_3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bernoulli_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bfloat16_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bincount_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_char_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_clamp_max_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_constant_pad_nd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cummax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_digamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_div_trunc_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_dot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_dstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_erf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_expm1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_fftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ifft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ifftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ihfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_irfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_irfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_rfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_rfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_flipud_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_floor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fmod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_gt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_igamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_index_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_item_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_jiterator_binary_return_by_ref_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_lerp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_lgamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_cholesky_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_lu_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_solve_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_tensorinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logaddexp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logaddexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_argmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_meshgrid_variadic_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nansum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_narrow_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_native_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_one_hot_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_norm_nuc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_permute_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_renorm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_resolve_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_rsub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_scalar_tensor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_gaussian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_bessel_y0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_log_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_ndtri_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_true_divide_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unsafe_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unsqueeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_xlogy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_arange_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_complex32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_arange_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_tensor_overload_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags___rsub___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__batch_norm_with_update_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_bool_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_chalf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_float_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_int_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_polar_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_addcmul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_amax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_broadcast_tensors_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_clamp_min_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_constant_pad_nd_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_copysign_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_cos_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_cosh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diag_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diagonal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_dsplit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_expand_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_fftshift_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_hfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_ifftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_floor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_hsplit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_isnan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_diagonal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_log10_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_masked_fill_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_minimum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_narrow_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_ne_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_neg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_elu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_softplus_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_pow_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_rad2deg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_remainder_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_renorm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_repeat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_reshape_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_rsub_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_sigmoid_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_signbit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_i1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_log_ndtr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_multigammaln_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_split_with_sizes_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_square_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_squeeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_squeeze_multiple_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_std_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_triu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_triu_indices_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_unfold_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_unsqueeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_vdot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__segment_reduce_lengths_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__unsafe_masked_index_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_abs_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_addr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_any_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_as_strided_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bitwise_xor_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_broadcast_shapes_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cauchy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_clamp_min_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_clone_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_column_stack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_conj_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_count_nonzero_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cummax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diag_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diagonal_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_empty_permuted_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_erfinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_exp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_expand_as_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_eye_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_fftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_hfftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_irfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_rfftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fliplr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_float_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_floor_divide_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_full_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_geometric_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_gradient_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_histogram_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_i0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_igammac_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isclose_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ldexp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_lerp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_cholesky_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_cond_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_cross_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_det_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_ldl_factor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_slogdet_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_svdvals_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_tensorinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_tensorsolve_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log10_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log1p_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_normal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_softmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mH_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_argmin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_cumsum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_median_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_select_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_sum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_meshgrid_variadic_tensors_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_minimum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_gelu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_unpool1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_mse_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_norm_nuc_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_normal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ones_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ones_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_pca_lowrank_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_pinverse_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_polar_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_rand_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_real_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_reshape_as_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_reshape_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_resolve_neg_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_select_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_blackman_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_cosine_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_hann_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_nuttall_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sinh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_bessel_y1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_log_ndtr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_zeta_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sqrt_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_squeeze_multiple_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_stack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_std_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sub_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_tanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_tensor_split_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_torch__scaled_mm_cpu_float8_e4m3fn, test/test_ops.py::TestTagsCPU::test_tags_trace_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_transpose_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_true_divide_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unbind_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unsqueeze_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unsqueeze_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_view_as_complex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_view_as_real_cpu_complex64, test/test_ops.py::TestTagsCPU::test_tags_view_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_zeros_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_zeros_like_cpu_float32 2025-07-17T08:00:55.2326487Z 2025-07-17T08:00:58.2898353Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:00:58.2901161Z import pkg_resources 2025-07-17T08:00:58.4113087Z Running test_ops 4/6 ... [2025-07-17 08:00:58.410954] 2025-07-17T08:00:58.4113742Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:00:58.4116465Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops.py', '-m', 'not serial', '--shard-id=4', '--num-shards=6', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:00:58.411322] 2025-07-17T08:01:36.6001363Z 2025-07-17T08:01:36.6002481Z test_ops 2/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_2.6_1b6ea09c3fbe811f_.log 2025-07-17T08:01:36.8004501Z Running 5684 items in this shard: test/test_ops.py::TestCommonCPU::test_compare_cpu___radd___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu___rpow___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_T_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_complex_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_as_strided_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_atleast_2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_atleast_3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_bucketize_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_column_stack_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_interpolate_linear_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_layer_norm_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_leaky_relu_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_max_pool3d_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_max_unpool2d_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_max_unpool3d_grad_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_multilabel_soft_margin_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_nll_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_normalize_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_pairwise_distance_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_soft_margin_loss_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_prod_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_resize__cpu, test/test_ops.py::TestCommonCPU::test_dtypes_rsqrt_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_rsub_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_scatter_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_signal_windows_general_cosine_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_signal_windows_hann_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_signal_windows_kaiser_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_signbit_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_slice_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_slice_scatter_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_softmax_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_sort_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_sparse_sampled_addmm_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_bessel_j0_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_erfcx_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_i1_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_modified_bessel_k0_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_shifted_chebyshev_polynomial_u_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_shifted_chebyshev_polynomial_w_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_stack_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_std_mean_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_stft_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_tan_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_tensor_split_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_tensordot_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_tile_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_topk_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_transpose_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_trapezoid_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_triu_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_unbind_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_uniform_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_var_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_view_as_complex_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_vsplit_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_zero__cpu, test/test_ops.py::TestCommonCPU::test_errors_as_strided_scatter_cpu, test/test_ops.py::TestCommonCPU::test_errors_bitwise_and_cpu, test/test_ops.py::TestCommonCPU::test_errors_diag_cpu, test/test_ops.py::TestCommonCPU::test_errors_dstack_cpu, test/test_ops.py::TestCommonCPU::test_errors_eye_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_hfft_cpu, test/test_ops.py::TestCommonCPU::test_errors_fft_hfftn_cpu, test/test_ops.py::TestCommonCPU::test_errors_floor_divide_cpu, test/test_ops.py::TestCommonCPU::test_errors_fmin_cpu, test/test_ops.py::TestCommonCPU::test_errors_histogramdd_cpu, test/test_ops.py::TestCommonCPU::test_errors_igammac_cpu, test/test_ops.py::TestCommonCPU::test_errors_index_add_cpu, test/test_ops.py::TestCommonCPU::test_errors_kthvalue_cpu, test/test_ops.py::TestCommonCPU::test_errors_linalg_lstsq_cpu, test/test_ops.py::TestCommonCPU::test_errors_linalg_lstsq_grad_oriented_cpu, test/test_ops.py::TestCommonCPU::test_errors_logical_xor_cpu, test/test_ops.py::TestCommonCPU::test_errors_lt_cpu, test/test_ops.py::TestCommonCPU::test_errors_maximum_cpu, test/test_ops.py::TestCommonCPU::test_errors_mean_cpu, test/test_ops.py::TestCommonCPU::test_errors_min_binary_cpu, test/test_ops.py::TestCommonCPU::test_errors_movedim_cpu, test/test_ops.py::TestCommonCPU::test_errors_native_layer_norm_cpu, test/test_ops.py::TestCommonCPU::test_errors_nextafter_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_adaptive_max_pool2d_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_conv3d_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_hinge_embedding_loss_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_margin_ranking_loss_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_max_pool2d_cpu, test/test_ops.py::TestCommonCPU::test_errors_ormqr_cpu, test/test_ops.py::TestCommonCPU::test_errors_polar_cpu, test/test_ops.py::TestCommonCPU::test_errors_rot90_cpu, test/test_ops.py::TestCommonCPU::test_errors_signal_windows_hamming_cpu, test/test_ops.py::TestCommonCPU::test_errors_signal_windows_hann_cpu, test/test_ops.py::TestCommonCPU::test_errors_signal_windows_kaiser_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_mul_layout0_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_randn_like_layout3_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_zeros_like_layout1_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_legendre_polynomial_p_cpu, test/test_ops.py::TestCommonCPU::test_errors_t_cpu, test/test_ops.py::TestCommonCPU::test_errors_tril_cpu, test/test_ops.py::TestCommonCPU::test_errors_true_divide_cpu, test/test_ops.py::TestCommonCPU::test_errors_vdot_cpu, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch__batch_norm_with_update_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_addcmul_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_addmm_decomposed_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_alias_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_any_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_atan2_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cat_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cholesky_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cos_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cummin_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_diagonal_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_exp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_fft_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_irfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_rfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_rfftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_float_power_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_hypot_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_igamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_index_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_index_reduce_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_index_reduce_amin_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_ldexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_le_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_eigh_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_householder_product_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_norm_subgradients_at_zero_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_tensorsolve_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_vector_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_log1p_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logaddexp2_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logaddexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logical_xor_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logsumexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_min_binary_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_msort_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_multinomial_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nansum_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_linear_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_norm_inf_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_polar_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_polygamma_polygamma_n_1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sign_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_bessel_j1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_bessel_y0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_i1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_xlog1py_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_split_with_sizes_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_tan_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_trunc_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_H_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_T_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices___radd___cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices__batch_norm_with_update_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices__upsample_bilinear2d_aa_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_addmv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_alias_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_amax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_angle_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_any_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_argmin_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_as_strided_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_as_strided_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_asinh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_atan_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_atanh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_atleast_2d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_baddbmm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_baddbmm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_broadcast_to_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cartesian_prod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cauchy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cdouble_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cfloat_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_chalf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_chalf_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cholesky_inverse_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_chunk_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_max_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_clamp_min_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_conj_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_contiguous_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_count_nonzero_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cummin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_diagonal_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_diff_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_eq_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_erf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_erf_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_expm1_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_expm1_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfft_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifft_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifftshift_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ihfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_flatten_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_flatten_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_flipud_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_floor_divide_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_floor_divide_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_full_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_gather_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_geometric_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_geqrf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_i0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_fill_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_put_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_int_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_isfinite_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_isin_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_isinf_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_binary_return_by_ref_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_jiterator_unary_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_kthvalue_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_lcm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_lgamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_lgamma_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_cross_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_inv_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_ldl_factor_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_lu_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_matrix_rank_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_pinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_tensorinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_vecdot_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_not_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_not_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_or_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_or_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logsumexp_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mH_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_amax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_argmin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_fill_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_log_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_sum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_var_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_var_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_matrix_exp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_meshgrid_list_of_tensors_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_binary_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_reduction_no_dim_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_reduction_with_dim_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_narrow_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_native_layer_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ne_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_empty_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_empty_strided_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_avg_pool1d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_batch_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_conv_transpose1d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_cosine_embedding_loss_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_dropout_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_linear_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_margin_ranking_loss_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_max_unpool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pad_replicate_negative_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pairwise_distance_cpu_int64, 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test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_ormqr_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_polygamma_polygamma_n_2_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_randn_like_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_reciprocal_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_reshape_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_resolve_conj_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_rot90_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_round_decimals_0_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_scalar_tensor_cpu_float32, 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test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_bessel_j0_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_entr_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_t_copy_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_tensordot_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_trace_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unfold_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unsafe_split_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_vdot_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_vsplit_cpu_float32, 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test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_fft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_ifft2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_flip_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fliplr_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_hsplit_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_index_select_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_isnan_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_linspace_tensor_overload_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_log_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_logical_not_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_narrow_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_ne_cpu_complex64, 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test/test_ops.py::TestMathBitsCPU::test_conj_view_einsum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_empty_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_eq_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_hfft2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_hfftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_ifft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fill_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_full_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_imag_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_index_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_index_put_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_isfinite_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_isinf_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_binary_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_binary_return_by_ref_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_unary_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_lerp_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_diagonal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_ldl_solve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_lstsq_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_lstsq_grad_oriented_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_lu_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_matrix_power_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_matrix_rank_hermitian_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_qr_cpu_complex64, 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test/test_ops.py::TestMathBitsCPU::test_conj_view_rot90_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_sigmoid_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_softmax_with_dtype_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_split_list_args_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_std_mean_unbiased_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_t_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tril_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_triu_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unbind_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unflatten_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_view_as_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_view_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_H_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___rsub___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_T_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_cdouble_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_cfloat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_double_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_int_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_atan_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_atleast_3d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_broadcast_tensors_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_column_stack_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_conj_physical_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cumsum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_dsplit_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_empty_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_equal_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_exp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_expm1_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_fft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fill_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fliplr_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_index_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_index_fill_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_isclose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_lerp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linalg_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_linspace_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logsumexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_narrow_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_narrow_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_new_empty_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_nn_functional_log_softmax_with_dtype_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_special_log_softmax_with_dtype_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_special_softmax_with_dtype_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sqrt_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_std_mean_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_stft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_sum_to_size_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_t_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_t_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_take_along_dim_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unfold_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unfold_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_view_as_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_vstack_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addmm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_as_strided_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_atanh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_char_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_clone_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_conj_physical_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_dist_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_ifft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_irfft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_irfftn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_index_add_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_jiterator_2inputs_2outputs_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_jiterator_binary_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_cross_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_eigvals_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_matrix_rank_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_multi_dot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_solve_triangular_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_svdvals_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linspace_tensor_overload_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logdet_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logical_not_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_prod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_matrix_exp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_movedim_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_ne_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_normalize_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_pixel_unshuffle_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_softsign_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_tanhshrink_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_outer_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_permute_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_positive_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_renorm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_scatter_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sparse_sampled_addmm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_split_with_sizes_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_squeeze_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_std_mean_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_std_mean_unbiased_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_std_unbiased_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_stft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_svd_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_tanh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_tensordot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_to_sparse_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_true_divide_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_unfold_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_var_mean_unbiased_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_var_unbiased_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_view__batch_norm_with_update_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_bool_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_chalf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_int_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_acos_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_all_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_as_strided_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atleast_1d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_clamp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_count_nonzero_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_diag_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_div_trunc_rounding_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_dot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_dstack_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_exp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_expand_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_expand_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_fftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_hfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_hfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ifftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ihfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_ihfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_floor_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fmax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fmin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_igammac_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_item_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_vector_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linspace_tensor_overload_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_log_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logaddexp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logical_not_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logsumexp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_narrow_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nextafter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_channel_shuffle_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_elu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_gelu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_group_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_relu6_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_relu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_softmax_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_softplus_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_normal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_ones_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_repeat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_roll_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sinh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_softmax_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_i1_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_log_ndtr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_multigammaln_mvlgamma_p_3_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_softmax_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_split_with_sizes_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sqrt_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_squeeze_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_t_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_take_along_dim_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_trunc_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_unbind_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_xlogy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__segment_reduce_lengths_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__upsample_bilinear2d_aa_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_addbmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_amin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_argwhere_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_asinh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_baddbmm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_bool_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cfloat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_char_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cholesky_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cholesky_inverse_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_clamp_min_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_contiguous_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_corrcoef_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cosh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cummax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_diag_embed_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_diagonal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_digamma_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_dot_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_ifftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fill_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_frexp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_full_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_histogram_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_hstack_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_index_add_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_index_reduce_amin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_index_select_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_int_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_jiterator_2inputs_2outputs_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_jiterator_4inputs_with_extra_args_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_jiterator_binary_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_kthvalue_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_inv_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_inv_ex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_svd_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linspace_tensor_overload_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_log10_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_log_softmax_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_mT_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_argmin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_cumsum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_median_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_softmax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_matmul_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_maximum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_min_binary_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nanmean_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_new_zeros_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_avg_pool3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_conv3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_conv_transpose1d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_cross_entropy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_ctc_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_hardsigmoid_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_interpolate_area_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_interpolate_bilinear_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_linear_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_unpool1d_grad_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_normalize_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_pad_replicate_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_prelu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_relu6_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_rms_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_softshrink_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nonzero_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nonzero_static_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_norm_nuc_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_normal_in_place_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_pow_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_qr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_real_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_reciprocal_cpu_float64, 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test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_fft_hfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_fft_ifftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_fft_irfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_fliplr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_gather_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_half_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_index_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_kron_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_le_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_inv_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_svd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linalg_vander_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_log_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_logaddexp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_logspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_long_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_mH_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_masked_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_masked_argmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_masked_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_masked_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_masked_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_matrix_exp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_minimum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_narrow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_conv_transpose1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_cross_entropy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_embedding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_pdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_silu_complex_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_normal_in_place_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_normal_number_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_ormqr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_outer_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_polar_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_renorm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_repeat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_rot90_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_select_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_bartlett_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_blackman_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_exponential_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_gaussian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_hann_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_signal_windows_nuttall_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_slice_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_bessel_j0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_bessel_y1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_legendre_polynomial_p_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_square_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_squeeze_multiple_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_std_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_stft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_sub_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_svd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_take_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_transpose_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_unsafe_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_var_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_var_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_view_as_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_view_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_copysign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp___getitem___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp___rmatmul___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp__upsample_bilinear2d_aa_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_acos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_addcdiv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_addmv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_alias_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_dist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_div_floor_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_fft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_fftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_ifft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_ihfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_flip_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_grid_sampler_2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_eigh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_multi_dot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_pinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_tensorsolve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_logcumsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_lu_unpack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_mH_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_matrix_exp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_minimum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_native_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_cross_entropy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_dropout2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_dropout3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_embedding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_constant_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pad_replicate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_pow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_repeat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_scatter_reduce_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_select_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_sinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_squeeze_multiple_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_stft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_take_along_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_take_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_trunc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unsqueeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_var_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_zero__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_T_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp___rsub___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp__native_batch_norm_legit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_acos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_acosh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_addcdiv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_angle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_as_strided_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_atan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_baddbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_corrcoef_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cov_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_dist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_einsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_erf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_ifftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_flatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_float_power_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_frac_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_gather_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_gradient_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_grid_sampler_2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_hstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_inner_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_kthvalue_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_inv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_inv_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_lstsq_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_matrix_power_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_log2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_log_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_max_binary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_max_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_native_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_alpha_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_conv_transpose3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_local_response_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_unpool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pad_replicate_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_prelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_softsign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_threshold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_normal_number_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_polygamma_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_real_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_repeat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_roll_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_rot90_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_i1e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_xlog1py_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_stft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_tril_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_triu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unbind_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unsafe_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unsqueeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_var_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_xlogy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_deg2rad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_div_floor_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_div_trunc_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_empty_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_eye_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ihfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_flip_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fmod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_half_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_histogramdd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_igammac_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_index_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_index_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_inner_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_isposinf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_item_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_lcm_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_ldexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_cond_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_householder_product_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_ldl_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_solve_triangular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_tensorinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_log_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_log_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logcumsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mT_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_logaddexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_matmul_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_max_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mul_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nanmean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_new_empty_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_new_full_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_cosine_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_glu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_layer_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_logsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_threshold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_normal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_outer_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_pow_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_real_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_reshape_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_round_decimals_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_bartlett_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_airy_ai_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_hermite_polynomial_he_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_i1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_log_ndtr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_modified_bessel_i1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_modified_bessel_k0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_split_list_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_split_with_sizes_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_split_with_sizes_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_sum_to_size_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_take_along_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_triu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_trunc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_var_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_var_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_view_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rand___cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rdiv___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops__segment_reduce_offsets_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_argmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_atleast_1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bitwise_not_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_block_diag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_bucketize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_byte_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cholesky_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_copysign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cummin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cumprod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_deg2rad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_diff_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_dsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_empty_permuted_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_eq_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_equal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_erfinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_expand_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_eye_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_fft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ifftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_ihfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fft_irfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_histc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_index_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_int_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_isnan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_isreal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_istft_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_kthvalue_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_le_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_cond_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_cross_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_ldl_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_lu_factor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_vander_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_vector_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_log10_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_log1p_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_log2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_log_normal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_log_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logical_or_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logical_xor_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_mT_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_amax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_logsumexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_meshgrid_list_of_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_min_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_minimum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_mm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_msort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nanmedian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_binary_cross_entropy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_conv2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_gaussian_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_gelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_glu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_hardsigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_pdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_silu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_threshold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_upsample_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_normal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_normal_number_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_positive_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_real_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_remainder_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_resize_as__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_roll_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_round_decimals_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_round_decimals_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_scatter_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_searchsorted_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sgn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_blackman_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_cosine_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_hann_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_nuttall_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_bessel_j1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_i0e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_modified_bessel_i1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_scaled_modified_bessel_k0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_xlog1py_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_std_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_std_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_svd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_t_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_tensordot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_tile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_to_sparse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_transpose_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_triangular_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_tril_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_trunc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unbind_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unique_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unravel_index_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unsqueeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_view_as_real_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_zero__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_zeros_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_zeros_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_arange_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_arange_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_arange_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_arange_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_arange_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_arange_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_complex32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_tensor_overload_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_complex128, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_int32, test/test_ops.py::TestTagsCPU::test_tags_T_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags___rmod___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags___ror___cpu_int64, test/test_ops.py::TestTagsCPU::test_tags___rxor___cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_T_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_byte_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_complex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs__conversions_double_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_add_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_addr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_broadcast_shapes_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_cat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_contiguous_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diag_embed_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_diagonal_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_empty_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_erf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_expand_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_fft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_fftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_ifftshift_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_ihfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_rfftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fliplr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fmin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_imag_cpu_complex64, test/test_ops.py::TestTagsCPU::test_tags__refs_lerp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_cross_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_svdvals_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_movedim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_mul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_new_empty_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_new_full_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_alpha_dropout_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_dropout_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_hardtanh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_hinge_embedding_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_pdist_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_relu6_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_threshold_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_permute_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_randn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_roll_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_rot90_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_rsqrt_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_sgn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_sign_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_logit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_stft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_sum_to_size_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_t_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_to_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_trace_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_tril_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_unbind_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_unfold_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_unsqueeze_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_view_as_complex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_where_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_zeros_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_addcdiv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_addmv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_alias_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_amax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_argsort_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_as_strided_partial_views_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_atleast_1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bitwise_and_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_broadcast_tensors_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_broadcast_to_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cfloat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_char_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_chunk_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_combinations_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cumsum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diagflat_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diagonal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_div_floor_rounding_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_empty_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_erfc_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_fft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_rfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fill_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_flatten_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_frac_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_geqrf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_index_add_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isneginf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_istft_cpu_complex64, test/test_ops.py::TestTagsCPU::test_tags_jiterator_4inputs_with_extra_args_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_kthvalue_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_householder_product_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_lu_factor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_matrix_rank_hermitian_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_pinv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_pinv_hermitian_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linspace_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_log2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_logical_or_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_amax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_prod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_softmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_matmul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_max_reduction_no_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_min_binary_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_min_reduction_no_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_movedim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_msort_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_narrow_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ne_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_new_zeros_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_dropout_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_embedding_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_huber_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_layer_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_pool1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_unpool1d_grad_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_multilabel_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_softplus_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_softshrink_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_softsign_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_upsample_bilinear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_normal_number_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_outer_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_qr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_randint_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_randn_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_repeat_interleave_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_resize__cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_rot90_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_select_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_softmax_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sort_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sparse_mm_reduce_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_entr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_modified_bessel_i1_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_ndtri_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_split_with_sizes_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_std_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_stft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_svd_lowrank_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_take_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_to_sparse_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_triangular_solve_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_triu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unique_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unsafe_split_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_var_mean_unbiased_cpu_float32 2025-07-17T08:01:36.9944176Z 2025-07-17T08:01:39.7556404Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:01:39.7558055Z import pkg_resources 2025-07-17T08:01:39.8793027Z Running inductor/test_ck_backend 1/1 ... [2025-07-17 08:01:39.879012] 2025-07-17T08:01:39.8793501Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:01:39.8795999Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_ck_backend.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:01:39.879343] 2025-07-17T08:01:46.6232672Z 2025-07-17T08:01:46.6233780Z inductor/test_ck_backend 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_ck_backend_1.1_e6bdafd7e8dcb6a6_.log 2025-07-17T08:01:46.6234500Z 2025-07-17T08:01:50.1247805Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:01:50.1249382Z import pkg_resources 2025-07-17T08:01:50.2465453Z Running typing/test_python_operators 1/1 ... [2025-07-17 08:01:50.246278] 2025-07-17T08:01:50.2465941Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:01:50.2468322Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'typing/test_python_operators.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'] ... [2025-07-17 08:01:50.246603] 2025-07-17T08:01:54.4667692Z 2025-07-17T08:01:54.4668687Z typing/test_python_operators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/typing.test_python_operators_1.1_59bd94629a592461_.log 2025-07-17T08:01:54.4783799Z Running 318 items in this shard: test/typing/test_python_operators.py::TestPythonOperators::test_binary_a100_op_%_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a101_op_%_b101, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a102_op_%_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a103_op_%_b103, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a104_op_*_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a105_op_*_b105, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a106_op_*_b_3, 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test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_+_b191, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_+_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_+_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_-_b193, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_-_b195, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_-_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_-_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_//_b201, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_//_b203, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_//_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_//_b_3, 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test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<=_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<_b157, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<_b159, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_<_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_==_b165, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_==_b167, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_==_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_==_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>=_b173, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>=_b175, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>=_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>=_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>>_b213, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>>_b215, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>>_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>>_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>_b169, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>_b171, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_>_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_@_b225, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_@_b227, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_@_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_@_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_^_b217, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_^_b219, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_^_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_^_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_|_b221, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_|_b223, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_|_b_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_binary_a_3_op_|_b_3, test/typing/test_python_operators.py::TestPythonOperators::test_operators_are_correct_and_complete, test/typing/test_python_operators.py::TestPythonOperators::test_type_tests_are_complete, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_+_a1, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_+_a3, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_+_a_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_+_a_3, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_-_a5, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_-_a7, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_-_a_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_-_a_3, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_~_a11, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_~_a9, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_~_a_1_5, test/typing/test_python_operators.py::TestPythonOperators::test_unary_op_~_a_3 2025-07-17T08:01:54.4897145Z 2025-07-17T08:01:57.4217223Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:01:57.4219006Z import pkg_resources 2025-07-17T08:01:57.5432098Z Running torch_np/test_nep50_examples 1/1 ... [2025-07-17 08:01:57.542842] 2025-07-17T08:01:57.5432993Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:01:57.5435616Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_nep50_examples.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'] ... [2025-07-17 08:01:57.543215] 2025-07-17T08:02:02.8656075Z 2025-07-17T08:02:02.8657738Z torch_np/test_nep50_examples 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_nep50_examples_1.1_8a92c6cce7c3ce6b_.log 2025-07-17T08:02:03.0220005Z Running 1573 items in this shard: test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_3j + array(3, complex64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_True + uint8(2), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array(1_0, float32) + 1e-14 == 1_0, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([0_1], float32) == float64(0_1), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([100], uint8) + 200, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 1, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 200, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + 300, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + array(1, int64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1], uint8) + int64(1), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_0], float32) + 1e-14 == 1_0, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + 3, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + array(1_, float64), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + float64(1_), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_array([1_], float32) + int64(3), test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_bool_(True) + 1, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(1) + 1j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(1) + 3e100, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_float32(5) + 5j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int16(2) + 2, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int16(4) + 4j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_int32(1) + 5j, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(1) + 2, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(1) + 300, test/torch_np/test_nep50_examples.py::TestNEP50Table::test_nep50_exceptions_example_uint8(100) + 200, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar27_array27, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar28_array28, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar29_array29, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar30_array30, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar31_array31, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar32_array32, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar33_array33, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar34_array34, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar35_array35, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array10, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array11, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array12, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array13, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array14, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array15, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array16, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array17, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_1_array9, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array18, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array19, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array20, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array21, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array22, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array23, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_compare_ufuncs_name_add_scalar_2_0_array24, 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test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar31_array31_dtype31, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar32_array32_dtype32, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar33_array33_dtype33, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar34_array34_dtype34, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar35_array35_dtype35, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array10_dtype10, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array11_dtype11, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array12_dtype12, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array13_dtype13, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array14_dtype14, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array15_dtype15, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array16_dtype16, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array17_dtype17, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_1_array9_dtype9, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array18_dtype18, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array19_dtype19, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array20_dtype20, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array21_dtype21, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array22_dtype22, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array23_dtype23, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array24_dtype24, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array25_dtype25, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_2_0_array26_dtype26, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array0_dtype0, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array1_dtype1, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array2_dtype2, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array3_dtype3, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array4_dtype4, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array5_dtype5, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array6_dtype6, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array7_dtype7, test/torch_np/test_nep50_examples.py::TestCompareToNumpy::test_direct_compare_scalar_True_array8_dtype8 2025-07-17T08:02:03.1717797Z 2025-07-17T08:02:05.9269324Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:02:05.9270912Z import pkg_resources 2025-07-17T08:02:06.0495461Z Running inductor/test_best_config 1/1 ... [2025-07-17 08:02:06.049277] 2025-07-17T08:02:06.0495941Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:02:06.0498674Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_best_config.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:02:06.049604] 2025-07-17T08:02:12.4744802Z 2025-07-17T08:02:12.4746285Z inductor/test_best_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_best_config_1.1_c5cd315786cb2926_.log 2025-07-17T08:02:12.4747908Z 2025-07-17T08:02:15.3939608Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:02:15.3942669Z import pkg_resources 2025-07-17T08:02:15.5157041Z Running inductor/test_compile_worker 1/1 ... [2025-07-17 08:02:15.515434] 2025-07-17T08:02:15.5157576Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:02:15.5160793Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_compile_worker.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'] ... [2025-07-17 08:02:15.515775] 2025-07-17T08:02:30.4005771Z 2025-07-17T08:02:30.4006577Z test_decomp 1/10 was successful, full logs can be found in artifacts with path test/test-reports/test_decomp_1.10_567fc49fc73a8532_.log 2025-07-17T08:02:30.4290150Z Running 870 items in this shard: test/test_decomp.py::TestDecompCPU::test_comprehensive_H_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_T_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive___getitem___cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___radd___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rand___cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rdiv___cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmatmul___cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmatmul___cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rmul___cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive___rsub___cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive__chunk_cat_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_abs_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_acos_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_acosh_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addbmm_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_addcmul_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_decomposed_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_addmm_decomposed_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_all_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_any_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_arange_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argmin_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argsort_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argsort_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argsort_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_argwhere_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_argwhere_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_as_strided_scatter_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_asin_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_asin_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_asinh_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan2_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_atan_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atanh_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_1d_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_3d_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_atleast_3d_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_baddbmm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_bfloat16_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_bincount_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_bincount_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_bitwise_or_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_block_diag_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_bmm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_bmm_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_broadcast_to_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_bucketize_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_byte_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_cat_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_cauchy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cdouble_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_chalf_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_chalf_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_clamp_max_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_combinations_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_combinations_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_combinations_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_combinations_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_complex_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_conj_physical_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_contiguous_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_copysign_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_corrcoef_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_cos_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_cos_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_cosh_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_count_nonzero_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cross_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_cummax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_cummin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_cumsum_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_diag_embed_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagflat_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_copy_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_diagonal_scatter_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_diff_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_diff_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_floor_rounding_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_no_rounding_mode_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_no_rounding_mode_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_div_trunc_rounding_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_dot_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_double_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_dsplit_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_dsplit_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_empty_strided_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_eq_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_equal_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_equal_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_equal_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_erf_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_erfc_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_exp2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_copy_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_expand_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_expm1_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_expm1_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_expm1_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_eye_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fft2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fft_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fftn_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_fftshift_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_hfft2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_hfft_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_hfft_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_hfft_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_hfft_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifft2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifft_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifft_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifftn_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ifftshift_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ihfft2_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ihfft_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_ihfft_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_irfft2_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_irfft_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_irfft_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_rfft2_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_rfft_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_rfft_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_fft_rfftn_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fill_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_fill_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fill_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_flatten_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_flatten_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_flatten_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_flip_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_flip_cpu_int16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fliplr_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_flipud_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_float_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_float_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_float_power_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmax_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_fmin_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_comprehensive_frac_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_full_cpu_float32, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_int32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_softmin_with_dtype_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_threshold_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_threshold_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_threshold_cpu_int64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_nn_functional_upsample_bilinear_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_nonzero_cpu_int16, 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test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_1_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_int8, test/test_decomp.py::TestDecompCPU::test_comprehensive_positive_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_comprehensive_positive_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_pow_cpu_float64, test/test_decomp.py::TestDecompCPU::test_comprehensive_prod_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_comprehensive_put_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_comprehensive_rad2deg_cpu_bool, test/test_decomp.py::TestDecompCPU::test_comprehensive_rad2deg_cpu_float32, test/test_decomp.py::TestDecompCPU::test_comprehensive_randint_like_cpu_float16, test/test_decomp.py::TestDecompCPU::test_comprehensive_randn_cpu_bfloat16, 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test/test_decomp.py::TestDecompCPU::test_quick_add_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_addcdiv_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_addcmul_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_addmm_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_addmm_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_addmm_decomposed_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_addmm_decomposed_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_addr_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_addr_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_alias_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_all_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_all_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_amax_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_amin_cpu_float64, 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test/test_decomp.py::TestDecompCPU::test_quick_constant_pad_nd_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_core_backward__softmax_backward_data_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_addcmul_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_native_dropout_backward_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_special_entr_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_core_backward_trace_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_cos_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_cosh_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_cumprod_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_cumsum_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_deg2rad_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_diag_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_diagonal_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_digamma_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_digamma_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_div_floor_rounding_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_div_floor_rounding_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_div_no_rounding_mode_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_div_trunc_rounding_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_dot_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_dot_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_dot_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_empty_like_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_empty_strided_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_empty_strided_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_empty_strided_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_empty_strided_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_eq_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_eq_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_erfc_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_erfc_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_erfinv_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_exp2_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_exp2_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_expand_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_expand_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_expm1_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft2_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_fft_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_fftn_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_fft_fftn_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft2_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfft_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_fft_hfftn_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_fft_ifft_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfft2_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fft_ihfftn_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfft2_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfftn_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfftn_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fft_irfftn_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft2_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft2_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_fft_rfft_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fill_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_flip_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_floor_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_fmin_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_fmin_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_fmod_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_frexp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_full_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_gcd_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_geometric_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_grid_sampler_2d_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_gt_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_heaviside_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_heaviside_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_index_add_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_index_fill_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_index_select_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_isin_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_isin_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_isnan_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_isneginf_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_isneginf_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_item_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_le_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_le_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_lerp_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_lerp_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_lgamma_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_linalg_diagonal_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_linalg_vector_norm_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_linspace_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_linspace_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_log1p_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_logaddexp_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_logical_and_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_logical_and_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_logical_and_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_logical_not_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_logical_or_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_logical_xor_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_logspace_tensor_overload_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_maximum_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_maximum_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_list_of_tensors_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_list_of_tensors_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_meshgrid_variadic_tensors_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_minimum_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_minimum_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_mul_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_mv_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_mv_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_nan_to_num_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nan_to_num_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_nansum_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_native_batch_norm_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_native_dropout_backward_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_ne_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_ne_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_neg_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_new_empty_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_new_full_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_new_full_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_new_ones_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_new_zeros_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_embedding_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_gelu_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_glu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_huber_loss_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_logsigmoid_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_max_unpool2d_grad_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_max_unpool3d_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_pad_constant_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_prelu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_prelu_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu6_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_relu_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_silu_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_nn_functional_softshrink_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_norm_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_norm_inf_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_normal_in_place_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_permute_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_permute_copy_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_permute_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_pow_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_pow_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_prod_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_randn_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_reciprocal_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_remainder_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_remainder_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_remainder_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_roll_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_rot90_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_rot90_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_round_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_round_decimals_neg_3_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_rsqrt_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_rsub_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_select_scatter_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sgn_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_sigmoid_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_sign_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_sign_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_signbit_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_sin_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sinh_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_slice_scatter_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_softmax_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_softmax_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_softmax_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_softmax_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_special_entr_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_special_i0e_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_special_i1_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_special_log_ndtr_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_special_ndtri_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_split_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_split_with_sizes_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_sqrt_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_multiple_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_multiple_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_squeeze_multiple_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_stack_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_std_unbiased_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_sum_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_t_copy_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_t_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_take_cpu_int64, test/test_decomp.py::TestDecompCPU::test_quick_tanh_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_trace_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_trace_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_complex32, test/test_decomp.py::TestDecompCPU::test_quick_transpose_copy_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_tril_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_triu_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_triu_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_trunc_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_unfold_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_unsafe_split_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_copy_cpu_bool, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_copy_cpu_complex128, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_bfloat16, test/test_decomp.py::TestDecompCPU::test_quick_unsqueeze_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_var_mean_unbiased_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_var_unbiased_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_var_unbiased_cpu_float32, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_vdot_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_view_copy_cpu_int16, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_float16, test/test_decomp.py::TestDecompCPU::test_quick_view_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_complex64, test/test_decomp.py::TestDecompCPU::test_quick_where_cpu_float64, test/test_decomp.py::TestDecompCPU::test_quick_xlogy_cpu_int8, test/test_decomp.py::TestDecompCPU::test_quick_xlogy_cpu_uint8, test/test_decomp.py::TestDecompCPU::test_quick_zeros_cpu_int32, test/test_decomp.py::TestDecompCPU::test_quick_zeros_like_cpu_int16, test/test_decomp.py::TestDecompCPU::test_rnn_decomp_module_nn_GRU_train_mode_cpu_float32, test/test_decomp.py::TestDecompCPU::test_rnn_decomp_module_nn_LSTM_eval_mode_cpu_float64, test/test_decomp.py::TestDecompCPU::test_uniform_cpu, test/test_decomp.py::DecompOneOffTestsCPU::test_sdpa_nn_functional_scaled_dot_product_attention_cpu_bfloat16, test/test_decomp.py::DecompOneOffTestsCPU::test_weight_norm_interface_cpu 2025-07-17T08:02:30.4565184Z 2025-07-17T08:02:32.1028230Z 2025-07-17T08:02:32.1029595Z inductor/test_compile_worker 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_compile_worker_1.1_b3264f795f5a023e_.log 2025-07-17T08:02:32.1033120Z Running 4 items in this shard: test/inductor/test_compile_worker.py::TestCompileWorker::test_basic_jobs, test/inductor/test_compile_worker.py::TestCompileWorker::test_crash, test/inductor/test_compile_worker.py::TestCompileWorker::test_exception, test/inductor/test_compile_worker.py::TestCompileWorker::test_quiesce 2025-07-17T08:02:32.1035549Z 2025-07-17T08:02:33.5280455Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:02:33.5282039Z import pkg_resources 2025-07-17T08:02:33.6508391Z Running test_ops_jit 1/2 ... [2025-07-17 08:02:33.650577] 2025-07-17T08:02:33.6508869Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:02:33.6511126Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_jit.py', '-m', 'not serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:02:33.650904] 2025-07-17T08:02:35.1252714Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:02:35.1254285Z import pkg_resources 2025-07-17T08:02:35.2487420Z Running test_ops_jit 2/2 ... [2025-07-17 08:02:35.248459] 2025-07-17T08:02:35.2487836Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:02:35.2489818Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_jit.py', '-m', 'not serial', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:02:35.248783] 2025-07-17T08:10:25.3785482Z 2025-07-17T08:10:25.3787055Z test_ops_jit 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_jit_1.2_f6084b611bf414f1_.log 2025-07-17T08:10:25.4229499Z Running 596 items in this shard: test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_acos_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_asin_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_atan2_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_clamp_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_div_trunc_rounding_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_erfinv_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_exp2_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_expm1_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_ge_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_i0_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_igamma_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_igammac_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_lgamma_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_linalg_det_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_lt_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_matmul_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_matrix_exp_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_min_binary_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_movedim_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_mvlgamma_mvlgamma_p_3_cpu_float32, 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test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv1d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv3d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose1d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose1d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose2d_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose2d_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_conv_transpose3d_cpu_complex64, 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test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unbind_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unfold_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unfold_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unfold_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_uniform_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unique_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsafe_chunk_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsafe_split_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsafe_split_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsqueeze_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_mean_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_mean_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_mean_unbiased_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_mean_unbiased_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_unbiased_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_as_real_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vsplit_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_where_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zero__cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zero__cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zeros_like_cpu_float32 2025-07-17T08:10:25.4661979Z 2025-07-17T08:10:26.2742627Z Uploading artifacts took 0.89 seconds 2025-07-17T08:10:28.7163845Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:10:28.7166781Z import pkg_resources 2025-07-17T08:10:28.8414393Z Running dynamo/test_skip_guard_eval_unsafe 1/1 ... [2025-07-17 08:10:28.841049] 2025-07-17T08:10:28.8415109Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:10:28.8417220Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_skip_guard_eval_unsafe.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'] ... [2025-07-17 08:10:28.841410] 2025-07-17T08:10:32.2099550Z 2025-07-17T08:10:32.2100931Z test_ops_jit 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_jit_2.2_a0d95acf084ed58a_.log 2025-07-17T08:10:32.2504871Z Running 540 items in this shard: test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_abs_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_acosh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_asinh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_atan_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_atanh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_cat_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_digamma_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_jit_alias_remapping_div_floor_rounding_cpu_float32, 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test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nonzero_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nonzero_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_nonzero_static_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_norm_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_norm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_norm_fro_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_norm_nuc_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_norm_nuc_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_normal_in_place_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_normal_number_mean_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_ones_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_ones_like_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_ones_like_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_ormqr_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_outer_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_permute_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_permute_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_permute_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_polygamma_polygamma_n_2_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_polygamma_polygamma_n_3_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_polygamma_polygamma_n_4_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_positive_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_positive_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_pow_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_prod_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_qr_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_qr_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_rad2deg_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_rand_like_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_rand_like_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_randn_like_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_ravel_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_real_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_reciprocal_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_remainder_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_renorm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_repeat_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_repeat_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_repeat_interleave_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_repeat_interleave_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_reshape_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_resize_as__cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_resize_as__cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_resolve_conj_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_resolve_neg_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_resolve_neg_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_roll_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_rot90_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_round_decimals_0_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_rsqrt_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_rsub_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_scalar_tensor_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_scatter_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_scatter_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_scatter_reduce_sum_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_searchsorted_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_select_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sgn_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sgn_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_short_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sign_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_signal_windows_bartlett_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_signal_windows_blackman_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_signal_windows_general_hamming_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_signal_windows_hamming_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_signal_windows_hann_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_signal_windows_kaiser_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sinh_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_slice_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sparse_sampled_addmm_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sparse_sampled_addmm_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_bessel_j0_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_bessel_j1_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_bessel_y1_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_hermite_polynomial_h_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_i0e_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_i1e_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_legendre_polynomial_p_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_log_ndtr_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_modified_bessel_k0_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_modified_bessel_k1_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_ndtri_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_scaled_modified_bessel_k0_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_special_zeta_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_split_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_split_with_sizes_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sqrt_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_square_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_squeeze_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_squeeze_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_squeeze_multiple_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_stack_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_mean_unbiased_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_unbiased_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_std_unbiased_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_stft_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sub_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sum_to_size_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_sum_to_size_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_svd_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_t_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_take_along_dim_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_take_along_dim_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_take_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_take_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tan_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tensor_split_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tensordot_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tensordot_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_to_sparse_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_topk_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trace_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_transpose_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trapezoid_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trapezoid_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_trapz_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_triangular_solve_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_tril_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_triu_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_true_divide_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_true_divide_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unbind_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unflatten_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unflatten_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unfold_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_uniform_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unique_consecutive_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsafe_chunk_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsqueeze_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsqueeze_copy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_unsqueeze_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_var_unbiased_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vdot_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vdot_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_as_complex_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_as_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_as_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_copy_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_view_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vsplit_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vstack_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_vstack_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_where_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_xlogy_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zeros_cpu_complex64, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zeros_cpu_float32, test/test_ops_jit.py::TestJitCPU::test_variant_consistency_jit_zeros_like_cpu_complex64 2025-07-17T08:10:32.2772445Z 2025-07-17T08:10:35.3518724Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:10:35.3521264Z import pkg_resources 2025-07-17T08:10:35.4789564Z Running functorch/test_ac_logging 1/1 ... [2025-07-17 08:10:35.478601] 2025-07-17T08:10:35.4790081Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:10:35.4792821Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac_logging.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:10:35.479015] 2025-07-17T08:10:38.2209107Z 2025-07-17T08:10:38.2210169Z dynamo/test_skip_guard_eval_unsafe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_skip_guard_eval_unsafe_1.1_017f4d4414e5d874_.log 2025-07-17T08:10:38.2212891Z Running 5 items in this shard: test/dynamo/test_skip_guard_eval_unsafe.py::RunDiffGuardTests::test_bool_recompile, test/dynamo/test_skip_guard_eval_unsafe.py::RunDiffGuardTests::test_cache_line_pickup, test/dynamo/test_skip_guard_eval_unsafe.py::RunDiffGuardTests::test_fail_on_tensor_shape_change, test/dynamo/test_skip_guard_eval_unsafe.py::RunDiffGuardTests::test_post_recompile, test/dynamo/test_skip_guard_eval_unsafe.py::RunDiffGuardTests::test_tensor_recompile 2025-07-17T08:10:38.2214926Z 2025-07-17T08:10:38.7478387Z 2025-07-17T08:10:38.7479406Z functorch/test_ac_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_logging_1.1_f888070e40c174d8_.log 2025-07-17T08:10:38.7482222Z Running 4 items in this shard: test/functorch/test_ac_logging.py::TestAcLogging::test_create_activation_checkpointing_logging_structure_payload, test/functorch/test_ac_logging.py::TestAcLogging::test_create_joint_graph_edges, test/functorch/test_ac_logging.py::TestAcLogging::test_create_joint_graph_node_information, test/functorch/test_ac_logging.py::TestAcLogging::test_create_structured_trace_for_min_cut_info 2025-07-17T08:10:38.7484026Z 2025-07-17T08:10:41.4625321Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:10:41.4627153Z import pkg_resources 2025-07-17T08:10:41.5880671Z Running test_custom_ops 1/1 ... [2025-07-17 08:10:41.587810] 2025-07-17T08:10:41.5881218Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:10:41.5883837Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_custom_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'] ... [2025-07-17 08:10:41.588145] 2025-07-17T08:10:41.8626675Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:10:41.8628234Z import pkg_resources 2025-07-17T08:10:41.9862839Z Running test_ao_sparsity 1/1 ... [2025-07-17 08:10:41.985967] 2025-07-17T08:10:41.9863299Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:10:41.9865572Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ao_sparsity.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'] ... [2025-07-17 08:10:41.986334] 2025-07-17T08:10:53.5689305Z 2025-07-17T08:10:53.5690469Z test_ao_sparsity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_ao_sparsity_1.1_74e2844fac4e6757_.log 2025-07-17T08:10:53.5720452Z Running 88 items in this shard: test/test_ao_sparsity.py::TestQuantizedSparseKernels::test_sparse_qlinear, test/test_ao_sparsity.py::TestQuantizedSparseLayers::test_sparse_qlinear, test/test_ao_sparsity.py::TestQuantizedSparseLayers::test_sparse_qlinear_serdes, test/test_ao_sparsity.py::TestFakeSparsity::test_jit_trace, test/test_ao_sparsity.py::TestFakeSparsity::test_masking_logic, test/test_ao_sparsity.py::TestFakeSparsity::test_state_dict_preserved, test/test_ao_sparsity.py::TestFakeSparsity::test_weights_parametrized, test/test_ao_sparsity.py::TestCubicScheduler::test_constructor, test/test_ao_sparsity.py::TestCubicScheduler::test_step, test/test_ao_sparsity.py::TestScheduler::test_constructor, test/test_ao_sparsity.py::TestScheduler::test_lambda_scheduler, test/test_ao_sparsity.py::TestScheduler::test_order_of_steps, test/test_ao_sparsity.py::TestScheduler::test_step, test/test_ao_sparsity.py::TestBaseSparsifier::test_constructor, test/test_ao_sparsity.py::TestBaseSparsifier::test_convert, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params1, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params2, test/test_ao_sparsity.py::TestBaseSparsifier::test_mask_squash_with_params3, test/test_ao_sparsity.py::TestBaseSparsifier::test_prepare_config, test/test_ao_sparsity.py::TestBaseSparsifier::test_state_dict, test/test_ao_sparsity.py::TestBaseSparsifier::test_step, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_constructor, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_mask_squash, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_prepare, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_sparsity_levels, test/test_ao_sparsity.py::TestNearlyDiagonalSparsifier::test_step, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_constructor, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_mask_squash, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_prepare, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_sparsity_levels, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_step, test/test_ao_sparsity.py::TestWeightNormSparsifier::test_step_2_of_4, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_complex_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_constructor, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prepare_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prepare_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_activation_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_bias_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_padding_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_conv2d_pool_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_activation_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_bias_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_linear_linear, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_layernorm_linear_multiple_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_layernorm_linear_single_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_multiple_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_prune_lstm_linear_single_layer, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_step_conv2d, test/test_ao_sparsity.py::TestBaseStructuredSparsifier::test_step_linear, test/test_ao_sparsity.py::TestFPGMPruner::test_compute_distance, test/test_ao_sparsity.py::TestFPGMPruner::test_update_mask, test/test_ao_sparsity.py::TestSaliencyPruner::test_lstm_saliency_pruner_update_mask, test/test_ao_sparsity.py::TestSaliencyPruner::test_saliency_pruner_update_mask, test/test_ao_sparsity.py::TestComposability::test_convert_without_squash_mask, test/test_ao_sparsity.py::TestComposability::test_fusion_before_s_prep, test/test_ao_sparsity.py::TestComposability::test_q_prep_before_s_prep, test/test_ao_sparsity.py::TestComposability::test_qat_prep_before_s_prep, test/test_ao_sparsity.py::TestComposability::test_s_prep_before_fusion, test/test_ao_sparsity.py::TestComposability::test_s_prep_before_q_prep, test/test_ao_sparsity.py::TestComposability::test_s_prep_before_qat_prep, test/test_ao_sparsity.py::TestFxComposability::test_q_prep_fx_before_s_prep, test/test_ao_sparsity.py::TestFxComposability::test_q_prep_fx_s_prep_ref_conv, test/test_ao_sparsity.py::TestFxComposability::test_s_prep_before_q_prep_fx, test/test_ao_sparsity.py::TestFxComposability::test_s_prep_before_qat_prep_fx, test/test_ao_sparsity.py::TestFxComposability::test_s_prep_q_prep_fx_ref, test/test_ao_sparsity.py::TestActivationSparsifier::test_activation_sparsifier, test/test_ao_sparsity.py::TestBaseDataScheduler::test_constructor, test/test_ao_sparsity.py::TestBaseDataScheduler::test_order_of_steps, test/test_ao_sparsity.py::TestBaseDataScheduler::test_state_dict, test/test_ao_sparsity.py::TestBaseDataScheduler::test_step, test/test_ao_sparsity.py::TestBaseDataSparsifier::test_nn_embeddings, test/test_ao_sparsity.py::TestBaseDataSparsifier::test_nn_parameters, test/test_ao_sparsity.py::TestBaseDataSparsifier::test_tensors, test/test_ao_sparsity.py::TestNormDataSparsifiers::test_nn_embeddings, test/test_ao_sparsity.py::TestNormDataSparsifiers::test_nn_parameters, test/test_ao_sparsity.py::TestNormDataSparsifiers::test_tensors, test/test_ao_sparsity.py::TestQuantizationUtils::test_ptq_quantize_first, test/test_ao_sparsity.py::TestQuantizationUtils::test_ptq_sparsify_first, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module_fail, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_fqn_to_module_for_tensors, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_get_arg_info_from_tensor_fqn, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_get_arg_info_from_tensor_fqn_fail, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn_fail, test/test_ao_sparsity.py::TestSparsityUtilFunctions::test_module_to_fqn_root 2025-07-17T08:10:53.5748913Z 2025-07-17T08:10:56.6591318Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:10:56.6594271Z import pkg_resources 2025-07-17T08:10:56.7824408Z Running torch_np/test_binary_ufuncs 1/1 ... [2025-07-17 08:10:56.782100] 2025-07-17T08:10:56.7825161Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:10:56.7827871Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_binary_ufuncs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:10:56.782470] 2025-07-17T08:11:00.2516198Z 2025-07-17T08:11:00.2517174Z torch_np/test_binary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_binary_ufuncs_1.1_fc6fa998de8b6be1_.log 2025-07-17T08:11:00.2530855Z Running 38 items in this shard: test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_add, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_arctan2, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_bitwise_and, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_bitwise_or, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_bitwise_xor, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_copysign, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_divide, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_float_power, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_floor_divide, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_fmax, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_fmin, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_fmod, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_gcd, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_greater, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_greater_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_heaviside, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_hypot, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_lcm, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_ldexp, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_left_shift, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_less, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_less_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logaddexp, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logaddexp2, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logical_and, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logical_or, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_logical_xor, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_matmul, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_maximum, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_minimum, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_multiply, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_nextafter, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_not_equal, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_power, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_remainder, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_right_shift, test/torch_np/test_binary_ufuncs.py::TestBinaryUfuncBasic::test_subtract 2025-07-17T08:11:00.2543129Z 2025-07-17T08:11:03.4833497Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:03.4836523Z import pkg_resources 2025-07-17T08:11:03.6050822Z Running test_hop_infra 1/1 ... [2025-07-17 08:11:03.604769] 2025-07-17T08:11:03.6051592Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:03.6054944Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hop_infra.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'] ... [2025-07-17 08:11:03.605160] 2025-07-17T08:11:07.6250843Z 2025-07-17T08:11:07.6251742Z test_hop_infra 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hop_infra_1.1_9f09873f2854e968_.log 2025-07-17T08:11:07.6253426Z Running 3 items in this shard: test/test_hop_infra.py::TestHOPInfra::test_all_hops_are_imported, test/test_hop_infra.py::TestHOPInfra::test_all_hops_have_opinfo, test/test_hop_infra.py::TestHOPInfra::test_imports_from_all_work 2025-07-17T08:11:07.6254394Z 2025-07-17T08:11:09.5650577Z 2025-07-17T08:11:09.5651769Z test_ops 4/6 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_4.6_ad652fc1fd618cb7_.log 2025-07-17T08:11:09.7873397Z Running 5651 items in this shard: test/test_ops.py::TestSelfKwarg::test_self_kwargs, test/test_ops.py::TestCommonCPU::test_compare_cpu_H_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu___rmul___cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_bool_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_byte_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_cfloat_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs__conversions_float_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_arange_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_as_strided_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_atleast_1d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_diag_embed_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_diagonal_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_div_trunc_rounding_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_dot_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_dstack_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_empty_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_expand_as_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_expand_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_fft_fftshift_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_fmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_geometric_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_hsplit_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_igamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_index_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_index_select_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_istft_cpu_complex64, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_lerp_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_linspace_tensor_overload_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_logspace_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_masked_fill_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_hardshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_huber_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_normal_number_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_ones_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_repeat_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_reshape_as_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_select_scatter_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_special_softmax_with_dtype_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_special_zeta_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_split_with_sizes_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_stft_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_unfold_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__refs_view_as_complex_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__segment_reduce_offsets_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu__softmax_backward_data_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_alias_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_argsort_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_as_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_bernoulli_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_bfloat16_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_bincount_cpu_int64, test/test_ops.py::TestCommonCPU::test_compare_cpu_bitwise_left_shift_cpu_int64, test/test_ops.py::TestCommonCPU::test_compare_cpu_cartesian_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_cdouble_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_diagonal_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_einsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_flip_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_gather_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_histc_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_hsplit_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_index_add_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_index_reduce_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_cholesky_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_cholesky_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_cond_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_eig_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_ldl_factor_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_matrix_power_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_qr_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_linalg_svdvals_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_lu_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_masked_cumsum_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_matmul_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_median_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_new_empty_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_new_empty_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_cross_entropy_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_interpolate_area_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_interpolate_linear_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_linear_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_max_pool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_max_unpool2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_multi_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nn_functional_upsample_nearest_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_nonzero_static_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_normal_in_place_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_outer_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_pca_lowrank_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_qr_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_resize__cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_scatter_reduce_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_sparse_sampled_addmm_cpu_float32, test/test_ops.py::TestCommonCPU::test_compare_cpu_special_zeta_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_as_strided_partial_views_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_atleast_1d_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_bfloat16_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_cfloat_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_column_stack_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_empty_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_long_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_new_empty_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_nonzero_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_norm_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_norm_inf_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_ones_like_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_permute_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_positive_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_squeeze_copy_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_unflatten_cpu_complex32, test/test_ops.py::TestCommonCPU::test_complex_half_reference_testing_vsplit_cpu_complex32, test/test_ops.py::TestCommonCPU::test_dtypes___radd___cpu, test/test_ops.py::TestCommonCPU::test_dtypes__batch_norm_with_update_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_cfloat_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs__conversions_chalf_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_alias_copy_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_all_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_amin_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_any_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_as_strided_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_as_strided_scatter_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_asin_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_broadcast_tensors_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_broadcast_to_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_cauchy_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_exp_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_expm1_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_eye_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_fft2_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_ihfft2_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_ihfft_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_fft_irfft2_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_hsplit_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_imag_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_isinf_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_isposinf_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_le_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_linalg_diagonal_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_linalg_svdvals_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_lt_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_mean_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_minimum_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_new_empty_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_new_full_cpu, test/test_ops.py::TestCommonCPU::test_dtypes__refs_new_ones_cpu, 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test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_max_pool1d_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_max_pool2d_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_nn_functional_threshold_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_normal_number_mean_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_rad2deg_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_reciprocal_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_remainder_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_renorm_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_rot90_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_scalar_tensor_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_select_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_select_scatter_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_signal_windows_gaussian_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_signal_windows_nuttall_cpu, test/test_ops.py::TestCommonCPU::test_dtypes_special_bessel_y0_cpu, 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test/test_ops.py::TestCommonCPU::test_errors_nn_functional_max_pool1d_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_prelu_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_rms_norm_cpu, test/test_ops.py::TestCommonCPU::test_errors_nn_functional_triplet_margin_loss_cpu, test/test_ops.py::TestCommonCPU::test_errors_remainder_cpu, test/test_ops.py::TestCommonCPU::test_errors_roll_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_randn_like_layout2_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_sum_layout0_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_zeros_like_layout2_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_zeros_like_layout3_cpu, test/test_ops.py::TestCommonCPU::test_errors_sparse_zeros_like_layout4_cpu, test/test_ops.py::TestCommonCPU::test_errors_special_chebyshev_polynomial_u_cpu, test/test_ops.py::TestCommonCPU::test_errors_sub_cpu, 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test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_conj_physical_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_cosh_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_diff_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_dot_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_eye_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_hfft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_fft_irfft2_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_geqrf_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_gt_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_histogramdd_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_lerp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_cholesky_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_cross_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_eigvals_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_lu_factor_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_matrix_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_matrix_rank_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linalg_multi_dot_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_linspace_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_log10_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_log2_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logcumsumexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logical_and_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logical_or_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logspace_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_lu_unpack_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_maximum_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_mode_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_mul_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_nextafter_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_norm_fro_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_ones_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_reciprocal_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_renorm_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_scatter_reduce_mean_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sin_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sort_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_airy_ai_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_bessel_j0_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_sqrt_cpu_float32, test/test_ops.py::TestCommonCPU::test_meta_consistency_out_dtype_mismatch_stack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices___rpow___cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices__chunk_cat_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices__native_batch_norm_legit_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices__unsafe_masked_index_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices__unsafe_masked_index_put_accumulate_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_addcdiv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_addcmul_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_addcmul_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_addmm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_addmv_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_amin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_argmin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_argwhere_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_as_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_asin_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_atan2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_atleast_2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_atleast_3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_atleast_3d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bincount_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_block_diag_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bmm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_bmm_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bool_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_broadcast_tensors_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_bucketize_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_byte_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cartesian_prod_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_ceil_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_clone_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_conj_physical_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_corrcoef_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cos_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cov_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_cummax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_cumulative_trapezoid_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_deg2rad_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_deg2rad_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_diag_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_diagflat_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_digamma_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_div_no_rounding_mode_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_div_no_rounding_mode_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_dot_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_double_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_double_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_dsplit_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_empty_like_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_empty_permuted_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_erfinv_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_erfinv_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_expand_as_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fft2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fft_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_fft_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_hfftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifftn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ifftshift_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ihfft_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_ihfftn_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_irfft2_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fft_rfft_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_float_power_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_fmax_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_fmin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_histc_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_hsplit_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_hypot_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_copy_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_reduce_amax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_index_select_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_isnan_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_diagonal_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_householder_product_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_ldl_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_lu_factor_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_multi_dot_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_norm_subgradients_at_zero_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_pinv_singular_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linalg_solve_ex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_linspace_tensor_overload_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_log_normal_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_log_softmax_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logical_xor_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_logsumexp_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_long_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_long_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_lt_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_lu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_cumsum_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_masked_sum_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_matmul_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_maximum_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_median_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_meshgrid_list_of_tensors_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_min_binary_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_movedim_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_movedim_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_msort_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_msort_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mul_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_narrow_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_native_batch_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_empty_strided_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_new_ones_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_conv1d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_conv3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_conv_transpose3d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_conv_transpose3d_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_dropout2d_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_hardswish_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pad_constant_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_pad_replicate_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_prelu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_silu_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_soft_margin_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_softmin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_triplet_margin_loss_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_nonzero_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_pca_lowrank_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_polygamma_polygamma_n_0_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_positive_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_put_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_rad2deg_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_randint_like_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_randn_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_remainder_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_repeat_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_repeat_interleave_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_reshape_as_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_round_decimals_3_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_add_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_reduce_prod_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_scatter_reduce_sum_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_sgn_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sigmoid_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_exponential_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_general_hamming_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_signal_windows_kaiser_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_sinh_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_sort_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_sparse_mm_reduce_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_bessel_y1_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_chebyshev_polynomial_u_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_entr_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_i1_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_log_ndtr_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_modified_bessel_i0_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_modified_bessel_i1_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_modified_bessel_k1_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_modified_bessel_k1_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_scaled_modified_bessel_k0_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_scaled_modified_bessel_k0_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_special_zeta_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_split_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_split_list_args_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_split_with_sizes_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_stack_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_stack_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_std_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_svd_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_take_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_topk_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_trace_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_transpose_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_trapezoid_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_triangular_solve_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_tril_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_triu_cpu_int64, test/test_ops.py::TestCommonCPU::test_multiple_devices_trunc_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unbind_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_unsqueeze_copy_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_view_as_complex_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_view_as_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_vsplit_cpu_float32, test/test_ops.py::TestCommonCPU::test_multiple_devices_zeros_cpu_int64, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values___radd___cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values___ror___cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_amax_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_amin_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_angle_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_atan2_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_byte_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_combinations_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_conj_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_contiguous_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_copysign_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_diff_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_double_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_dsplit_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_eq_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_fft_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_ifftn_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_ihfftn_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_irfftn_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fft_rfft_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_fmin_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_index_put_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_index_select_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_int_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_le_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_logical_or_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_long_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_lt_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_mH_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_masked_select_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_meshgrid_list_of_tensors_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_min_reduction_no_dim_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_msort_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_mul_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_narrow_copy_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_nn_functional_channel_shuffle_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_nn_functional_pixel_shuffle_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_nonzero_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_nonzero_static_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_polygamma_polygamma_n_2_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_real_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_softmax_with_dtype_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_sort_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_airy_ai_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_bessel_y1_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_chebyshev_polynomial_w_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_i0e_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_modified_bessel_i0_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_scaled_modified_bessel_k1_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_special_shifted_chebyshev_polynomial_u_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_split_with_sizes_copy_cpu_bool, test/test_ops.py::TestCommonCPU::test_non_standard_bool_values_split_with_sizes_cpu_bool, 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test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_instance_norm_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_interpolate_bilinear_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_kl_div_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_linear_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_margin_ranking_loss_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_max_pool3d_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_circular_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_constant_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_reflect_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_pad_replicate_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_softmin_with_dtype_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_softsign_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_nn_functional_triplet_margin_loss_cpu_float32, 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test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_prod_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_put_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_put_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_qr_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_ravel_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_reciprocal_cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_renorm_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_repeat_cpu_float32, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_reshape_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_resize__cpu_int64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_resolve_conj_cpu_complex64, test/test_ops.py::TestCommonCPU::test_noncontiguous_samples_resolve_conj_cpu_int64, 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test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_nll_loss_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nn_functional_tanhshrink_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_nonzero_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_norm_inf_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_norm_nuc_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_ones_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_prod_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_rand_like_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_randint_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_randint_like_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_roll_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_round_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_scatter_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_select_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signal_windows_exponential_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_signbit_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_softmax_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_laguerre_polynomial_l_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_modified_bessel_i1_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_special_spherical_bessel_j0_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_split_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_split_with_sizes_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_std_mean_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_std_mean_unbiased_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_t_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_tan_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_to_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_to_sparse_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_topk_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unbind_copy_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_unique_cpu_float32, test/test_ops.py::TestCompositeComplianceCPU::test_view_replay_zeros_cpu_float32, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs__conversions_bool_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs__conversions_double_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_abs_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_acosh_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_all_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_as_strided_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_constant_pad_nd_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_div_no_rounding_mode_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_empty_like_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_exp2_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_ifft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_fft_ifftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_float_power_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_log10_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_log1p_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_logical_and_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_mean_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_mul_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_narrow_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_nn_functional_softmin_with_dtype_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_norm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_sigmoid_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_sinc_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_special_log_softmax_with_dtype_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_sub_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_t_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_triu_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_unfold_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_unsqueeze_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_vdot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__refs_where_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view__unsafe_masked_index_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_abs_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_acos_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_addbmm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_addmv_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_angle_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_any_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_as_strided_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_as_strided_scatter_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_atleast_2d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_baddbmm_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_broadcast_tensors_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_cdouble_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_chalf_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_conj_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_conj_physical_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_cross_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_cumsum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_diag_embed_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_diagonal_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_diagonal_scatter_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_dist_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_dot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_empty_permuted_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_expand_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_eye_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_fft_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fft_ifftn_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_fliplr_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_gradient_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_index_add_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_isnan_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_item_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_jiterator_2inputs_2outputs_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_ldexp_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_multi_dot_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_pinv_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_solve_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linalg_vander_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_linspace_tensor_overload_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_cumsum_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_masked_fill_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv1d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv_transpose2d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_conv_transpose3d_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_pad_circular_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_softmin_with_dtype_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_nn_functional_softsign_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_normal_in_place_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_permute_copy_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_resize_as__cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_std_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_tile_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_to_sparse_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_trapezoid_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_unsafe_split_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_conj_view_view_cpu_complex64, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_T_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___rdiv___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view___rmul___cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_bfloat16_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs__conversions_bool_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_addr_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_alias_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_allclose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_as_strided_scatter_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_asinh_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_atleast_1d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_block_diag_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_cumprod_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_diag_embed_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_diagonal_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_diagonal_scatter_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_fftshift_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_hfftn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_ifft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_irfft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_fft_irfft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_flipud_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_float_power_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_hstack_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_istft_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logical_and_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logical_xor_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logspace_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_logspace_tensor_overload_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_masked_fill_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_nn_functional_tanhshrink_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_randn_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_renorm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_squeeze_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_stack_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_triu_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unbind_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_unsqueeze_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_var_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_var_mean_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_where_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view__refs_zeros_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_addcdiv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_alias_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_as_strided_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_atan_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_constant_pad_nd_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_count_nonzero_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_diagonal_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_expand_as_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_fftshift_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_hfft2_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fft_ifftshift_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_fliplr_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_float_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_isfinite_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_jiterator_unary_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_ldexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_cholesky_ex_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_lu_factor_ex_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_matrix_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_matrix_rank_hermitian_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_pinv_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_pinv_singular_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_linalg_vecdot_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_logsumexp_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_lu_solve_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_cumsum_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_fill_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_masked_var_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_new_empty_strided_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_conv_transpose2d_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_rms_norm_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_nn_functional_silu_complex_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_norm_inf_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_norm_nuc_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_randn_like_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_ravel_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_repeat_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_resolve_neg_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_roll_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_short_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sinc_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_split_with_sizes_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_sub_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_t_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_tile_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_transpose_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_triangular_solve_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_unbind_copy_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_conj_view_uniform_cpu_complex128, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_long_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_polar_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs__conversions_short_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_abs_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_add_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_allclose_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_amax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_arange_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_as_strided_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_asin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atanh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_atleast_3d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_broadcast_to_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_bucketize_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cat_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_clamp_min_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_copysign_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_cosh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_deg2rad_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_diagonal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_div_floor_rounding_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_equal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_erf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_exp2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_exponential_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_fft_rfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_flatten_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_flipud_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_geometric_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_index_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_isnan_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_isposinf_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_isreal_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_cross_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_linalg_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_log10_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_logspace_tensor_overload_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_maximum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_minimum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_new_full_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_hardshrink_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_hinge_embedding_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_log_softmax_with_dtype_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_pdist_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_tanhshrink_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_nn_functional_triplet_margin_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_remainder_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_renorm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_rot90_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_rsqrt_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_select_scatter_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sgn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_sin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_entr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_ndtri_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_special_xlog1py_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_std_mean_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_tanh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_transpose_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_transpose_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_unsqueeze_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_unsqueeze_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_var_mean_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view__refs_view_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_abs_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_addcmul_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_addmm_decomposed_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_addr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_allclose_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_aminmax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_argmax_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_argmin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_atan_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_atanh_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_atleast_1d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_bernoulli_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_clamp_max_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_cov_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_diagonal_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_diff_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_div_floor_rounding_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_dstack_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_empty_permuted_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_exponential_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_hfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_ihfft2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fft_rfftn_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_floor_divide_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_fmin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_frac_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_heaviside_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_hsplit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_index_put_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_isin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_isnan_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_item_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_kron_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_lerp_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_cholesky_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_cholesky_ex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_cross_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_householder_product_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_ldl_factor_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_ldl_factor_ex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_lstsq_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_lstsq_grad_oriented_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_lu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_pinv_singular_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_qr_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_solve_ex_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_linalg_tensorinv_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_logical_and_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_logical_or_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_long_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_amin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_masked_normalize_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_min_reduction_with_dim_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_mm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_mode_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_new_empty_strided_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_celu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_cosine_embedding_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_embedding_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_fractional_max_pool2d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_glu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_group_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_hardshrink_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_layer_norm_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_pool1d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_pool2d_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_max_unpool2d_grad_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_mish_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_multi_margin_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_multilabel_margin_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_nll_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_pad_circular_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_pairwise_distance_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_poisson_nll_loss_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_silu_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_softmin_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_nn_functional_unfold_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_ones_like_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_polar_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_polygamma_polygamma_n_2_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_positive_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_put_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_quantile_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_remainder_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_reshape_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_resize__cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_resolve_conj_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_roll_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_round_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_scatter_reduce_mean_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_scatter_reduce_prod_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_scatter_reduce_sum_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_short_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_signbit_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_sparse_mm_reduce_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_special_modified_bessel_k1_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_special_ndtri_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_special_xlog1py_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_std_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_sum_to_size_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_take_along_dim_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_take_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_tile_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_tril_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_unflatten_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_view_copy_cpu_float64, test/test_ops.py::TestMathBitsCPU::test_neg_view_vstack_cpu_float64, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_cfloat_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_char_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs__conversions_complex_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_abs_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_alias_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_asin_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_atanh__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_atleast_2d_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_bitwise_left_shift__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_bitwise_not_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_broadcast_tensors_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_broadcast_to_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_clamp_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_conj_physical_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_copysign_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_cosh__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_cumprod_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_deg2rad__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_diagonal_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_div__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_eq_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_erfc__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_exp2_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_expand_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fft_fftn_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fft_fftshift_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fft_hfft_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fft_ifftn_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fft_ifftshift_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fft_ihfft_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fft_rfft_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fft_rfftn_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fill__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_fmax_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_hypot_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_igammac_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_isnan_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_isposinf_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_istft_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_linspace_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_log__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_log_normal__cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_logical_and_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_logical_xor_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_logsumexp_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_narrow_copy_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_narrow_cpu, test/test_ops.py::TestRefsOpsInfoCPU::test_refs_are_in_decomp_table_op__refs_ne_cpu, 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test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_hermite_polynomial_h_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_special_i1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_squeeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_std_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_transpose_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_tril_indices_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_trunc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_unique_consecutive_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_unique_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_unsqueeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_unsqueeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_vdot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_view_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_autocast_xlogy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_baddbmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_bitwise_and_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_bitwise_not_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_fake_bool_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cauchy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cdist_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cfloat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_char_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cholesky_inverse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_cos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp__segment_reduce_offsets_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_as_strided_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_asinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_atan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_atleast_3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_bmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_ceil_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cfloat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_column_stack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_combinations_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_constant_pad_nd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_contiguous_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_corrcoef_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cos_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cosh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_cummin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_deg2rad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_diag_embed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_expand_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_fft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_fftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_hfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_ifft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_ihfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_irfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fft_irfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_fmod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_hstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_index_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_cholesky_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_householder_product_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_matrix_power_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_linalg_slogdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_logaddexp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_lu_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_cumsum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_masked_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_max_pool2d_with_indices_backward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_maximum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_min_binary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_min_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nanmean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nanmedian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_native_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_conv_transpose1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_conv_transpose2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_ctc_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_embedding_bag_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_fractional_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_gelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_grid_sample_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_interpolate_trilinear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_local_response_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_mse_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_prelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_smooth_l1_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_softsign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_norm_nuc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_permute_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_pinverse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_polar_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_polygamma_polygamma_n_1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_reciprocal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_resolve_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_rot90_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_slice_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_softmax_with_dtype_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_entr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_i0e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_i1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_split_with_sizes_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_squeeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_std_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_std_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_tan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_tanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_tensordot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unsafe_chunk_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_unsqueeze_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_view_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_view_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_amp_xlogy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_as_strided_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_atan2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_broadcast_to_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cholesky_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_column_stack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cumprod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_diagonal_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_div_no_rounding_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_div_trunc_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_double_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_dsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_exp2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_fftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_hfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_ifft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_ifft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_ifftshift_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_ihfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_ihfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_fft_rfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_i0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_index_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_lerp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_cholesky_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_eigh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_pinv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_solve_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_linalg_vander_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_log10_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_log1p_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_log_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_logdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_masked_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_matmul_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_maximum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_min_reduction_no_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mode_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mul_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_mv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_neg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_avg_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_avg_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_dropout_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_elu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_group_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_pool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_max_unpool3d_grad_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pad_reflect_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_pixel_shuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_nn_functional_poisson_nll_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_reciprocal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_remainder_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_resolve_conj_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_select_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_sin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_special_i0e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_split_list_args_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_std_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_take_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_to_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_to_sparse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_transpose_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_unsqueeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_view_as_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_view_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_crossref_backward_no_amp_zero__cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diag_embed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_hfft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_hfft_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_ifft2_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_fft_irfftn_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_flatten_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_hstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_igamma_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_index_reduce_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_int_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_isclose_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_isnan_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_lerp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_eigh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_lu_factor_ex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_lu_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_matrix_power_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_matrix_rank_hermitian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_multi_dot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_svd_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_linalg_vector_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_log1p_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_logical_not_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_long_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_argmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_log_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_masked_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_max_binary_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_maximum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_median_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_meshgrid_variadic_tensors_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_minimum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_movedim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_new_ones_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_avg_pool2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_binary_cross_entropy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_dropout2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_huber_loss_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_max_unpool1d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_multi_head_attention_forward_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_rms_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_nn_functional_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_norm_nuc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_normal_number_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_pinverse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_polar_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_rad2deg_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_remainder_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_renorm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_repeat_interleave_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_reshape_as_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_rot90_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_scatter_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_searchsorted_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_cosine_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_signal_windows_gaussian_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_chebyshev_polynomial_u_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_entr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_i1e_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_special_zeta_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_squeeze_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_std_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_std_mean_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_std_unbiased_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_svd_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tensor_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_tensordot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_triangular_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_unfold_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_var_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_view_as_complex_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_fake_zeros_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rmul___cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops___rxor___cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addcdiv_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addmm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_addmm_decomposed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_aminmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_arange_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_as_strided_partial_views_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_as_strided_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_asinh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_broadcast_shapes_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_broadcast_to_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cdouble_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_ceil_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cfloat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cholesky_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cholesky_inverse_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_clamp_min_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_combinations_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_cov_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_diag_embed_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_diagflat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_div_floor_rounding_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_empty_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_empty_strided_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_float_power_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_floor_divide_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_fmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_geometric_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_geqrf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_grid_sampler_2d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_hsplit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_hstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_igammac_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_index_add_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_index_fill_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_index_reduce_mean_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_index_select_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_isinf_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_kron_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_ldexp_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_diagonal_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_eigh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_eigvalsh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_lstsq_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_slogdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linalg_svdvals_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_linspace_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_logdet_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_lu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_lu_solve_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_cumprod_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_log_softmax_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_normalize_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_masked_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_max_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_maximum_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_min_reduction_with_dim_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_multinomial_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_native_batch_norm_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_cosine_similarity_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_bicubic_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_interpolate_linear_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_max_pool3d_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_pad_circular_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_pad_constant_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_prelu_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_softmin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_softsign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_nn_functional_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_pca_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_polar_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_put_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_quantile_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_randn_like_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_repeat_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_reshape_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_rot90_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_round_decimals_neg_3_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_rsqrt_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_scatter_reduce_amin_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sigmoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sign_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_general_cosine_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signal_windows_hamming_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_signbit_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sinc_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_slice_scatter_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sort_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_bessel_j0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_bessel_y1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_entr_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_erfcx_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_modified_bessel_k0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_scaled_modified_bessel_k1_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_special_zeta_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_split_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_stack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_sum_to_size_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_svd_lowrank_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_take_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_tanh_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_trapezoid_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unfold_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_unique_consecutive_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_vdot_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_view_copy_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_vstack_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_pointwise_ops_where_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_linspace_tensor_overload_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_float64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_logspace_tensor_overload_cpu_uint8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_bool, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_ones_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout__refs_zeros_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_arange_cpu_float16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_arange_cpu_float32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_full_cpu_complex64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_linspace_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_cpu_int64, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_tensor_overload_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_tensor_overload_cpu_int16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_tensor_overload_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_logspace_tensor_overload_cpu_int8, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_bfloat16, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_ones_cpu_int32, test/test_ops.py::TestFakeTensorCPU::test_strided_layout_zeros_cpu_uint8, test/test_ops.py::TestTagsCPU::test_tags___radd___cpu_float32, test/test_ops.py::TestTagsCPU::test_tags___rand___cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_allclose_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_any_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atleast_1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_atleast_3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_bitwise_and_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_bitwise_left_shift_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_bitwise_or_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_bitwise_right_shift_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_bucketize_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_cauchy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_chunk_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_clamp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_clamp_max_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_column_stack_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_conj_physical_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_cumprod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_div_no_rounding_mode_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_empty_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_fft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_hfftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_ifft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_ihfft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_irfft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_fft_irfft_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_float_power_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_heaviside_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_hypot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_index_fill_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_isfinite_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_linalg_vector_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_log_normal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_logaddexp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_logical_and_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_logical_xor_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_maximum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_new_empty_strided_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_celu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_channel_shuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_leaky_relu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_mish_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_pixel_unshuffle_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_relu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_normal__in_place_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_permute_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_prod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_ravel_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_reciprocal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_reshape_as_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_select_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_sin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_i0e_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_log_softmax_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_multigammaln_mvlgamma_p_3_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_special_softmax_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_take_along_dim_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_tril_indices_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags__refs_true_divide_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_trunc_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_unbind_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_var_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags__refs_view_as_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_addcmul_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_addmm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_amin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_asinh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_atan_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bfloat16_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_bool_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cdouble_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_chalf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cholesky_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_clamp_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_conj_physical_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cosh_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_cumulative_trapezoid_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_diff_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_dist_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_dot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_double_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_dsplit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_empty_like_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_erf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_expand_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_fftshift_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_hfft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_irfftn_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fft_rfft2_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_fmin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_half_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_hsplit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_hypot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_index_reduce_amin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_index_reduce_prod_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_isin_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_jiterator_2inputs_2outputs_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_jiterator_binary_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_jiterator_binary_return_by_ref_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_diagonal_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_inv_ex_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_lstsq_grad_oriented_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_matrix_rank_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_svd_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_linalg_vecdot_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_argmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_log_softmax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_masked_norm_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_maximum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_median_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_meshgrid_list_of_tensors_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_mv_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nan_to_num_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nansum_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_new_empty_strided_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_binary_cross_entropy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_cross_entropy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_dropout2d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_elu_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_fractional_max_pool3d_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_gaussian_nll_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_interpolate_nearest_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_linear_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_margin_ranking_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_max_unpool2d_grad_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_normalize_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_one_hot_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_relu6_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_silu_complex_cpu_complex64, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_softmin_with_dtype_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_nn_functional_triplet_margin_loss_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_norm_inf_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ormqr_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_polygamma_polygamma_n_3_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_polygamma_polygamma_n_4_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_quantile_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_ravel_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_resolve_conj_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_roll_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_round_decimals_3_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_add_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_amax_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_scatter_reduce_mean_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sigmoid_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_exponential_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_gaussian_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_signal_windows_kaiser_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sinc_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_slice_scatter_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_i0e_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_modified_bessel_i0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_scaled_modified_bessel_k0_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_special_xlog1py_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_sum_to_size_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_t_copy_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_trapezoid_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_trapz_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_tril_indices_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_unflatten_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unfold_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unique_consecutive_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_unravel_index_cpu_int64, test/test_ops.py::TestTagsCPU::test_tags_view_as_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_vsplit_cpu_float32, test/test_ops.py::TestTagsCPU::test_tags_zero__cpu_float32 2025-07-17T08:11:09.9829239Z 2025-07-17T08:11:10.8917237Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:10.8919069Z import pkg_resources 2025-07-17T08:11:11.0169209Z Running torch_np/test_unary_ufuncs 1/1 ... [2025-07-17 08:11:11.016579] 2025-07-17T08:11:11.0169776Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:11.0172089Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_unary_ufuncs.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:11:11.016921] 2025-07-17T08:11:12.7433736Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:12.7435371Z import pkg_resources 2025-07-17T08:11:12.8662475Z Running dynamo/test_nops 1/1 ... [2025-07-17 08:11:12.865943] 2025-07-17T08:11:12.8662953Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:12.8665178Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:11:12.866278] 2025-07-17T08:11:14.4857291Z 2025-07-17T08:11:14.4858231Z torch_np/test_unary_ufuncs 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_unary_ufuncs_1.1_9a2e150238bba287_.log 2025-07-17T08:11:14.4870460Z Running 42 items in this shard: test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_absolute, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arccos, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arccosh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arcsin, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arcsinh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arctan, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_arctanh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_cbrt, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_ceil, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_conjugate, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_cos, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_cosh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_deg2rad, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_degrees, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_exp, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_exp2, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_expm1, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_fabs, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_floor, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_isfinite, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_isinf, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_isnan, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log10, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log1p, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_log2, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_logical_not, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_negative, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_positive, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_rad2deg, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_radians, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_reciprocal, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_rint, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sign, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_signbit, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sin, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sinh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_sqrt, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_square, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_tan, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_tanh, test/torch_np/test_unary_ufuncs.py::TestUnaryUfuncs::test_trunc 2025-07-17T08:11:14.4882639Z 2025-07-17T08:11:14.5561474Z 2025-07-17T08:11:14.5562453Z test_custom_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_custom_ops_1.1_235e1904f7994440_.log 2025-07-17T08:11:14.5650402Z Running 276 items in this shard: test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op, test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op_with_CompositeExplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op_with_CompositeImplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_abstract_impl_on_existing_op_with_meta, test/test_custom_ops.py::TestCustomOp::test_autogen_aten_ops_are_pt2_compliant, test/test_custom_ops.py::TestCustomOp::test_autograd_function_backed_op, test/test_custom_ops.py::TestCustomOp::test_autograd_notimplemented, test/test_custom_ops.py::TestCustomOp::test_autograd_notimplemented_gradmode, test/test_custom_ops.py::TestCustomOp::test_backward_dict_grad_for_nontensor, test/test_custom_ops.py::TestCustomOp::test_backward_dict_invalid_keys, test/test_custom_ops.py::TestCustomOp::test_backward_dict_requires_keys_for_input_optional_tensors, test/test_custom_ops.py::TestCustomOp::test_backward_dict_requires_keys_for_input_tensors, test/test_custom_ops.py::TestCustomOp::test_backward_grads_are_tensor_or_none, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_CompositeImplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_incorrect_schema_mutable, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_incorrect_schema_no_output, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_incorrect_schema_views, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_with_key_key_Autograd, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_with_key_key_AutogradCPU, test/test_custom_ops.py::TestCustomOp::test_backward_impl_on_existing_op_with_key_key_AutogradCUDA, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_non_tensor, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_numel, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_tensorlist, test/test_custom_ops.py::TestCustomOp::test_backward_output_differentiability_type, test/test_custom_ops.py::TestCustomOp::test_backward_partially_registered, test/test_custom_ops.py::TestCustomOp::test_backward_returns_dict, test/test_custom_ops.py::TestCustomOp::test_backward_tensorlist_input_requires_list_grads, test/test_custom_ops.py::TestCustomOp::test_backward_tensorlist_input_requires_list_grads_none_or_Tensor, test/test_custom_ops.py::TestCustomOp::test_backward_tensorlist_input_requires_list_grads_with_same_numel, test/test_custom_ops.py::TestCustomOp::test_basic_make_fx, test/test_custom_ops.py::TestCustomOp::test_builtin_aten_ops_are_pt2_compliant, test/test_custom_ops.py::TestCustomOp::test_builtin_torchscript_ops, test/test_custom_ops.py::TestCustomOp::test_data_dependent_basic, test/test_custom_ops.py::TestCustomOp::test_data_dependent_compile, test/test_custom_ops.py::TestCustomOp::test_data_dependent_fake_tracing, test/test_custom_ops.py::TestCustomOp::test_data_dependent_nms_dynamic_compile, test/test_custom_ops.py::TestCustomOp::test_define_and_impl, test/test_custom_ops.py::TestCustomOp::test_define_bad_schema, test/test_custom_ops.py::TestCustomOp::test_define_validation, test/test_custom_ops.py::TestCustomOp::test_define_with_tags_list, test/test_custom_ops.py::TestCustomOp::test_define_with_tags_single, test/test_custom_ops.py::TestCustomOp::test_define_with_tags_tuple, test/test_custom_ops.py::TestCustomOp::test_defined_in_python, test/test_custom_ops.py::TestCustomOp::test_duplicate_impl, test/test_custom_ops.py::TestCustomOp::test_functionalize_error, test/test_custom_ops.py::TestCustomOp::test_impl_abstract_overload, test/test_custom_ops.py::TestCustomOp::test_impl_cpu, test/test_custom_ops.py::TestCustomOp::test_impl_device_cpu, test/test_custom_ops.py::TestCustomOp::test_impl_device_cuda, test/test_custom_ops.py::TestCustomOp::test_impl_device_function, test/test_custom_ops.py::TestCustomOp::test_impl_device_invalid, test/test_custom_ops.py::TestCustomOp::test_impl_function, test/test_custom_ops.py::TestCustomOp::test_impl_invalid_devices, test/test_custom_ops.py::TestCustomOp::test_impl_meta, test/test_custom_ops.py::TestCustomOp::test_impl_multiple, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CPU, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CUDA, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CompositeExplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_impl_on_existing_op_with_cpu_registration_key_CompositeImplicitAutograd, test/test_custom_ops.py::TestCustomOp::test_impl_separate, test/test_custom_ops.py::TestCustomOp::test_incorrect_schema_types, test/test_custom_ops.py::TestCustomOp::test_infer_schema_no_return, test/test_custom_ops.py::TestCustomOp::test_infer_schema_supported, test/test_custom_ops.py::TestCustomOp::test_infer_schema_unsupported, test/test_custom_ops.py::TestCustomOp::test_invalid_qualname, test/test_custom_ops.py::TestCustomOp::test_invalid_schemas, test/test_custom_ops.py::TestCustomOp::test_is_functional_schema, test/test_custom_ops.py::TestCustomOp::test_is_tensorlist_like_type, test/test_custom_ops.py::TestCustomOp::test_legacy_define, test/test_custom_ops.py::TestCustomOp::test_legacy_impl, test/test_custom_ops.py::TestCustomOp::test_lifetime, test/test_custom_ops.py::TestCustomOp::test_meta_for_data_dependent_shape_operation, test/test_custom_ops.py::TestCustomOp::test_name_must_match, test/test_custom_ops.py::TestCustomOp::test_new_data_dependent_symint, test/test_custom_ops.py::TestCustomOp::test_not_implemented_error, test/test_custom_ops.py::TestCustomOp::test_override_cea, test/test_custom_ops.py::TestCustomOp::test_override_fake, test/test_custom_ops.py::TestCustomOp::test_override_impl, test/test_custom_ops.py::TestCustomOp::test_override_meta, test/test_custom_ops.py::TestCustomOp::test_private_ctor, test/test_custom_ops.py::TestCustomOp::test_reserved_ns, test/test_custom_ops.py::TestCustomOp::test_resolve_packet, test/test_custom_ops.py::TestCustomOp::test_save_for_backward_inputs_are_namedtuple, test/test_custom_ops.py::TestCustomOp::test_schema_matches_signature, test/test_custom_ops.py::TestCustomOp::test_sequences, test/test_custom_ops.py::TestCustomOp::test_supported_param_types, test/test_custom_ops.py::TestCustomOp::test_supported_return_types_multi_return, test/test_custom_ops.py::TestCustomOp::test_supported_return_types_single_return, test/test_custom_ops.py::TestCustomOp::test_supported_schemas, test/test_custom_ops.py::TestCustomOp::test_symints, test/test_custom_ops.py::TestCustomOp::test_unsupported_param_types, test/test_custom_ops.py::TestCustomOp::test_unsupported_schemas, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_inplace, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_dynamic__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_inplace, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_aot_dispatch_static__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_inplace, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_autograd_registration__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_dont_generate, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_inplace, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_faketensor__test_nonzero, test/test_custom_ops.py::MiniOpTest::test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_inplace, test/test_custom_ops.py::MiniOpTest::test_mm, test/test_custom_ops.py::MiniOpTest::test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_nonzero, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_aten_mm, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_aten_nonzero, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_aten_sin_, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_mini_op_test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_mini_op_test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_pt2_compliant_tag_mini_op_test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_schema__test_delayed_error, test/test_custom_ops.py::MiniOpTest::test_schema__test_delayed_error_no_requires_grad, test/test_custom_ops.py::MiniOpTest::test_schema__test_incorrect_schema, test/test_custom_ops.py::MiniOpTest::test_schema__test_inplace, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm_errors, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm_fake, test/test_custom_ops.py::MiniOpTest::test_schema__test_mm_meta, test/test_custom_ops.py::MiniOpTest::test_schema__test_no_abstract, test/test_custom_ops.py::MiniOpTest::test_schema__test_nonzero, test/test_custom_ops.py::TestCustomOpAPI::test_any_output_is_alias_to_input_or_output, test/test_custom_ops.py::TestCustomOpAPI::test_any_requires_grad, test/test_custom_ops.py::TestCustomOpAPI::test_basic, test/test_custom_ops.py::TestCustomOpAPI::test_compile, test/test_custom_ops.py::TestCustomOpAPI::test_default_values, test/test_custom_ops.py::TestCustomOpAPI::test_disallows_output_aliasing, test/test_custom_ops.py::TestCustomOpAPI::test_factory_function, test/test_custom_ops.py::TestCustomOpAPI::test_fake, test/test_custom_ops.py::TestCustomOpAPI::test_kwarg_only_tensors, test/test_custom_ops.py::TestCustomOpAPI::test_layout_constraint_tags, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast_list_input, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast_multiple_times, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autocast_multiple_times_different_devices, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autograd, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_autograd_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_0, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_1, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_2, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_3, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_4, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_fake_source_idx_5, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_kernel, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_kernel_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch_rule_mode, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_torch_dispatch_rule_subclass, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_library_decorator, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_op_decorator, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_register_multiple_times, test/test_custom_ops.py::TestCustomOpAPI::test_library_register_vmap_register_multiple_times_2, test/test_custom_ops.py::TestCustomOpAPI::test_library_schema_infer, test/test_custom_ops.py::TestCustomOpAPI::test_manual_schema, test/test_custom_ops.py::TestCustomOpAPI::test_manual_schema_error, test/test_custom_ops.py::TestCustomOpAPI::test_multi_types, test/test_custom_ops.py::TestCustomOpAPI::test_mutated, test/test_custom_ops.py::TestCustomOpAPI::test_mutated_error, test/test_custom_ops.py::TestCustomOpAPI::test_mutated_unknown, test/test_custom_ops.py::TestCustomOpAPI::test_no_grad_skips_autograd, test/test_custom_ops.py::TestCustomOpAPI::test_overloading, test/test_custom_ops.py::TestCustomOpAPI::test_register_autograd_defaults, test/test_custom_ops.py::TestCustomOpAPI::test_register_autograd_error_cases, test/test_custom_ops.py::TestCustomOpAPI::test_register_autograd_kwargonly_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_register_vmap_defaults, test/test_custom_ops.py::TestCustomOpAPI::test_register_vmap_kwargonly_low_level, test/test_custom_ops.py::TestCustomOpAPI::test_replacement, test/test_custom_ops.py::TestCustomOpAPI::test_set_kernel_enabled, test/test_custom_ops.py::TestCustomOpAPI::test_split_device, test/test_custom_ops.py::TestCustomOpAPI::test_supports_tensorlist, test/test_custom_ops.py::MiniOpTestOther::test_aot_dispatch_dynamic__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_aot_dispatch_static__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_autograd_registration__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_faketensor__test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_nonzero_again, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_aten_mm, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_aten_nonzero, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_aten_sin_, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_mini_op_test_delayed_error, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_mini_op_test_incorrect_schema, test/test_custom_ops.py::MiniOpTestOther::test_pt2_compliant_tag_mini_op_test_no_abstract, test/test_custom_ops.py::MiniOpTestOther::test_schema__test_nonzero_again, test/test_custom_ops.py::TestGenerateOpcheckTests::test_MiniOpTest, test/test_custom_ops.py::TestGenerateOpcheckTests::test_dont_generate_decorator, test/test_custom_ops.py::TestGenerateOpcheckTests::test_failures_dict_validation, test/test_custom_ops.py::TestGenerateOpcheckTests::test_generate_repro_no_save_data, test/test_custom_ops.py::TestGenerateOpcheckTests::test_generate_repro_save_data, test/test_custom_ops.py::TestGenerateOpcheckTests::test_is_inside_opcheck_mode, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck_bad_op, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck_customopdef, test/test_custom_ops.py::TestGenerateOpcheckTests::test_opcheck_does_not_require_extra_deps, test/test_custom_ops.py::TestTypeConversion::test_mixed_types, test/test_custom_ops.py::TestTypeConversion::test_optional, test/test_custom_ops.py::TestTypeConversion::test_simple_tuple, test/test_custom_ops.py::TestTypeConversion::test_supported_types, test/test_custom_ops.py::TestOpProfiles::test_duplicate_registration_custom_op, test/test_custom_ops.py::TestOpProfiles::test_duplicate_registration_impl, test/test_custom_ops.py::TestOpProfiles::test_fake_registration, test/test_custom_ops.py::TestOpProfiles::test_save_to_file, test/test_custom_ops.py::TestOpProfiles::test_version, test/test_custom_ops.py::TestOpProfiles::test_yaml, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_False_dynamic_False_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_False_dynamic_True_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_auto_dynamic_False_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_aot_autograd_check_degenerate_cases_check_gradients_auto_dynamic_True_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_assert_raises_regex_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registered_at_backend_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_autograd_kernel_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_compositeimplicitautograd_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_incorrect_composite_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_autograd_registration_check_incorrect_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_global_state_mutation_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_incorrect_abstract_impl_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_incorrect_schema_mutation_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_incorrect_schema_view_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_missing_abstract_impl_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_missing_functionalization_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_fails_basic_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyCatCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyCubeCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyMulCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyMulScalarCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyNMSCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyNonzeroCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpySortCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpySplitCopyCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyTakeCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_opinfo_NumpyViewCopyCustomOp_cpu_float32, test/test_custom_ops.py::TestCustomOpTestingCPU::test_opcheck_unbacked_stride_cpu, test/test_custom_ops.py::TestCustomOpTestingCPU::test_single_element_tuple_output_cpu 2025-07-17T08:11:14.5737381Z 2025-07-17T08:11:16.6855747Z 2025-07-17T08:11:16.6856656Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_206c013df98acbc7_.log 2025-07-17T08:11:16.6858171Z Running 4 items in this shard: test/dynamo/test_nops.py::NopTests::test1, test/dynamo/test_nops.py::NopTests::test2, test/dynamo/test_nops.py::NopTests::test3, test/dynamo/test_nops.py::NopTests::test_extended_args 2025-07-17T08:11:16.6859099Z 2025-07-17T08:11:17.6556874Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:17.6559843Z import pkg_resources 2025-07-17T08:11:17.6646813Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:17.6649742Z import pkg_resources 2025-07-17T08:11:17.8074324Z Running inductor/test_move_constructors_to_cuda 1/1 ... [2025-07-17 08:11:17.807083] 2025-07-17T08:11:17.8075125Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:17.8077267Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_move_constructors_to_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:11:17.807466] 2025-07-17T08:11:17.8147760Z Running backends/xeon/test_launch 1/1 ... [2025-07-17 08:11:17.814542] 2025-07-17T08:11:17.8148580Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:17.8151518Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'backends/xeon/test_launch.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'] ... [2025-07-17 08:11:17.814860] 2025-07-17T08:11:19.8354066Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:19.8355692Z import pkg_resources 2025-07-17T08:11:19.9611088Z Running inductor/test_triton_kernels 1/1 ... [2025-07-17 08:11:19.960832] 2025-07-17T08:11:19.9611594Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:19.9614097Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_triton_kernels.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'] ... [2025-07-17 08:11:19.961174] 2025-07-17T08:11:22.8944800Z 2025-07-17T08:11:22.8945999Z backends/xeon/test_launch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/backends.xeon.test_launch_1.1_73a31cf99144b6ea_.log 2025-07-17T08:11:22.8947413Z Running 2 items in this shard: test/backends/xeon/test_launch.py::TestTorchrun::test_cpu_info, test/backends/xeon/test_launch.py::TestTorchrun::test_multi_threads 2025-07-17T08:11:22.8948155Z 2025-07-17T08:11:24.6856348Z 2025-07-17T08:11:24.6857543Z inductor/test_move_constructors_to_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_move_constructors_to_cuda_1.1_58d10c4f1ef0c7c1_.log 2025-07-17T08:11:24.6858392Z 2025-07-17T08:11:26.0761629Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:26.0763228Z import pkg_resources 2025-07-17T08:11:26.2006691Z Running inductor/test_async_compile 1/1 ... [2025-07-17 08:11:26.200354] 2025-07-17T08:11:26.2007208Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:26.2009213Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_async_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'] ... [2025-07-17 08:11:26.200683] 2025-07-17T08:11:27.7372884Z 2025-07-17T08:11:27.7373863Z inductor/test_triton_kernels 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_triton_kernels_1.1_5c2e9b39a638e203_.log 2025-07-17T08:11:27.7546593Z Running 346 items in this shard: test/inductor/test_triton_kernels.py::KernelTests::test_constexpr_dynamic_shapes_wrapped_False_autotune_False, test/inductor/test_triton_kernels.py::KernelTests::test_constexpr_dynamic_shapes_wrapped_False_autotune_True, test/inductor/test_triton_kernels.py::KernelTests::test_constexpr_dynamic_shapes_wrapped_True_autotune_False, test/inductor/test_triton_kernels.py::KernelTests::test_constexpr_dynamic_shapes_wrapped_True_autotune_True, test/inductor/test_triton_kernels.py::KernelTests::test_i64_input, test/inductor/test_triton_kernels.py::KernelTests::test_kernel_inline_asm_quotes_double, test/inductor/test_triton_kernels.py::KernelTests::test_kernel_inline_asm_quotes_single, test/inductor/test_triton_kernels.py::KernelTests::test_kernel_with_docstring_quotes_double, test/inductor/test_triton_kernels.py::KernelTests::test_kernel_with_docstring_quotes_single, test/inductor/test_triton_kernels.py::KernelTests::test_layout_constraint_needs_fixed_stride_order, test/inductor/test_triton_kernels.py::KernelTests::test_no_nan_kernels, test/inductor/test_triton_kernels.py::KernelTests::test_on_device_tma_dynamic_False_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_on_device_tma_dynamic_False_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_on_device_tma_dynamic_True_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_on_device_tma_dynamic_True_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_capture_and_functionalize_dynamic_False_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_capture_and_functionalize_dynamic_False_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_capture_and_functionalize_dynamic_True_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_capture_and_functionalize_dynamic_True_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_False_backend_aot_eager_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_False_backend_aot_eager_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_False_backend_eager_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_False_backend_eager_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_False_backend_inductor_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_False_backend_inductor_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_True_backend_aot_eager_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_True_backend_aot_eager_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_True_backend_eager_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_True_backend_eager_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_True_backend_inductor_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_1d_dynamic_True_backend_inductor_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_False_backend_aot_eager_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_False_backend_aot_eager_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_False_backend_eager_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_False_backend_eager_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_True_backend_aot_eager_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_True_backend_aot_eager_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_True_backend_eager_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_2d_dynamic_True_backend_eager_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_dedup_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_descriptor_dedup_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_False_after_create_desc_False_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_False_after_create_desc_False_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_False_after_create_desc_True_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_False_after_create_desc_True_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_True_after_create_desc_False_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_True_after_create_desc_False_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_True_after_create_desc_True_tma_version_new, test/inductor/test_triton_kernels.py::KernelTests::test_tma_graph_breaks_after_data_ptr_True_after_create_desc_True_tma_version_old, test/inductor/test_triton_kernels.py::KernelTests::test_triton_attrs_dict_equal_1_None_format, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_eager_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_eager_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_eager_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_eager_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_eager_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_eager_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_inductor_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_inductor_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_inductor_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_inductor_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_inductor_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_False_backend_inductor_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_eager_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_eager_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_eager_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_eager_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_eager_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_eager_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_inductor_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_inductor_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_inductor_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_inductor_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_inductor_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_False_dynamic_True_backend_inductor_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_eager_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_eager_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_eager_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_eager_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_eager_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_eager_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_inductor_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_inductor_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_inductor_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_inductor_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_inductor_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_False_backend_inductor_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_eager_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_eager_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_eager_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_eager_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_eager_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_eager_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_inductor_grid_type_1_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_inductor_grid_type_1_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_inductor_grid_type_2_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_inductor_grid_type_2_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_inductor_grid_type_3_tdlp_0, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_2d_autotune_grad_True_dynamic_True_backend_inductor_grid_type_3_tdlp_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_False_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_False_dynamic_True_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_False_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_aot_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_eager_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_eager_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_eager_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_inductor_grid_type_1, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_inductor_grid_type_2, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_grad_True_dynamic_True_backend_inductor_grid_type_3, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_with_unsupported_args_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_with_unsupported_args_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_autotune_with_unsupported_args_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_caching, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_caching_duplicate, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_constants, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_dependancies, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_different_shapes_size_16_dynamic_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_different_shapes_size_16_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_different_shapes_size_4_dynamic_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_different_shapes_size_4_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_dtype_view_cfg_cpp_wrapper, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_dtype_view_cfg_normal, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_empty_autotune_config_dict_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_empty_autotune_config_dict_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_empty_autotune_config_dict_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_equal_to_1_arg_dynamic_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_equal_to_1_arg_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_equal_to_1_float_arg_dynamic_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_equal_to_1_float_arg_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_fallback, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_float64_constant_float16, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_float64_constant_float32, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_float64_constant_float64, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_functionalize, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_global_constexpr, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_higher_order_func, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_inner_triton_function_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_inner_triton_function_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_inner_triton_function_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_inputs_buffer_reuse, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_matmul_tracking, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_multi_kernel_grad_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_multi_kernel_grad_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_multiple_outputs_dynamic_False_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_multiple_outputs_dynamic_False_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_multiple_outputs_dynamic_False_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_multiple_outputs_dynamic_True_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_multiple_outputs_dynamic_True_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_multiple_outputs_dynamic_True_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_mutation_not_mark_dirty, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_mutation_type, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_False_dynamic_False_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_False_dynamic_False_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_False_dynamic_False_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_False_dynamic_True_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_False_dynamic_True_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_False_dynamic_True_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_True_dynamic_False_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_True_dynamic_False_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_True_dynamic_False_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_True_dynamic_True_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_True_dynamic_True_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_native_grad_True_dynamic_True_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_no_clones_grad_False_dynamic_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_no_clones_grad_False_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_no_clones_grad_True_dynamic_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_no_clones_grad_True_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_none_args, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_num_ctas_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_num_ctas_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_num_ctas_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_out_of_order, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_reinplace_inplaceable_pass, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_restore_value_backend_aot_eager_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_restore_value_backend_aot_eager_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_restore_value_backend_eager_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_restore_value_backend_eager_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_restore_value_backend_inductor_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_restore_value_backend_inductor_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_slice_and_view_input, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_kwargs_with_autotune_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_kwargs_with_autotune_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_kwargs_with_autotune_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_kwargs_without_autotune_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_kwargs_without_autotune_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_kwargs_without_autotune_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_params_autotune_False_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_params_autotune_False_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_params_autotune_False_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_params_autotune_True_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_params_autotune_True_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_special_params_autotune_True_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_strided_input, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_strided_input_nonzero_offset, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_tracing_dynamic_False, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_tracing_dynamic_True, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_triton_dtype_dynamic_False_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_triton_dtype_dynamic_False_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_triton_dtype_dynamic_False_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_triton_dtype_dynamic_True_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_triton_dtype_dynamic_True_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_triton_dtype_dynamic_True_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_unbacked_shape_tensor_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_unbacked_shape_tensor_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_unbacked_shape_tensor_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_various_args, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_grad_option_grad_fn0_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_grad_option_grad_fn0_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_grad_option_grad_fn0_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_grad_option_grad_fn1_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_grad_option_grad_fn1_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_grad_option_grad_fn1_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_imported_symbol, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_imported_symbol_with_custom_name, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_kernel_param, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_views_dynamic_False_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_views_dynamic_False_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_views_dynamic_False_backend_inductor, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_views_dynamic_True_backend_aot_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_views_dynamic_True_backend_eager, test/inductor/test_triton_kernels.py::KernelTests::test_triton_kernel_with_views_dynamic_True_backend_inductor, test/inductor/test_triton_kernels.py::MutationTests::test_add_for_loop, test/inductor/test_triton_kernels.py::MutationTests::test_add_for_loop2, test/inductor/test_triton_kernels.py::MutationTests::test_add_kernel_on_device_tma_new_api, test/inductor/test_triton_kernels.py::MutationTests::test_add_kernel_on_device_tma_old_api, test/inductor/test_triton_kernels.py::MutationTests::test_add_nested_for_loop, test/inductor/test_triton_kernels.py::MutationTests::test_add_nested_for_loop_multi_return, test/inductor/test_triton_kernels.py::MutationTests::test_argmax, test/inductor/test_triton_kernels.py::MutationTests::test_branch_with_multiple_yield_args, test/inductor/test_triton_kernels.py::MutationTests::test_cumsum, test/inductor/test_triton_kernels.py::MutationTests::test_fn_call_multi_return, test/inductor/test_triton_kernels.py::MutationTests::test_fn_call_one_return, test/inductor/test_triton_kernels.py::MutationTests::test_for_loop_arg, test/inductor/test_triton_kernels.py::MutationTests::test_for_loop_arg_2, test/inductor/test_triton_kernels.py::MutationTests::test_get_tma_stores, test/inductor/test_triton_kernels.py::MutationTests::test_labels, test/inductor/test_triton_kernels.py::MutationTests::test_nested_cond_op_kernel, test/inductor/test_triton_kernels.py::MutationTests::test_out_of_order_kernel, test/inductor/test_triton_kernels.py::MutationTests::test_out_of_order_kernel_call, test/inductor/test_triton_kernels.py::MutationTests::test_reduce_sum, test/inductor/test_triton_kernels.py::MutationTests::test_triton_kernel_inference_mode, test/inductor/test_triton_kernels.py::MutationTests::test_while_loop, test/inductor/test_triton_kernels.py::CustomOpTests::test_add_kernel_autotuned_False_dynamic_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_add_kernel_autotuned_False_dynamic_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_add_kernel_autotuned_True_dynamic_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_add_kernel_autotuned_True_dynamic_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_autotune_no_pre_or_post_hook_user_defined, test/inductor/test_triton_kernels.py::CustomOpTests::test_autotune_unbacked, test/inductor/test_triton_kernels.py::CustomOpTests::test_capture_triton_meta, test/inductor/test_triton_kernels.py::CustomOpTests::test_capture_triton_special_kwargs_dynamic_False_autotune_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_capture_triton_special_kwargs_dynamic_False_autotune_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_capture_triton_special_kwargs_dynamic_True_autotune_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_capture_triton_special_kwargs_dynamic_True_autotune_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_preserves_strides_variant_custom_op, test/inductor/test_triton_kernels.py::CustomOpTests::test_preserves_strides_variant_mutable_custom_op, test/inductor/test_triton_kernels.py::CustomOpTests::test_preserves_strides_variant_triton_kernel, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_dynamic_grid_no_recompile, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_False_backend_aot_eager_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_False_backend_aot_eager_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_False_backend_eager_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_False_backend_eager_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_False_backend_inductor_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_False_backend_inductor_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_True_backend_aot_eager_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_True_backend_aot_eager_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_True_backend_eager_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_True_backend_eager_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_True_backend_inductor_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_heuristic_non_strict_True_backend_inductor_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_False_backend_aot_eager_with_perf_model_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_False_backend_aot_eager_with_perf_model_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_False_backend_eager_with_perf_model_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_False_backend_eager_with_perf_model_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_False_backend_inductor_with_perf_model_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_False_backend_inductor_with_perf_model_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_True_backend_aot_eager_with_perf_model_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_True_backend_aot_eager_with_perf_model_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_True_backend_eager_with_perf_model_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_True_backend_eager_with_perf_model_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_True_backend_inductor_with_perf_model_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_non_strict_True_backend_inductor_with_perf_model_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_recompile_backend_aot_eager_with_perf_model_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_recompile_backend_aot_eager_with_perf_model_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_recompile_backend_eager_with_perf_model_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_recompile_backend_eager_with_perf_model_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_recompile_backend_inductor_with_perf_model_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_prune_configs_by_recompile_backend_inductor_with_perf_model_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_reset_to_zero_backend_aot_eager_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_reset_to_zero_backend_aot_eager_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_reset_to_zero_backend_eager_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_reset_to_zero_backend_eager_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_reset_to_zero_backend_inductor_autotune_at_compile_time_False, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_kernel_reset_to_zero_backend_inductor_autotune_at_compile_time_True, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_single_autotune_backend_aot_eager, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_single_autotune_backend_eager, test/inductor/test_triton_kernels.py::CustomOpTests::test_triton_single_autotune_backend_inductor, test/inductor/test_triton_kernels.py::CustomOpTests::test_wrap_triton_disabled_in_triton_op 2025-07-17T08:11:27.7717735Z 2025-07-17T08:11:27.9036072Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:27.9037645Z import pkg_resources 2025-07-17T08:11:28.0293276Z Running torch_np/numpy_tests/linalg/test_linalg 1/1 ... [2025-07-17 08:11:28.029015] 2025-07-17T08:11:28.0293800Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:28.0295947Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/linalg/test_linalg.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'] ... [2025-07-17 08:11:28.029351] 2025-07-17T08:11:30.9097816Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:31.0331006Z import pkg_resources 2025-07-17T08:11:31.0331475Z Running dynamo/test_skip_non_tensor 1/1 ... [2025-07-17 08:11:31.032758] 2025-07-17T08:11:31.0331924Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:31.0333613Z 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'] ... [2025-07-17 08:11:31.033104] 2025-07-17T08:11:33.1247898Z 2025-07-17T08:11:33.1248862Z inductor/test_async_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_async_compile_1.1_5a25e85d5477c8a0_.log 2025-07-17T08:11:33.1252004Z Running 6 items in this shard: test/inductor/test_async_compile.py::TestAsyncCompile::test_autotune_lookup_table_method_fork, test/inductor/test_async_compile.py::TestAsyncCompile::test_autotune_lookup_table_method_spawn, test/inductor/test_async_compile.py::TestAsyncCompile::test_autotune_lookup_table_method_subprocess, test/inductor/test_async_compile.py::TestAsyncCompile::test_pool_method_fork, test/inductor/test_async_compile.py::TestAsyncCompile::test_pool_method_spawn, test/inductor/test_async_compile.py::TestAsyncCompile::test_pool_method_subprocess 2025-07-17T08:11:33.1254462Z 2025-07-17T08:11:35.3174542Z 2025-07-17T08:11:35.3176090Z 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_b2f7d8f1858015a6_.log 2025-07-17T08:11:35.3182802Z Running 8 items in this shard: test/dynamo/test_skip_non_tensor.py::SkipNonTensorTests::test_add_skip, test/dynamo/test_skip_non_tensor.py::SkipNonTensorTests::test_add_tensor1, test/dynamo/test_skip_non_tensor.py::SkipNonTensorTests::test_add_tensor2, test/dynamo/test_skip_non_tensor.py::SkipNonTensorTests::test_add_tensor_dict, test/dynamo/test_skip_non_tensor.py::SkipNonTensorTests::test_add_tensor_list, test/dynamo/test_skip_non_tensor.py::SkipNonTensorTests::test_custom_list, test/dynamo/test_skip_non_tensor.py::SkipNonTensorTests::test_do_not_skip_side_effects, test/dynamo/test_skip_non_tensor.py::SkipNonTensorTests::test_recursive_list 2025-07-17T08:11:35.3188088Z 2025-07-17T08:11:36.2125984Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:36.2129238Z import pkg_resources 2025-07-17T08:11:36.3386247Z Running dynamo/test_interop 1/1 ... [2025-07-17 08:11:36.338235] 2025-07-17T08:11:36.3387049Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:36.3389323Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_interop.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:11:36.338615] 2025-07-17T08:11:38.3578004Z 2025-07-17T08:11:38.3579593Z torch_np/numpy_tests/linalg/test_linalg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.linalg.test_linalg_1.1_f76fb0a55f7166d3_.log 2025-07-17T08:11:38.3681254Z Running 268 items in this shard: test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_0_size, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_0_size_k, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_types_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_types_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_types_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSolve::test_types_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_0_size, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_types_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_types_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_types_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestInv::test_types_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_0_size, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_types_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_types_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_types_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEigvals::test_types_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_0_size, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_types_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_types_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_types_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestEig::test_types_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVD::test_empty_identity, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVD::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVD::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVD::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVD::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVD::test_types_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVD::test_types_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVD::test_types_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVD::test_types_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVDHermitian::test_empty_herm_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVDHermitian::test_generalized_empty_herm_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVDHermitian::test_generalized_herm_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVDHermitian::test_herm_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVDHermitian::test_types_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVDHermitian::test_types_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVDHermitian::test_types_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestSVDHermitian::test_types_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCond::test_basic_nonsvd, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCond::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCond::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCond::test_generalized_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCond::test_nan, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCond::test_singular, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCond::test_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCond::test_stacked_singular, test/torch_np/numpy_tests/linalg/test_linalg.py::TestPinv::test_empty_nonsq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestPinv::test_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestPinv::test_generalized_empty_nonsq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestPinv::test_generalized_empty_sq_cases, test/torch_np/numpy_tests/linalg/test_linalg.py::TestPinv::test_generalized_nonsq_cases, 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test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape0_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape0_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape0_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape1_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape1_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape1_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape1_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape2_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape2_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape2_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape2_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape3_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape3_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape3_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape3_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape4_dtype0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape4_dtype1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape4_dtype2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestCholesky::test_basic_property_shape4_dtype3, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc::test_byteorder_check, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc::test_generalized_raise_multiloop, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc::test_sdot_bug_8577, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc::test_xerbla_override, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_basic_function_with_dynamic_programming_optimization, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_basic_function_with_three_arguments, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_basic_function_with_two_arguments, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_dynamic_programming_logic, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_dynamic_programming_optimization_and_out, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_three_arguments_and_out, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_too_few_input_arrays, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_two_arguments_and_out, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_vector_as_first_and_last_argument, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_vector_as_first_argument, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMultiDot::test_vector_as_last_argument, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_non_square_handling_arr0_ind_2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_non_square_handling_arr1_ind_1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_ind_limit_ind_-2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_ind_limit_ind_0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_result, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_shape_shape0_ind_2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorinv::test_tensorinv_shape_shape1_ind_1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_non_square_handling_a0_axes0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_non_square_handling_a1_axes1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_tensorsolve_result_shape0, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_tensorsolve_result_shape1, test/torch_np/numpy_tests/linalg/test_linalg.py::TestTensorsolve::test_tensorsolve_result_shape2, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc2::test_blas64_dot, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc2::test_blas64_geqrf_lwork_smoketest, test/torch_np/numpy_tests/linalg/test_linalg.py::TestMisc2::test_unsupported_commontype 2025-07-17T08:11:38.3861546Z 2025-07-17T08:11:38.6616068Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:38.6617636Z import pkg_resources 2025-07-17T08:11:38.7873449Z Running inductor/test_torchinductor 1/2 ... [2025-07-17 08:11:38.787003] 2025-07-17T08:11:38.7874015Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:38.7876717Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor.py', '-m', 'not serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:11:38.787384] 2025-07-17T08:11:40.4094158Z 2025-07-17T08:11:40.4095328Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_6d7cc23dcdc38c51_.log 2025-07-17T08:11:40.4097593Z Running 5 items in this shard: test/dynamo/test_interop.py::InteropTests::test_fx_fn, test/dynamo/test_interop.py::InteropTests::test_script_fn, test/dynamo/test_interop.py::InteropTests::test_staticmethod_script_fn, test/dynamo/test_interop.py::InteropTests::test_trace_fn, test/dynamo/test_interop.py::InteropTests::test_vmap_in_graph 2025-07-17T08:11:40.4099060Z 2025-07-17T08:11:41.6434047Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:41.6435637Z import pkg_resources 2025-07-17T08:11:41.7667786Z Running inductor/test_metrics 1/1 ... [2025-07-17 08:11:41.766479] 2025-07-17T08:11:41.7668382Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:41.7671018Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_metrics.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:11:41.766816] 2025-07-17T08:11:43.5813472Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:43.5815028Z import pkg_resources 2025-07-17T08:11:43.7060838Z Running inductor/test_torchinductor_opinfo 3/26 ... [2025-07-17 08:11:43.705759] 2025-07-17T08:11:43.7061403Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:43.7063729Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=3', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:11:43.706107] 2025-07-17T08:11:48.6681801Z 2025-07-17T08:11:48.6683037Z inductor/test_metrics 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_metrics_1.1_d511e049a009d90f_.log 2025-07-17T08:11:48.6683901Z 2025-07-17T08:11:51.9869498Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:11:51.9871335Z import pkg_resources 2025-07-17T08:11:52.1123820Z Running inductor/test_torchinductor_opinfo 5/26 ... [2025-07-17 08:11:52.112014] 2025-07-17T08:11:52.1124797Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:11:52.1127143Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=5', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:11:52.112355] 2025-07-17T08:20:25.2002220Z 2025-07-17T08:20:25.2004781Z inductor/test_torchinductor 1/2 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_1.2_3c1c3bdeed186bda_.log 2025-07-17T08:20:25.2159956Z Running 463 items in this shard: test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast1_broadcast1, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast1_broadcast2, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast1_int, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast1_strided, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast2_broadcast1, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast2_broadcast2, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast2_broadcast3, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast2_transposed, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast3_dense, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast3_strided, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_broadcast3_transposed, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_dense_broadcast1, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_dense_broadcast2, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_dense_int, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_dense_strided, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_dense_transposed, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_double_broadcast2, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_double_broadcast3, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_double_dense, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_double_transposed, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_int_broadcast2, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_int_double, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_int_strided, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_strided_broadcast3, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_strided_int, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_transposed_broadcast1, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_transposed_broadcast3, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_transposed_dense, test/inductor/test_torchinductor.py::SweepInputsCpuTest::test_cpu_transposed_double, test/inductor/test_torchinductor.py::CpuTests::test__dyn_quant_matmul_4bit_cpu, test/inductor/test_torchinductor.py::CpuTests::test__unsafe_masked_index_cpu, test/inductor/test_torchinductor.py::CpuTests::test_abs_cpu, test/inductor/test_torchinductor.py::CpuTests::test_adaptive_avg_pool2d1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_adaptive_avg_pool2d2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_adaptive_avg_pool2d_low_prec_cpu, test/inductor/test_torchinductor.py::CpuTests::test_adaptive_max_pool2d1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_adaptive_max_pool2d2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_adaptive_max_pool2d3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_adaptive_pool_errors_with_long_cpu, test/inductor/test_torchinductor.py::CpuTests::test_add_complex5_cpu, test/inductor/test_torchinductor.py::CpuTests::test_add_complex6_cpu, test/inductor/test_torchinductor.py::CpuTests::test_add_const_int_cpu, test/inductor/test_torchinductor.py::CpuTests::test_add_inplace_permuted_cpu, test/inductor/test_torchinductor.py::CpuTests::test_aliased_buffer_reuse_cpu, test/inductor/test_torchinductor.py::CpuTests::test_angle_cpu, test/inductor/test_torchinductor.py::CpuTests::test_aoti_eager_cache_hit_cpu, test/inductor/test_torchinductor.py::CpuTests::test_aoti_eager_override_registration_cpu, test/inductor/test_torchinductor.py::CpuTests::test_arange1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_arange2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_arange5_cpu, test/inductor/test_torchinductor.py::CpuTests::test_arange6_cpu, test/inductor/test_torchinductor.py::CpuTests::test_argmax_argmin1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_argmax_argmin2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_argmax_min_int32_cpu, test/inductor/test_torchinductor.py::CpuTests::test_argmax_to_float_cpu, test/inductor/test_torchinductor.py::CpuTests::test_as_strided_scatter_cpu, test/inductor/test_torchinductor.py::CpuTests::test_assert_size_stride_op_name_pass_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d5_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d6_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d7_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d_backward3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d_backward4_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool2d_backward_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool3d_backward2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool3d_backward4_cpu, test/inductor/test_torchinductor.py::CpuTests::test_avg_pool3d_backward_cpu, test/inductor/test_torchinductor.py::CpuTests::test_batch_norm_2d_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bitwise3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_both_scalars_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_add_autotune_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_broadcast_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_int16_int16_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_int16_int32_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_int32_int8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_int32_uint8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_int64_int8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_int64_uint8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_int8_int64_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_int8_int8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_int8_uint8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_uint8_int16_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_uint8_int32_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_int_uint8_uint8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_nd_tiling_False_cpu, test/inductor/test_torchinductor.py::CpuTests::test_bucketize_nd_tiling_True_cpu, test/inductor/test_torchinductor.py::CpuTests::test_buffer_batch_norm_cpu, test/inductor/test_torchinductor.py::CpuTests::test_buffer_copied_in_graph_with_different_shapes_cpu, test/inductor/test_torchinductor.py::CpuTests::test_builtins_round_cpu, test/inductor/test_torchinductor.py::CpuTests::test_builtins_round_float_ndigits_neg_cpu, test/inductor/test_torchinductor.py::CpuTests::test_builtins_round_float_ndigits_zero_cpu, test/inductor/test_torchinductor.py::CpuTests::test_builtins_round_int_ndigits_pos_cpu, test/inductor/test_torchinductor.py::CpuTests::test_builtins_round_int_ndigits_zero_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_empty_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_inplace_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_negative_dim_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_of_loops_and_extern_kernel_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_unbacked_legacy_empty_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cat_upcasting_cpu, test/inductor/test_torchinductor.py::CpuTests::test_cauchy_cpu, test/inductor/test_torchinductor.py::CpuTests::test_clamp_type_promotion_non_tensor_cpu, test/inductor/test_torchinductor.py::CpuTests::test_clone_cpu, test/inductor/test_torchinductor.py::CpuTests::test_compar_cpu, test/inductor/test_torchinductor.py::CpuTests::test_computed_buffer_inlining_cpu, test/inductor/test_torchinductor.py::CpuTests::test_concat_add_inplace_cpu, test/inductor/test_torchinductor.py::CpuTests::test_config_option_dont_assume_alignment_cpu, test/inductor/test_torchinductor.py::CpuTests::test_config_option_dont_assume_alignment_cudagraphs_cpu, test/inductor/test_torchinductor.py::CpuTests::test_config_option_dont_assume_alignment_recompiles_cpu, test/inductor/test_torchinductor.py::CpuTests::test_consecutive_split_cumprod_cpu, test/inductor/test_torchinductor.py::CpuTests::test_consecutive_split_cumsum_cpu, test/inductor/test_torchinductor.py::CpuTests::test_const_int32_to_float_cpu, test/inductor/test_torchinductor.py::CpuTests::test_constant_pad_2d_cpu, test/inductor/test_torchinductor.py::CpuTests::test_constant_pad_float64_cpu, test/inductor/test_torchinductor.py::CpuTests::test_conv3d_channels_last_use_block_ptr_True_cpu, test/inductor/test_torchinductor.py::CpuTests::test_conv_backward_cpu, test/inductor/test_torchinductor.py::CpuTests::test_conv_bn_fuse_cpu, 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test/inductor/test_torchinductor.py::CpuTests::test_profiler_mark_wrapper_call_cpu, test/inductor/test_torchinductor.py::CpuTests::test_rand_like_deterministic_cpu, test/inductor/test_torchinductor.py::CpuTests::test_randint_cpu, test/inductor/test_torchinductor.py::CpuTests::test_randn_generator_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reduction2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reduction3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reduction4_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reflection_pad2d_backward_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reflection_pad2d_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reinterpret_dtypeview_cpu, test/inductor/test_torchinductor.py::CpuTests::test_relu_cpu, test/inductor/test_torchinductor.py::CpuTests::test_remove_no_ops_cpu, test/inductor/test_torchinductor.py::CpuTests::test_remove_noop_clone_cpu, test/inductor/test_torchinductor.py::CpuTests::test_remove_noop_copy_cpu, test/inductor/test_torchinductor.py::CpuTests::test_remove_noop_slice_cpu, test/inductor/test_torchinductor.py::CpuTests::test_remove_noop_slice_scatter_cpu, test/inductor/test_torchinductor.py::CpuTests::test_remove_noop_view_default_cpu, test/inductor/test_torchinductor.py::CpuTests::test_repeat_cpu, test/inductor/test_torchinductor.py::CpuTests::test_repeat_interleave_cpu, test/inductor/test_torchinductor.py::CpuTests::test_replication_pad_errors_with_bool_cpu, test/inductor/test_torchinductor.py::CpuTests::test_resize_cpu, test/inductor/test_torchinductor.py::CpuTests::test_reuse_buffers_with_aliasing_cpu, test/inductor/test_torchinductor.py::CpuTests::test_roll_cpu, test/inductor/test_torchinductor.py::CpuTests::test_round_cpu, test/inductor/test_torchinductor.py::CpuTests::test_rsqrt_cpu, test/inductor/test_torchinductor.py::CpuTests::test_rsqrt_dynamic_shapes_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scalar_output_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scaled_dot_product_attention_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter_add3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter_bf16_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter_reduce1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter_reduce2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scatter_reduce3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_scheduler_vertical_fusion1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sdpa_prefer_nd_tiling_False_use_block_ptr_False_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sdpa_prefer_nd_tiling_False_use_block_ptr_True_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sdpa_prefer_nd_tiling_True_use_block_ptr_False_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sdpa_prefer_nd_tiling_True_use_block_ptr_True_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sdpa_unaligned_mask_cpu, test/inductor/test_torchinductor.py::CpuTests::test_searchsorted_broadcast_cpu, test/inductor/test_torchinductor.py::CpuTests::test_searchsorted_cpu, test/inductor/test_torchinductor.py::CpuTests::test_setitem_with_int_parameter_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sgn_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sgn_extremal_cpu, test/inductor/test_torchinductor.py::CpuTests::test_shape_padding_cpu, test/inductor/test_torchinductor.py::CpuTests::test_shape_prop_torch_ones_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sigmoid_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sign_dtype_cpu, test/inductor/test_torchinductor.py::CpuTests::test_signbit_cpu, test/inductor/test_torchinductor.py::CpuTests::test_silu_cpu, test/inductor/test_torchinductor.py::CpuTests::test_simplify_loops_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice4_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice_mutation1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice_mutation3_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice_scatter2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice_scatter_reinplace_cpu, test/inductor/test_torchinductor.py::CpuTests::test_slice_view_with_graph_break_cpu, test/inductor/test_torchinductor.py::CpuTests::test_softmax_backward_data_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sort_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sort_stable_cpu, test/inductor/test_torchinductor.py::CpuTests::test_special_polygamma_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_cumprod_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_cumsum_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_cumsum_low_prec_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_reduction_with_int64_size_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_with_integer_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_with_sizes_with_unbacked_symints_cpu, test/inductor/test_torchinductor.py::CpuTests::test_split_with_unbacked_symints_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sqrt_dynamic_shapes_cpu, test/inductor/test_torchinductor.py::CpuTests::test_squeeze1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_squeeze2_cpu, test/inductor/test_torchinductor.py::CpuTests::test_squeeze_varargs_cpu, test/inductor/test_torchinductor.py::CpuTests::test_stack_cpu, test/inductor/test_torchinductor.py::CpuTests::test_strided_inputs_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sum5_cpu, test/inductor/test_torchinductor.py::CpuTests::test_sum_keepdims_cpu, test/inductor/test_torchinductor.py::CpuTests::test_tensor1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_tensor_index_slice_cpu, test/inductor/test_torchinductor.py::CpuTests::test_to_device_constant_cpu, test/inductor/test_torchinductor.py::CpuTests::test_to_device_cpu, test/inductor/test_torchinductor.py::CpuTests::test_to_dtype_cpu, test/inductor/test_torchinductor.py::CpuTests::test_to_memory_format_cpu, test/inductor/test_torchinductor.py::CpuTests::test_transpose_add_cpu, test/inductor/test_torchinductor.py::CpuTests::test_transpose_cpu, test/inductor/test_torchinductor.py::CpuTests::test_transposed_propagates_cpu, test/inductor/test_torchinductor.py::CpuTests::test_uint4x2_mixed_mm_cpu, test/inductor/test_torchinductor.py::CpuTests::test_uint_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unbacked_floordiv_simplify_errors_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unfold_zero_dimension_tensor_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unspec_inputs_float32_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unspec_inputs_float64_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unspec_inputs_int16_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unspec_inputs_int32_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unspec_inputs_int8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unspec_inputs_uint8_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unsqueeze_cpu, test/inductor/test_torchinductor.py::CpuTests::test_unsqueeze_inplace_cpu, test/inductor/test_torchinductor.py::CpuTests::test_upsample_nearest2d_cpu, test/inductor/test_torchinductor.py::CpuTests::test_upsample_nearest3d_cpu, test/inductor/test_torchinductor.py::CpuTests::test_var_correction_cpu, test/inductor/test_torchinductor.py::CpuTests::test_var_mean_div_by_cpu, test/inductor/test_torchinductor.py::CpuTests::test_var_mean_tile_reduction_False_cpu, test/inductor/test_torchinductor.py::CpuTests::test_vdd_clamp_cpu, test/inductor/test_torchinductor.py::CpuTests::test_vectorized_ops_masked_var_novec_cpu, test/inductor/test_torchinductor.py::CpuTests::test_view_as_complex_cpu, test/inductor/test_torchinductor.py::CpuTests::test_view_detach_cpu, test/inductor/test_torchinductor.py::CpuTests::test_view_uint8_through_differing_bitwidths_cpu, test/inductor/test_torchinductor.py::CpuTests::test_views1_cpu, test/inductor/test_torchinductor.py::CpuTests::test_views5_cpu, test/inductor/test_torchinductor.py::CpuTests::test_views7_cpu, test/inductor/test_torchinductor.py::CpuTests::test_weight_norm_bwd_cpu, test/inductor/test_torchinductor.py::CpuTests::test_where_broadcast_cpu, test/inductor/test_torchinductor.py::CpuTests::test_xblock_divides_xnumel_cpu, test/inductor/test_torchinductor.py::TestFull::test_full_dtype 2025-07-17T08:20:25.2310409Z 2025-07-17T08:20:28.3177941Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:20:28.3179509Z import pkg_resources 2025-07-17T08:20:28.4417154Z Running inductor/test_torchinductor_opinfo 7/26 ... [2025-07-17 08:20:28.441391] 2025-07-17T08:20:28.4417688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:20:28.4419502Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=7', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:20:28.441728] 2025-07-17T08:21:25.6448317Z 2025-07-17T08:21:25.6451962Z inductor/test_torchinductor_opinfo 5/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_5.26_387f12a0214ec950_.log 2025-07-17T08:21:25.6577348Z Running 139 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rdiv___cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addbmm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmv_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_alias_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_all_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argmax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argwhere_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asin_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atanh_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_2d_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_2d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bfloat16_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bfloat16_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_xor_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_xor_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_broadcast_to_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cat_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cdouble_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ceil_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_chunk_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_min_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_column_stack_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_copysign_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_deg2rad_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dot_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dsplit_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_equal_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expm1_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftshift_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftn_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftshift_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft2_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fill_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flatten_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fliplr_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flipud_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_power_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_power_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_geqrf_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_half_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_heaviside_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hstack_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_igammac_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amax_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amin_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isfinite_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isinf_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isposinf_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_kron_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ldexp_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cholesky_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_eigh_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_ldl_solve_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_vander_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumprod_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_fill_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_logsumexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_prod_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_std_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_sum_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_reduction_with_dim_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_list_of_tensors_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_meshgrid_variadic_tensors_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mm_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_msort_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mul_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nan_to_num_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmean_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nanmedian_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nansum_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_native_layer_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_empty_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_avg_pool2d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_max_pool1d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool2d_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_channel_shuffle_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv1d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv2d_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv_transpose2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cosine_embedding_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cross_entropy_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hardsigmoid_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_hardsigmoid_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_linear_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_margin_ranking_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_unpool1d_grad_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_unpool2d_grad_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_normalize_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_constant_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_relu_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_tanhshrink_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_0_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_remainder_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_renorm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_reshape_as_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize__cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize__cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize_as__cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_neg_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_exponential_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_scatter_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_softmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_u_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_v_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i1_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k1_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_polygamma_special_polygamma_n_0_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_to_size_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triangular_solve_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unbind_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unflatten_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_as_complex_cpu_float16 2025-07-17T08:21:25.6736698Z 2025-07-17T08:21:28.8901454Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:21:28.8903295Z import pkg_resources 2025-07-17T08:21:29.0155194Z Running inductor/test_torchinductor_opinfo 18/26 ... [2025-07-17 08:21:29.015191] 2025-07-17T08:21:29.0155750Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:21:29.0157758Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=18', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:21:29.015532] 2025-07-17T08:22:40.5888189Z 2025-07-17T08:22:40.5889929Z inductor/test_torchinductor_opinfo 3/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_3.26_f4641c58032d3d7c_.log 2025-07-17T08:22:40.6028043Z Running 132 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_H_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rsub___cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_abs_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acos_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acosh_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_add_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addbmm_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addmv_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amin_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_amin_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_aminmax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argsort_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_left_shift_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_block_diag_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_block_diag_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bmm_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_broadcast_tensors_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bucketize_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_byte_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_chunk_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_column_stack_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_combinations_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_constant_pad_nd_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_constant_pad_nd_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cos_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummax_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagflat_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dot_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dot_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_einsum_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_strided_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_equal_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eye_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eye_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fftshift_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfftn_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_irfft2_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfftn_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_full_like_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_half_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_half_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_heaviside_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isfinite_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isinf_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isreal_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_2inputs_2outputs_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_unary_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_kron_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_kthvalue_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_householder_product_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_pinv_hermitian_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_svd_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linspace_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_var_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_with_dim_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mul_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mul_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mvlgamma_mvlgamma_p_1_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nan_to_num_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_native_dropout_backward_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_native_dropout_backward_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ne_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_neg_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_ones_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_ones_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_avg_pool3d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_conv_transpose3d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout3d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_huber_loss_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_instance_norm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_bilinear_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_trilinear_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_interpolate_trilinear_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_layer_norm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_leaky_relu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_linear_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_margin_ranking_loss_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_multilabel_margin_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_constant_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_reflect_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pairwise_distance_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_rms_norm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_selu_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ones_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ones_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_3_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_3_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rad2deg_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randint_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randn_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randn_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_neg_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_short_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sinc_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_t_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_v_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_hermite_polynomial_h_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_scaled_modified_bessel_k0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_copy_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_with_sizes_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_to_sparse_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_topk_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_transpose_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tril_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_true_divide_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unbind_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unbind_copy_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_copy_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vsplit_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_xlogy_cpu_int64 2025-07-17T08:22:40.6161115Z 2025-07-17T08:22:43.8014839Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:22:43.8016461Z import pkg_resources 2025-07-17T08:22:43.9273290Z Running inductor/test_torchinductor_opinfo 20/26 ... [2025-07-17 08:22:43.926973] 2025-07-17T08:22:43.9273969Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:22:43.9275469Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=20', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:22:43.927309] 2025-07-17T08:30:26.7973290Z 2025-07-17T08:30:26.7974802Z inductor/test_torchinductor_opinfo 20/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_20.26_180b304ec44adc25_.log 2025-07-17T08:30:26.8093600Z Running 136 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_T_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmul___cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__unsafe_masked_index_put_accumulate_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addbmm_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addr_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_arange_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_arange_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argwhere_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bfloat16_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bincount_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bitwise_and_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_broadcast_tensors_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bucketize_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_byte_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cauchy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clamp_max_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_clone_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_column_stack_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_conj_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_conj_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_conj_physical_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_constant_pad_nd_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_contiguous_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cross_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_digamma_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_double_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dstack_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_equal_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expand_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft2_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft2_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fill_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fliplr_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flipud_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmax_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hstack_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_fill_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_mean_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_2inputs_2outputs_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_le_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_diagonal_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_ldl_factor_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_multi_dot_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_norm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_qr_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log2_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_softmax_with_dtype_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_not_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_xor_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logit_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mT_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumsum_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_std_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mean_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_median_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_with_dim_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_minimum_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_msort_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mvlgamma_mvlgamma_p_3_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nansum_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nansum_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_narrow_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_empty_strided_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_avg_pool2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_bilinear_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_celu_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cosine_embedding_loss_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_cross_entropy_cpu_float64, 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test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rsub_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_add_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_amax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_sum_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sgn_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sigmoid_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sin_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_slice_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_j0_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_y0_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_w_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_entr_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_erfcx_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_hermite_polynomial_h_cpu_float64, 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test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unbind_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_uniform_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_consecutive_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_copy_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zeros_like_cpu_int64 2025-07-17T08:30:26.8245124Z 2025-07-17T08:30:28.5081900Z Uploading artifacts took 1.71 seconds 2025-07-17T08:30:29.9554147Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:30:29.9555794Z import pkg_resources 2025-07-17T08:30:30.0802780Z Running inductor/test_torchinductor_opinfo 22/26 ... [2025-07-17 08:30:30.079966] 2025-07-17T08:30:30.0803314Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:30:30.0805513Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_opinfo.py', '-m', 'not serial', '--shard-id=22', '--num-shards=26', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:30:30.080311] 2025-07-17T08:31:04.9201959Z 2025-07-17T08:31:04.9203124Z inductor/test_torchinductor_opinfo 18/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_18.26_06ceae859bbaaacb_.log 2025-07-17T08:31:04.9278071Z Running 148 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_H_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_T_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_T_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___radd___cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rxor___cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__softmax_backward_data_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_abs_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_abs_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acos_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acosh_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_add_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_any_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argwhere_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_scatter_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_scatter_cpu_int32, 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test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_log_ndtr_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_i0_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k1_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtr_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_scaled_modified_bessel_k1_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_w_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_split_list_args_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_copy_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_take_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_topk_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unique_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsafe_chunk_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_as_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vstack_cpu_float16 2025-07-17T08:31:04.9352371Z 2025-07-17T08:31:08.0079877Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:08.0081625Z import pkg_resources 2025-07-17T08:31:08.1351570Z Running inductor/test_cuda_repro 1/1 ... [2025-07-17 08:31:08.134849] 2025-07-17T08:31:08.1352036Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:08.1354492Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cuda_repro.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:31:08.135192] 2025-07-17T08:31:14.7180172Z 2025-07-17T08:31:14.7181600Z inductor/test_torchinductor_opinfo 7/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_7.26_91099903a3774b75_.log 2025-07-17T08:31:14.7258047Z Running 150 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmul___cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rsub___cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__chunk_cat_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__native_batch_norm_legit_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_acosh_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addcmul_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_allclose_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argsort_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_partial_views_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_scatter_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atan_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_1d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_3d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bernoulli_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cdouble_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ceil_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_chalf_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_contiguous_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_corrcoef_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_count_nonzero_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummax_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_embed_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dist_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_dsplit_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_like_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eq_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfinv_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_fft_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft2_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifftshift_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfftn_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fill_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_flatten_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_float_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmod_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_frac_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ge_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_add_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_copy_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_fill_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amin_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_mean_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_prod_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_int_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isfinite_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isinf_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isinf_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isreal_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_unary_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_unary_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_le_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lgamma_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_diagonal_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_lstsq_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_solve_ex_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log1p_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log2_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logaddexp2_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logaddexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_and_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_tensor_overload_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logsumexp_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_long_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_lu_unpack_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mT_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_amax_cpu_uint8, 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test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_mish_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pad_replicate_negative_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_soft_margin_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_softsign_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_3_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_4_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_positive_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_prod_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rad2deg_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ravel_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resolve_conj_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_roll_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_round_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scalar_tensor_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scalar_tensor_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_amax_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_reduce_prod_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_select_scatter_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_short_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sin_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sparse_sampled_addmm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_bessel_y0_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_i0e_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_i0_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_i0_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k0_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_modified_bessel_k1_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtr_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtri_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_xlog1py_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_zeta_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_square_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_square_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_squeeze_copy_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_stack_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_svd_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tanh_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_transpose_copy_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trapezoid_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_triangular_solve_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_true_divide_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unravel_index_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unsqueeze_copy_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_var_mean_unbiased_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_view_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_vstack_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_where_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_zero__cpu_int32 2025-07-17T08:31:14.7333204Z 2025-07-17T08:31:15.1317551Z 2025-07-17T08:31:15.1318793Z inductor/test_cuda_repro 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cuda_repro_1.1_2cc91216718a648c_.log 2025-07-17T08:31:15.1319786Z 2025-07-17T08:31:17.8762749Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:17.8764314Z import pkg_resources 2025-07-17T08:31:18.0006726Z Running inductor/test_helion_kernels 1/1 ... [2025-07-17 08:31:18.000366] 2025-07-17T08:31:18.0007218Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:18.0010002Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_helion_kernels.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'] ... [2025-07-17 08:31:18.000724] 2025-07-17T08:31:18.3903448Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:18.3905025Z import pkg_resources 2025-07-17T08:31:18.5153166Z Running torch_np/test_random 1/1 ... [2025-07-17 08:31:18.514972] 2025-07-17T08:31:18.5153705Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:18.5155141Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_random.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'] ... [2025-07-17 08:31:18.515309] 2025-07-17T08:31:21.9348163Z 2025-07-17T08:31:21.9349316Z torch_np/test_random 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_random_1.1_52619443f23f092a_.log 2025-07-17T08:31:21.9364164Z Running 41 items in this shard: test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_False_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_array_use_numpy_True_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_False_random_sample, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func0, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func1, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func2, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func3, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func6, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_func7, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_random_random, test/torch_np/test_random.py::TestScalarReturn::test_rndm_scalar_use_numpy_True_random_sample, test/torch_np/test_random.py::TestShuffle::test_1d_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_1d_use_numpy_True, test/torch_np/test_random.py::TestShuffle::test_2d_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_2d_use_numpy_True, test/torch_np/test_random.py::TestShuffle::test_shuffle_list_use_numpy_False, test/torch_np/test_random.py::TestShuffle::test_shuffle_list_use_numpy_True, test/torch_np/test_random.py::TestChoice::test_choice_use_numpy_False, test/torch_np/test_random.py::TestChoice::test_choice_use_numpy_True, test/torch_np/test_random.py::TestNumpyGlobal::test_numpy_global 2025-07-17T08:31:21.9378972Z 2025-07-17T08:31:24.8751575Z 2025-07-17T08:31:24.8752632Z inductor/test_helion_kernels 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_helion_kernels_1.1_2de1240f9201f5b0_.log 2025-07-17T08:31:24.8754319Z Running 2 items in this shard: test/inductor/test_helion_kernels.py::HelionTests::test_add_kernel, test/inductor/test_helion_kernels.py::HelionTests::test_softmax_view_reshape 2025-07-17T08:31:24.8755113Z 2025-07-17T08:31:25.1343342Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:25.2607300Z import pkg_resources 2025-07-17T08:31:25.2607720Z Running test_namedtensor 1/1 ... [2025-07-17 08:31:25.260370] 2025-07-17T08:31:25.2608196Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:25.2609736Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtensor.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'] ... [2025-07-17 08:31:25.260715] 2025-07-17T08:31:27.9734505Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:27.9737624Z import pkg_resources 2025-07-17T08:31:28.0977912Z Running export/test_package 1/1 ... [2025-07-17 08:31:28.097399] 2025-07-17T08:31:28.0978772Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:28.0981250Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_package.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'] ... [2025-07-17 08:31:28.097762] 2025-07-17T08:31:29.1807906Z 2025-07-17T08:31:29.1808786Z test_namedtensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtensor_1.1_a63f1829532ca2da_.log 2025-07-17T08:31:29.1833949Z Running 88 items in this shard: test/test_namedtensor.py::TestNamedTensor::test_aaa_must_run_first_check_experimental_warning, test/test_namedtensor.py::TestNamedTensor::test_addcmul_addcdiv, test/test_namedtensor.py::TestNamedTensor::test_addmm, test/test_namedtensor.py::TestNamedTensor::test_addmv, test/test_namedtensor.py::TestNamedTensor::test_align_as, test/test_namedtensor.py::TestNamedTensor::test_align_tensors, test/test_namedtensor.py::TestNamedTensor::test_align_tensors_two_inputs, test/test_namedtensor.py::TestNamedTensor::test_align_to, test/test_namedtensor.py::TestNamedTensor::test_align_to_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_any_all, test/test_namedtensor.py::TestNamedTensor::test_as_strided, test/test_namedtensor.py::TestNamedTensor::test_as_strided_cuda, test/test_namedtensor.py::TestNamedTensor::test_autograd_ignores_names, test/test_namedtensor.py::TestNamedTensor::test_autograd_smoke, test/test_namedtensor.py::TestNamedTensor::test_autograd_warns_named_grad, test/test_namedtensor.py::TestNamedTensor::test_bernoulli, test/test_namedtensor.py::TestNamedTensor::test_big_tensor_repr_has_names, test/test_namedtensor.py::TestNamedTensor::test_binary_ops, test/test_namedtensor.py::TestNamedTensor::test_bitwise_not, test/test_namedtensor.py::TestNamedTensor::test_bmm, test/test_namedtensor.py::TestNamedTensor::test_cat, test/test_namedtensor.py::TestNamedTensor::test_cdist, test/test_namedtensor.py::TestNamedTensor::test_comparison_ops, test/test_namedtensor.py::TestNamedTensor::test_copy_transpose, test/test_namedtensor.py::TestNamedTensor::test_cummax_cummin, test/test_namedtensor.py::TestNamedTensor::test_detach, test/test_namedtensor.py::TestNamedTensor::test_diagonal, test/test_namedtensor.py::TestNamedTensor::test_dot, test/test_namedtensor.py::TestNamedTensor::test_equal, test/test_namedtensor.py::TestNamedTensor::test_expand, test/test_namedtensor.py::TestNamedTensor::test_factory_coverage, test/test_namedtensor.py::TestNamedTensor::test_factory_edge_cases, test/test_namedtensor.py::TestNamedTensor::test_flatten, test/test_namedtensor.py::TestNamedTensor::test_flatten_index_error, test/test_namedtensor.py::TestNamedTensor::test_flatten_nodims, test/test_namedtensor.py::TestNamedTensor::test_has_names, test/test_namedtensor.py::TestNamedTensor::test_index_fill, test/test_namedtensor.py::TestNamedTensor::test_info_smoke, test/test_namedtensor.py::TestNamedTensor::test_logcumsumexp, test/test_namedtensor.py::TestNamedTensor::test_logical_not, test/test_namedtensor.py::TestNamedTensor::test_logical_ops, test/test_namedtensor.py::TestNamedTensor::test_masked_fill, test/test_namedtensor.py::TestNamedTensor::test_masked_select, test/test_namedtensor.py::TestNamedTensor::test_matmul, test/test_namedtensor.py::TestNamedTensor::test_max_pooling, test/test_namedtensor.py::TestNamedTensor::test_max_pooling_without_names_does_not_warn, test/test_namedtensor.py::TestNamedTensor::test_mm, test/test_namedtensor.py::TestNamedTensor::test_mv, test/test_namedtensor.py::TestNamedTensor::test_no_jit_script_support, test/test_namedtensor.py::TestNamedTensor::test_no_jit_tracer_support, test/test_namedtensor.py::TestNamedTensor::test_no_multiprocessing_support, test/test_namedtensor.py::TestNamedTensor::test_no_pickle_support, test/test_namedtensor.py::TestNamedTensor::test_no_save_support, test/test_namedtensor.py::TestNamedTensor::test_noncontig_contiguous, test/test_namedtensor.py::TestNamedTensor::test_none_names_refcount, test/test_namedtensor.py::TestNamedTensor::test_nyi_dimname_overload_msg, test/test_namedtensor.py::TestNamedTensor::test_out_fn_semantics, test/test_namedtensor.py::TestNamedTensor::test_pow_special, test/test_namedtensor.py::TestNamedTensor::test_py3_ellipsis, test/test_namedtensor.py::TestNamedTensor::test_reduction_fns, test/test_namedtensor.py::TestNamedTensor::test_refine_names, test/test_namedtensor.py::TestNamedTensor::test_rename, test/test_namedtensor.py::TestNamedTensor::test_rename_, test/test_namedtensor.py::TestNamedTensor::test_rename_globber, test/test_namedtensor.py::TestNamedTensor::test_rename_rename_map, test/test_namedtensor.py::TestNamedTensor::test_repr, test/test_namedtensor.py::TestNamedTensor::test_resize, test/test_namedtensor.py::TestNamedTensor::test_select, test/test_namedtensor.py::TestNamedTensor::test_select_cuda, test/test_namedtensor.py::TestNamedTensor::test_set_names_property, test/test_namedtensor.py::TestNamedTensor::test_size, test/test_namedtensor.py::TestNamedTensor::test_split_fns_propagates_names, test/test_namedtensor.py::TestNamedTensor::test_squeeze, test/test_namedtensor.py::TestNamedTensor::test_stride, test/test_namedtensor.py::TestNamedTensor::test_support_device_named_grad, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_lists, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_named_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_numpy, test/test_namedtensor.py::TestNamedTensor::test_tensor_from_tensor, test/test_namedtensor.py::TestNamedTensor::test_tensor_grad_is_unnamed, test/test_namedtensor.py::TestNamedTensor::test_transpose_variants, test/test_namedtensor.py::TestNamedTensor::test_trivial, test/test_namedtensor.py::TestNamedTensor::test_unary_propagate_names_fns, test/test_namedtensor.py::TestNamedTensor::test_unflatten, test/test_namedtensor.py::TestNamedTensor::test_unsupported_op_error_msg, test/test_namedtensor.py::TestNamedTensor::test_using_seen_interned_string_doesnt_bump_refcount, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_interned_string_bumps_refcount_permanently, test/test_namedtensor.py::TestNamedTensor::test_using_unseen_uninterned_string_refcounts 2025-07-17T08:31:29.1858379Z 2025-07-17T08:31:31.7676255Z 2025-07-17T08:31:31.7677168Z export/test_package 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_package_1.1_8763273594f97d29_.log 2025-07-17T08:31:31.7679085Z Running 4 items in this shard: test/export/test_package.py::TestPackage::test_basic, test/export/test_package.py::TestPackage::test_error, test/export/test_package.py::TestPackage::test_more_than_once, test/export/test_package.py::TestPackage::test_overloads 2025-07-17T08:31:31.7680223Z 2025-07-17T08:31:32.2658534Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:32.2660089Z import pkg_resources 2025-07-17T08:31:32.3915431Z Running dynamo/test_config 1/1 ... [2025-07-17 08:31:32.391269] 2025-07-17T08:31:32.3916123Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:32.3918267Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_config.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:31:32.391606] 2025-07-17T08:31:34.8991249Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:34.8992856Z import pkg_resources 2025-07-17T08:31:35.0244229Z Running inductor/test_codecache 1/1 ... [2025-07-17 08:31:35.024108] 2025-07-17T08:31:35.0244690Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:35.0246912Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_codecache.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'] ... [2025-07-17 08:31:35.024461] 2025-07-17T08:31:40.3677845Z 2025-07-17T08:31:40.3678889Z dynamo/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_config_1.1_6701eaca141d6a2d_.log 2025-07-17T08:31:40.3681307Z Running 5 items in this shard: test/dynamo/test_config.py::ConfigTests::test_automatic_dynamic, test/dynamo/test_config.py::ConfigTests::test_config_compile_ignored, test/dynamo/test_config.py::ConfigTests::test_config_hash, test/dynamo/test_config.py::ConfigTests::test_no_assume_static_by_default, test/dynamo/test_config.py::ConfigTests::test_no_automatic_dynamic 2025-07-17T08:31:40.3682883Z 2025-07-17T08:31:43.6365389Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:43.6366983Z import pkg_resources 2025-07-17T08:31:43.7617391Z Running torch_np/numpy_tests/core/test_scalarinherit 1/1 ... [2025-07-17 08:31:43.761378] 2025-07-17T08:31:43.7617980Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:43.7619589Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_scalarinherit.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'] ... [2025-07-17 08:31:43.761708] 2025-07-17T08:31:47.0308677Z 2025-07-17T08:31:47.0310101Z torch_np/numpy_tests/core/test_scalarinherit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_scalarinherit_1.1_afb8d0a4a5d1ede8_.log 2025-07-17T08:31:47.0312308Z Running 3 items in this shard: test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_gh_15395, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init, test/torch_np/numpy_tests/core/test_scalarinherit.py::TestInherit::test_init2 2025-07-17T08:31:47.0313715Z 2025-07-17T08:31:50.1299880Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:50.1301458Z import pkg_resources 2025-07-17T08:31:50.2562504Z Running export/test_upgrader 1/1 ... [2025-07-17 08:31:50.255903] 2025-07-17T08:31:50.2562991Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:50.2564723Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_upgrader.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'] ... [2025-07-17 08:31:50.256244] 2025-07-17T08:31:53.5262242Z 2025-07-17T08:31:53.5263292Z export/test_upgrader 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_upgrader_1.1_3b700523dd3abb6e_.log 2025-07-17T08:31:53.5266302Z Running 6 items in this shard: test/export/test_upgrader.py::TestUpgrader::test_field_renaming_chain_from_v0_complete, test/export/test_upgrader.py::TestUpgrader::test_field_renaming_chain_from_v0_missing_field, test/export/test_upgrader.py::TestUpgrader::test_field_renaming_from_v1_partial_chain, test/export/test_upgrader.py::TestUpgrader::test_nn_module_stack_error_handling_invalid_type, test/export/test_upgrader.py::TestUpgrader::test_nn_module_stack_transformation_from_v0, test/export/test_upgrader.py::TestUpgrader::test_nodes_without_metadata_handled_gracefully 2025-07-17T08:31:53.5268769Z 2025-07-17T08:31:56.7001038Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:31:56.7002607Z import pkg_resources 2025-07-17T08:31:56.8250211Z Running dynamo/test_compile 1/1 ... [2025-07-17 08:31:56.824688] 2025-07-17T08:31:56.8250687Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:31:56.8252959Z 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'] ... [2025-07-17 08:31:56.825043] 2025-07-17T08:32:06.7726067Z 2025-07-17T08:32:06.7726955Z dynamo/test_compile 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compile_1.1_c118c566b7328b75_.log 2025-07-17T08:32:06.7734140Z Running 17 items in this shard: test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_callback, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_callback_with_graph_break, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_constant_hasattr_fail, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_evnum_hasattr_fail, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_name_error, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_nn_module_invalid_method, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compilation_tensor_invalid_method, test/dynamo/test_compile.py::InPlaceCompilationTests::test_compile_eager_options, test/dynamo/test_compile.py::InPlaceCompilationTests::test_jit_save, test/dynamo/test_compile.py::InPlaceCompilationTests::test_list_bad_access, test/dynamo/test_compile.py::InPlaceCompilationTests::test_overwrite_call_impl, test/dynamo/test_compile.py::InPlaceCompilationTests::test_save, test/dynamo/test_compile.py::InPlaceCompilationTests::test_state_dict_save, test/dynamo/test_compile.py::InPlaceCompilationTests::test_to_sparse_to_dense_with_graph_break, test/dynamo/test_compile.py::InPlaceCompilationTests::test_torch_script_compilation, test/dynamo/test_compile.py::PublicTorchCompilerTests::test_dynamo_signatures 2025-07-17T08:32:06.7740977Z 2025-07-17T08:32:09.9171129Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:32:09.9172837Z import pkg_resources 2025-07-17T08:32:10.0419755Z Running dynamo/test_after_aot 1/1 ... [2025-07-17 08:32:10.041672] 2025-07-17T08:32:10.0420235Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:32:10.0422977Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_after_aot.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'] ... [2025-07-17 08:32:10.042022] 2025-07-17T08:32:19.7213997Z 2025-07-17T08:32:19.7214899Z dynamo/test_after_aot 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_after_aot_1.1_faaae715e70444de_.log 2025-07-17T08:32:19.7216256Z Running 2 items in this shard: test/dynamo/test_after_aot.py::TestAfterAot::test_dump_tensor, test/dynamo/test_after_aot.py::TestAfterAot::test_save_graph_repro 2025-07-17T08:32:19.7216965Z 2025-07-17T08:32:22.9665071Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:32:22.9667892Z import pkg_resources 2025-07-17T08:32:23.0980966Z Running test_hub 1/1 ... [2025-07-17 08:32:23.097729] 2025-07-17T08:32:23.0981634Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:32:23.0983578Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_hub.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:32:23.098080] 2025-07-17T08:32:35.2312649Z 2025-07-17T08:32:35.2313495Z test_hub 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_hub_1.1_12ab4544dc12d14e_.log 2025-07-17T08:32:35.2318865Z Running 20 items in this shard: test/test_hub.py::TestHub::test_download_url_to_file, test/test_hub.py::TestHub::test_get_set_dir, test/test_hub.py::TestHub::test_hub_parse_repo_info, test/test_hub.py::TestHub::test_list_entrypoints, test/test_hub.py::TestHub::test_load_commit_from_forked_repo, test/test_hub.py::TestHub::test_load_from_branch, test/test_hub.py::TestHub::test_load_from_github, test/test_hub.py::TestHub::test_load_from_local_dir, test/test_hub.py::TestHub::test_load_legacy_zip_checkpoint, test/test_hub.py::TestHub::test_load_state_dict_from_url, test/test_hub.py::TestHub::test_load_zip_1_6_checkpoint, test/test_hub.py::TestHub::test_trust_repo_builtin_trusted_owners, test/test_hub.py::TestHub::test_trust_repo_check_no, test/test_hub.py::TestHub::test_trust_repo_check_yes, test/test_hub.py::TestHub::test_trust_repo_false_emptystring, test/test_hub.py::TestHub::test_trust_repo_false_no, test/test_hub.py::TestHub::test_trust_repo_legacy, test/test_hub.py::TestHub::test_trust_repo_none, test/test_hub.py::TestHub::test_trust_repo_true, test/test_hub.py::TestHub::test_trusted_repo_false_yes 2025-07-17T08:32:35.2323674Z 2025-07-17T08:32:38.3472632Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:32:38.3475341Z import pkg_resources 2025-07-17T08:32:38.4725437Z Running dynamo/test_autograd_function 1/1 ... [2025-07-17 08:32:38.472218] 2025-07-17T08:32:38.4725960Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:32:38.4727682Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_autograd_function.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:32:38.472552] 2025-07-17T08:32:55.0128163Z 2025-07-17T08:32:55.0129448Z dynamo/test_autograd_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_autograd_function_1.1_e4d1aebfa4bdc9e3_.log 2025-07-17T08:32:55.0146524Z Running 40 items in this shard: test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_allow_in_graph, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_amp_custom_fwd_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_assert_is_contiguous_after_matmul, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_assert_is_contiguous_on_grad_output_directly, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_autograd_function_equivalence, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_autograd_function_has_graph_break, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_backward_returns_none_for_tensor_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_classmethod, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_data_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_default_values, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_enum_arg, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_forward_returns_constant, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_context_mark_and_save, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_context_save_and_mark, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_function_with_bound_free_variable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_fwd_no_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_fwd_propogation_correctness, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_linear_setup_context, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_mark_multi_output_non_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_mark_non_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_materialize_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_multi_output, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_multiple_different_non_tensor_inputs, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_needs_input_grad, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_once_differentiable, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_print_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_repeated_save_for_backward_calls, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_requires_grad_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_save_for_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_set_materialize_grads_no_graph_break, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smoke_from_test_autograd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smuggle_symint_issue_111031, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_smuggle_tensor_and_complex_structures, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_stride_in_bwd, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tensor_list_as_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tensor_subclass_intermediary_input, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_triton_kernel_basic, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_triton_kernel_multiple_out, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_tuple_arg, test/dynamo/test_autograd_function.py::AutogradFunctionTests::test_user_defined_object_as_input 2025-07-17T08:32:55.0162835Z 2025-07-17T08:32:58.1627316Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:32:58.1629048Z import pkg_resources 2025-07-17T08:32:58.2885273Z Running export/test_db 1/1 ... [2025-07-17 08:32:58.288184] 2025-07-17T08:32:58.2885902Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:32:58.2887429Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_db.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'] ... [2025-07-17 08:32:58.288529] 2025-07-17T08:33:12.1390977Z 2025-07-17T08:33:12.1392282Z inductor/test_codecache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_codecache_1.1_d670c339e85c0b0e_.log 2025-07-17T08:33:12.1628351Z Running 209 items in this shard: test/inductor/test_codecache.py::TestPyCodeCache::test_linemaps_empty, test/inductor/test_codecache.py::TestFxGraphCache::test_async_compile_cache, test/inductor/test_codecache.py::TestFxGraphCache::test_auto_functionalized_caching_variant_v1, test/inductor/test_codecache.py::TestFxGraphCache::test_auto_functionalized_caching_variant_v2, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_clear, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_guard, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_guard_overspec, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cpu_bfloat16_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cpu_bfloat16_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cpu_float32_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cpu_float32_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cuda_bfloat16_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cuda_bfloat16_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cuda_float32_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_device_cuda_float32_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_empty, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_generic, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_pgo, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_pgo_swap_file_names, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_hot_load_repeat, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True_grad_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True_grad_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cpu_float32_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cpu_float32_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cpu_float64_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cpu_float64_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cuda_float32_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cuda_float32_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cuda_float64_dynamic_False, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_model_device_cuda_float64_dynamic_True, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_int32_bounds_device_cuda_bfloat16, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_int32_bounds_device_cuda_float16, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_static_bounds_device_cpu_bfloat16, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_static_bounds_device_cpu_float32, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_static_bounds_device_cuda_bfloat16, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_load_with_guards_static_bounds_device_cuda_float32, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_with_nt, test/inductor/test_codecache.py::TestFxGraphCache::test_cache_with_symint_non_arg_guard, test/inductor/test_codecache.py::TestFxGraphCache::test_constant_handling_device_cpu, test/inductor/test_codecache.py::TestFxGraphCache::test_constant_handling_device_cuda, test/inductor/test_codecache.py::TestFxGraphCache::test_flex_attention_caching, test/inductor/test_codecache.py::TestFxGraphCache::test_freezing_device_cpu_inlinable_False, test/inductor/test_codecache.py::TestFxGraphCache::test_freezing_device_cpu_inlinable_True, test/inductor/test_codecache.py::TestFxGraphCache::test_freezing_device_cuda_inlinable_False, test/inductor/test_codecache.py::TestFxGraphCache::test_freezing_device_cuda_inlinable_True, test/inductor/test_codecache.py::TestFxGraphCache::test_generated_kernel_count, test/inductor/test_codecache.py::TestFxGraphCache::test_higher_order_op_bypass_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_higher_order_op_bypass_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_inductor_counters, test/inductor/test_codecache.py::TestFxGraphCache::test_no_arguments_tensor_device_guards, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cpu_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_bfloat16_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_False_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_False_bundle_triton_True_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_False_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_remote_cache_load_function_device_cuda_float32_dynamic_True_bundle_triton_True_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_tensor_device_guards_cpu_tensor, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_higher_order_op_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_higher_order_op_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_higher_order_op_different_configs_bundle_triton_False, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_higher_order_op_different_configs_bundle_triton_True, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_op_bundle_triton_False_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_op_bundle_triton_False_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_op_bundle_triton_True_use_static_cuda_launcher_False, test/inductor/test_codecache.py::TestFxGraphCache::test_triton_op_bundle_triton_True_use_static_cuda_launcher_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_backend_dynamic_shapes_from_example_inputs, test/inductor/test_codecache.py::TestStandaloneCompile::test_backend_dynamic_shapes_from_graph, test/inductor/test_codecache.py::TestStandaloneCompile::test_backend_dynamic_shapes_from_tracing_context, test/inductor/test_codecache.py::TestStandaloneCompile::test_backend_static_shapes_dynamic_shapes_from_example_inputs, test/inductor/test_codecache.py::TestStandaloneCompile::test_backend_static_shapes_dynamic_shapes_from_graph, test/inductor/test_codecache.py::TestStandaloneCompile::test_backend_static_shapes_dynamic_shapes_from_tracing_context, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cpu_format_binary_dynamic_False_graph_partition_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cpu_format_binary_dynamic_False_graph_partition_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cpu_format_binary_dynamic_True_graph_partition_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cpu_format_binary_dynamic_True_graph_partition_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cpu_format_unpacked_dynamic_False_graph_partition_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cpu_format_unpacked_dynamic_False_graph_partition_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cpu_format_unpacked_dynamic_True_graph_partition_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cpu_format_unpacked_dynamic_True_graph_partition_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cuda_format_binary_dynamic_False_graph_partition_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cuda_format_binary_dynamic_False_graph_partition_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cuda_format_binary_dynamic_True_graph_partition_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cuda_format_binary_dynamic_True_graph_partition_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cuda_format_unpacked_dynamic_False_graph_partition_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cuda_format_unpacked_dynamic_False_graph_partition_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cuda_format_unpacked_dynamic_True_graph_partition_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_basic_device_cuda_format_unpacked_dynamic_True_graph_partition_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_call_in_backend_dynamic_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_call_in_backend_dynamic_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_custom_pass_handling, test/inductor/test_codecache.py::TestStandaloneCompile::test_different_process, test/inductor/test_codecache.py::TestStandaloneCompile::test_dynamic_shapes_from_example_inputs_config_patches_False, test/inductor/test_codecache.py::TestStandaloneCompile::test_dynamic_shapes_from_example_inputs_config_patches_True, test/inductor/test_codecache.py::TestStandaloneCompile::test_dynamic_shapes_from_graph, test/inductor/test_codecache.py::TestStandaloneCompile::test_modify_unpacked_file_device_cpu, test/inductor/test_codecache.py::TestStandaloneCompile::test_modify_unpacked_file_device_cuda, test/inductor/test_codecache.py::TestStandaloneCompile::test_save_in_new_path, test/inductor/test_codecache.py::TestStandaloneCompile::test_split_module, test/inductor/test_codecache.py::TestStandaloneCompile::test_static_shapes_dynamic_shapes_from_example_inputs, test/inductor/test_codecache.py::TestStandaloneCompile::test_static_shapes_dynamic_shapes_from_graph, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_bypass_unsupported, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_get_hash_for_files, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_config_changes, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_custom_backend_pass, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_custom_passes, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_fake_tensors, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_kwargs, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_hash_private_config_changes, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_non_serializable_custom_passes_causes_cache_miss, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_parameter_constants, test/inductor/test_codecache.py::TestFxGraphCacheHashing::test_stable_strings, test/inductor/test_codecache.py::TestCudaCompileCommand::test_cuda_compile_command, test/inductor/test_codecache.py::TestAutotuneCache::test_autotune_cache, test/inductor/test_codecache.py::TestAutotuneCache::test_autotune_cache_warm_start, test/inductor/test_codecache.py::TestAutotuneCache::test_bundled_autotune_remote_cache, test/inductor/test_codecache.py::TestAutotuneCache::test_modified_autotune_cache_remote_cache_False, test/inductor/test_codecache.py::TestAutotuneCache::test_modified_autotune_cache_remote_cache_True, test/inductor/test_codecache.py::TestRemoteAOTAutogradCache::test_autograd_remote_cache, test/inductor/test_codecache.py::TestRemoteAOTAutogradCache::test_autograd_remote_lazy_backward, test/inductor/test_codecache.py::TestUtils::test_force_disable_coordinate_descent, test/inductor/test_codecache.py::TestUtils::test_fresh_cache 2025-07-17T08:33:12.1821199Z 2025-07-17T08:33:15.2706618Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:33:15.2708559Z import pkg_resources 2025-07-17T08:33:15.3948948Z Running test_optim 1/1 ... [2025-07-17 08:33:15.394545] 2025-07-17T08:33:15.3949360Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:33:15.3951585Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', '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'] ... [2025-07-17 08:33:15.394916] 2025-07-17T08:33:24.6937211Z 2025-07-17T08:33:24.6938528Z export/test_db 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_db_1.1_f5905a72456788fd_.log 2025-07-17T08:33:24.6966832Z Running 36 items in this shard: test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_dynamic_shape_round, test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_model_attr_mutation, test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_optional_input, test/export/test_db.py::ExampleTests::test_exportdb_not_supported_case_unsupported_operator, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_assume_constant_result, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_autograd_function, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_class_method, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_branch_class_method, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_branch_nested_function, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_branch_nonlocal_variables, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_closed_over_variable, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_operands, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_cond_predicate, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_constrain_as_size_example, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_constrain_as_value_example, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_decorator, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dictionary, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_assert, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_constructor, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_if_guard, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_map, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_slicing, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_dynamic_shape_view, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_fn_with_kwargs, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_list_contains, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_list_unpack, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_nested_function, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_null_context_manager, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_pytree_flatten, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_scalar_output, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_specialized_attribute, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_static_for_loop, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_static_if, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_tensor_setattr, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_type_reflection_method, test/export/test_db.py::ExampleTests::test_exportdb_supported_case_user_input_mutation 2025-07-17T08:33:24.6994520Z 2025-07-17T08:33:27.8488332Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:33:27.8489893Z import pkg_resources 2025-07-17T08:33:27.9709632Z Running inductor/test_cooperative_reductions 1/1 ... [2025-07-17 08:33:27.970660] 2025-07-17T08:33:27.9710178Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:33:27.9711995Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cooperative_reductions.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:33:27.970965] 2025-07-17T08:33:34.5179042Z 2025-07-17T08:33:34.5180126Z inductor/test_cooperative_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cooperative_reductions_1.1_37e410db41b17607_.log 2025-07-17T08:33:34.5180975Z 2025-07-17T08:33:37.6020598Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:33:37.6023633Z import pkg_resources 2025-07-17T08:33:37.7260653Z Running inductor/test_cpu_select_algorithm 1/16 ... [2025-07-17 08:33:37.725685] 2025-07-17T08:33:37.7261618Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:33:37.7263931Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=1', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:33:37.726012] 2025-07-17T08:34:17.4480831Z 2025-07-17T08:34:17.4482388Z test_optim 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_optim_1.1_919862df33aae69c_.log 2025-07-17T08:34:17.5183315Z Running 966 items in this shard: test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_T_cur_reset, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr1_T_mult_1, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr1_T_mult_2, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr1_T_mult_4, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr2, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr3, test/test_optim.py::TestLRScheduler::test_CosineAnnealingWarmRestarts_lr_state_dict, test/test_optim.py::TestLRScheduler::test_add_param_group_does_not_break_reduce_lr_on_plateau_min_lr_list, test/test_optim.py::TestLRScheduler::test_add_param_group_does_not_break_reduce_lr_on_plateau_min_lr_scalar, test/test_optim.py::TestLRScheduler::test_add_param_group_errors_reduce_lr_on_plateau, test/test_optim.py::TestLRScheduler::test_chained_lr1, test/test_optim.py::TestLRScheduler::test_chained_lr2, test/test_optim.py::TestLRScheduler::test_chained_lr2_get_last_lr_before_step, test/test_optim.py::TestLRScheduler::test_chained_lr3, test/test_optim.py::TestLRScheduler::test_chained_lr4, test/test_optim.py::TestLRScheduler::test_chained_lr5, test/test_optim.py::TestLRScheduler::test_closed_form_constantlr, test/test_optim.py::TestLRScheduler::test_closed_form_cos_anneal_lr, test/test_optim.py::TestLRScheduler::test_closed_form_exp_lr, test/test_optim.py::TestLRScheduler::test_closed_form_linearlr, test/test_optim.py::TestLRScheduler::test_closed_form_multi_step_lr, test/test_optim.py::TestLRScheduler::test_closed_form_poly_lr, test/test_optim.py::TestLRScheduler::test_closed_form_step_lr, test/test_optim.py::TestLRScheduler::test_compound_cosanneal_and_exp_lr, test/test_optim.py::TestLRScheduler::test_compound_cosanneal_and_linearlr, test/test_optim.py::TestLRScheduler::test_compound_cosanneal_and_multistep_lr, test/test_optim.py::TestLRScheduler::test_compound_cosanneal_and_step_lr, test/test_optim.py::TestLRScheduler::test_compound_exp_and_linearlr, test/test_optim.py::TestLRScheduler::test_compound_exp_and_multistep_lr, test/test_optim.py::TestLRScheduler::test_compound_linearlr_and_multistep_lr, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau1, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau2, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau3, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau4, test/test_optim.py::TestLRScheduler::test_compound_reduce_lr_on_plateau5, test/test_optim.py::TestLRScheduler::test_compound_step_and_constantlr, test/test_optim.py::TestLRScheduler::test_compound_step_and_exp_lr, test/test_optim.py::TestLRScheduler::test_compound_step_and_multistep_lr, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass0, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass1, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass2, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass3, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass4, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass5, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass6, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass7, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass8, test/test_optim.py::TestLRScheduler::test_constant_initial_lr_LRClass9, test/test_optim.py::TestLRScheduler::test_constant_initial_params_cyclelr, test/test_optim.py::TestLRScheduler::test_constant_initial_params_onecyclelr, test/test_optim.py::TestLRScheduler::test_constant_initial_params_swalr, test/test_optim.py::TestLRScheduler::test_constantlr, test/test_optim.py::TestLRScheduler::test_constantlr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_constantlr_with_epoch, test/test_optim.py::TestLRScheduler::test_cos_anneal_lr, test/test_optim.py::TestLRScheduler::test_cos_anneal_lr_continue, test/test_optim.py::TestLRScheduler::test_cosine_lr_state_dict, test/test_optim.py::TestLRScheduler::test_cosine_then_cyclic, test/test_optim.py::TestLRScheduler::test_cycle_lr_cycle_momentum_fail_with_momentumless_optimizer, test/test_optim.py::TestLRScheduler::test_cycle_lr_cycle_momentum_with_beta1_optimizer, test/test_optim.py::TestLRScheduler::test_cycle_lr_exp_range_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_exp_range_mode_one_lr, test/test_optim.py::TestLRScheduler::test_cycle_lr_exp_range_mode_step_size_up_down, test/test_optim.py::TestLRScheduler::test_cycle_lr_invalid_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_removed_after_out_of_scope, test/test_optim.py::TestLRScheduler::test_cycle_lr_scale_fn_restored_from_state_dict, test/test_optim.py::TestLRScheduler::test_cycle_lr_state_dict_picklable, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular2_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular2_mode_one_lr, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular2_mode_step_size_up_down, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular_mode, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular_mode_one_lr, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular_mode_one_lr_no_momentum, test/test_optim.py::TestLRScheduler::test_cycle_lr_triangular_mode_step_size_up_down, test/test_optim.py::TestLRScheduler::test_cycle_lr_with_adam, test/test_optim.py::TestLRScheduler::test_cycle_lr_with_momentumless_optimizer, test/test_optim.py::TestLRScheduler::test_error_when_getlr_has_epoch, test/test_optim.py::TestLRScheduler::test_exp_lr, test/test_optim.py::TestLRScheduler::test_exp_step_lr_state_dict, test/test_optim.py::TestLRScheduler::test_exponential_lr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_get_last_lr_constantlr, test/test_optim.py::TestLRScheduler::test_get_last_lr_linearlr, test/test_optim.py::TestLRScheduler::test_get_last_lr_multi_step_lr, test/test_optim.py::TestLRScheduler::test_get_last_lr_sequentiallr, test/test_optim.py::TestLRScheduler::test_get_last_lr_step_lr, test/test_optim.py::TestLRScheduler::test_lambda_lr, test/test_optim.py::TestLRScheduler::test_lambda_lr_state_dict_fn, test/test_optim.py::TestLRScheduler::test_lambda_lr_state_dict_obj, test/test_optim.py::TestLRScheduler::test_linear_linearlr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_linearlr, test/test_optim.py::TestLRScheduler::test_linearlr_start_factor_limits1, test/test_optim.py::TestLRScheduler::test_linearlr_start_factor_limits2, test/test_optim.py::TestLRScheduler::test_linearlr_with_epoch, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass0, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass1, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass10, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass11, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass12, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass2, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass3, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass4, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass5, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass6, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass7, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass8, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_LRClass9, test/test_optim.py::TestLRScheduler::test_lr_scheduler_checkpoint_on_plateau, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass0_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass0_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass10_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass10_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass11_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass11_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass12_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass12_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass13_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass13_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass14_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass14_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass1_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass1_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass2_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass2_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass3_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass3_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass4_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass4_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass5_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass5_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass6_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass6_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass7_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass7_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass8_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass8_weights_only_True, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass9_weights_only_False, test/test_optim.py::TestLRScheduler::test_lr_scheduler_state_dict_load_LRClass9_weights_only_True, test/test_optim.py::TestLRScheduler::test_multi_step_lr, test/test_optim.py::TestLRScheduler::test_multi_step_lr_state_dict, test/test_optim.py::TestLRScheduler::test_multi_step_lr_with_epoch, test/test_optim.py::TestLRScheduler::test_multiplicative_lr, test/test_optim.py::TestLRScheduler::test_multiplicative_lr_with_lr_lambda, test/test_optim.py::TestLRScheduler::test_new_pattern_no_warning, test/test_optim.py::TestLRScheduler::test_new_pattern_no_warning_with_arg, test/test_optim.py::TestLRScheduler::test_new_pattern_no_warning_with_overridden_optim_step, test/test_optim.py::TestLRScheduler::test_no_cyclic_references, test/test_optim.py::TestLRScheduler::test_no_cyclic_references_in_step, test/test_optim.py::TestLRScheduler::test_old_pattern_warning, test/test_optim.py::TestLRScheduler::test_old_pattern_warning_resuming, test/test_optim.py::TestLRScheduler::test_old_pattern_warning_resuming_with_arg, test/test_optim.py::TestLRScheduler::test_old_pattern_warning_with_arg, test/test_optim.py::TestLRScheduler::test_old_pattern_warning_with_overridden_optim_step, test/test_optim.py::TestLRScheduler::test_onecycle_lr_cannot_calculate_total_steps, test/test_optim.py::TestLRScheduler::test_onecycle_lr_cosine_annealing, test/test_optim.py::TestLRScheduler::test_onecycle_lr_invalid_anneal_strategy, test/test_optim.py::TestLRScheduler::test_onecycle_lr_invalid_pct_start, test/test_optim.py::TestLRScheduler::test_onecycle_lr_legacy_state_dict, test/test_optim.py::TestLRScheduler::test_onecycle_lr_linear_annealing, test/test_optim.py::TestLRScheduler::test_onecycle_lr_linear_annealing_three_phases, test/test_optim.py::TestLRScheduler::test_poly_lr, test/test_optim.py::TestLRScheduler::test_polynomial_lr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau1, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau2, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau3, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau4, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau5, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau6, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau7, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau8, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau_get_last_lr_before_step, test/test_optim.py::TestLRScheduler::test_reduce_lr_on_plateau_state_dict, test/test_optim.py::TestLRScheduler::test_sequentiallr1, test/test_optim.py::TestLRScheduler::test_sequentiallr2, test/test_optim.py::TestLRScheduler::test_sequentiallr3, test/test_optim.py::TestLRScheduler::test_sequentiallr4, test/test_optim.py::TestLRScheduler::test_sequentiallr5, test/test_optim.py::TestLRScheduler::test_step_lr, test/test_optim.py::TestLRScheduler::test_step_lr_is_constant_for_constant_epoch, test/test_optim.py::TestLRScheduler::test_step_lr_state_dict, test/test_optim.py::TestLRScheduler::test_swa_lr_state_dict, test/test_optim.py::TestLRScheduler::test_swalr_cosine_anneal_after_multiplicative, test/test_optim.py::TestLRScheduler::test_swalr_hypers, test/test_optim.py::TestLRScheduler::test_swalr_linear_anneal_after_multiplicative, test/test_optim.py::TestLRScheduler::test_swalr_no_anneal, test/test_optim.py::TestDifferentiableOptimizer::test_adadelta, test/test_optim.py::TestDifferentiableOptimizer::test_adagrad, test/test_optim.py::TestDifferentiableOptimizer::test_adam, test/test_optim.py::TestDifferentiableOptimizer::test_adam_differentiable_all_hyperparams, test/test_optim.py::TestDifferentiableOptimizer::test_adam_differentiable_betas, test/test_optim.py::TestDifferentiableOptimizer::test_adam_differentiable_lr, test/test_optim.py::TestDifferentiableOptimizer::test_adam_differentiable_weight_decay, test/test_optim.py::TestDifferentiableOptimizer::test_adamax, test/test_optim.py::TestDifferentiableOptimizer::test_adamw, test/test_optim.py::TestDifferentiableOptimizer::test_adamw_differentiable_all_hyperparams, test/test_optim.py::TestDifferentiableOptimizer::test_adamw_differentiable_betas, test/test_optim.py::TestDifferentiableOptimizer::test_adamw_differentiable_lr, test/test_optim.py::TestDifferentiableOptimizer::test_adamw_differentiable_weight_decay, test/test_optim.py::TestDifferentiableOptimizer::test_asgd, test/test_optim.py::TestDifferentiableOptimizer::test_differentiable_lr, test/test_optim.py::TestDifferentiableOptimizer::test_differentiable_weight_decay, test/test_optim.py::TestDifferentiableOptimizer::test_differentiable_weight_decay_and_lr, test/test_optim.py::TestDifferentiableOptimizer::test_nadam, test/test_optim.py::TestDifferentiableOptimizer::test_radam, test/test_optim.py::TestDifferentiableOptimizer::test_rmsprop, test/test_optim.py::TestDifferentiableOptimizer::test_rprop, test/test_optim.py::TestDifferentiableOptimizer::test_sgd, test/test_optim.py::TestSWAUtils::test_averaged_model_all_devices_ema_False, test/test_optim.py::TestSWAUtils::test_averaged_model_all_devices_ema_True, test/test_optim.py::TestSWAUtils::test_averaged_model_default_avg_fn_picklable, test/test_optim.py::TestSWAUtils::test_averaged_model_exponential_use_multi_avg_fn_False_use_buffers_False, test/test_optim.py::TestSWAUtils::test_averaged_model_exponential_use_multi_avg_fn_False_use_buffers_True, test/test_optim.py::TestSWAUtils::test_averaged_model_exponential_use_multi_avg_fn_True_use_buffers_False, test/test_optim.py::TestSWAUtils::test_averaged_model_exponential_use_multi_avg_fn_True_use_buffers_True, test/test_optim.py::TestSWAUtils::test_averaged_model_mixed_device_ema_False, test/test_optim.py::TestSWAUtils::test_averaged_model_mixed_device_ema_True, test/test_optim.py::TestSWAUtils::test_averaged_model_state_dict, test/test_optim.py::TestSWAUtils::test_bn_update_eval_momentum, test/test_optim.py::TestSWAUtils::test_update_bn_cnn, test/test_optim.py::TestSWAUtils::test_update_bn_dnn, test/test_optim.py::TestOptimRenewedCPU::test_adamw_serialization_cpu, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_False_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_False_is_named_optim1_True_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_False_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_from_to_named_state_dict_is_named_optim0_True_is_named_optim1_True_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_can_load_older_state_dict_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_ASGD_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adadelta_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adagrad_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_AdamW_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Adamax_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_LBFGS_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_NAdam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_RAdam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_RMSprop_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_Rprop_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_2d_SGD_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_ASGD_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_Adadelta_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_Adagrad_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_AdamW_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_Adam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_Adamax_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_LBFGS_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_NAdam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_RAdam_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_RMSprop_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_Rprop_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_complex_SGD_cpu_complex64, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_capturable_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_capturable_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_capturable_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_capturable_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_fused_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_fused_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_fused_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_cpu_load_state_dict_impl_fused_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_deepcopy_copies_all_public_attrs_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_defaults_changed_to_foreach_Adamax_cpu_float32, 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test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_group_with_lrscheduler_goes_right_direction_RMSprop_cpu_float32, 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test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_param_groups_weight_decay_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_peak_memory_foreach_ASGD_cpu_float32, 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test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_False_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_save_load_equality_with_weights_only_is_named_optim_True_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_second_order_optims_return_consistent_types_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_ASGD_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adadelta_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adafactor_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adagrad_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_AdamW_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adam_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Adamax_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_NAdam_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_RAdam_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_RMSprop_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_Rprop_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_set_default_dtype_works_with_foreach_SGD_cpu_float64, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_False_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_False_is_named_optim1_True_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_False_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_deterministic_is_named_optim0_True_is_named_optim1_True_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_post_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_pre_post_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_state_dict_with_cuda_params_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_all_hooks_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_for_zero_grads_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_is_noop_when_params_have_no_grad_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_post_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_step_pre_hook_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_0_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_1_SparseAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_ASGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_Adadelta_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_Adafactor_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_Adagrad_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_AdamW_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_Adam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_Adamax_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_LBFGS_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_NAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_RAdam_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_RMSprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_Rprop_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_SGD_cpu_float32, test/test_optim.py::TestOptimRenewedCPU::test_tensor_lr_num_dim_2_SparseAdam_cpu_float32 2025-07-17T08:34:17.5604356Z 2025-07-17T08:34:20.6298780Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:34:20.6300463Z import pkg_resources 2025-07-17T08:34:20.7533725Z Running inductor/test_cpu_select_algorithm 5/16 ... [2025-07-17 08:34:20.753034] 2025-07-17T08:34:20.7534389Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:34:20.7536094Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=5', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:34:20.753369] 2025-07-17T08:38:44.6444256Z 2025-07-17T08:38:44.6447701Z inductor/test_torchinductor_opinfo 22/26 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_opinfo_22.26_7c1330f6228b9017_.log 2025-07-17T08:38:44.6518340Z Running 139 items in this shard: test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_H_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_H_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___getitem___cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___getitem___cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive___rmul___cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__chunk_cat_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__chunk_cat_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive__softmax_backward_data_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_addbmm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_aminmax_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_arange_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_arange_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argsort_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_argwhere_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_as_strided_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asin_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asin_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_asinh_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atanh_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_1d_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_atleast_2d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_baddbmm_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bmm_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_bool_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cdouble_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_chalf_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_chalf_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_char_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_column_stack_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_constant_pad_nd_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_cummin_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_deg2rad_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diag_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_diagonal_copy_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_digamma_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_no_rounding_mode_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_div_trunc_rounding_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_permuted_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_empty_strided_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_eq_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erf_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfc_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_erfinv_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_exp_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_expand_as_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_hfftn_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ifft_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_ihfft2_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fft_rfft_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_floor_divide_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_fmax_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_gather_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ge_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_geometric_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_grid_sampler_2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_half_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hsplit_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hsplit_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_hstack_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_add_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_reduce_amax_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_index_select_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_inner_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isclose_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isinf_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_isnan_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_item_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_jiterator_4inputs_with_extra_args_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_ldexp_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_cross_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_eig_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_eigh_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_matrix_power_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_linalg_norm_subgradients_at_zero_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_log2_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_and_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logical_or_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logspace_tensor_overload_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_logsumexp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_argmax_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_cumsum_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_masked_normalize_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_matrix_exp_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_max_reduction_with_dim_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_maximum_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_min_reduction_with_dim_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_mvlgamma_mvlgamma_p_5_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_zeros_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_new_zeros_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_adaptive_avg_pool1d_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_avg_pool2d_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_channel_shuffle_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_channel_shuffle_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_dropout2d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_grid_sample_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_l1_loss_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_unpool1d_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_max_unpool2d_grad_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_pairwise_distance_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_tanhshrink_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_nn_functional_triplet_margin_loss_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_norm_fro_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_outer_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_polygamma_polygamma_n_3_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_randint_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_repeat_interleave_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize__cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_resize_as__cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_rot90_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_scatter_add_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sgn_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sign_cpu_float16, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sign_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signal_windows_hann_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_signbit_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sort_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sparse_sampled_addmm_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_u_cpu_bool, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_u_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_chebyshev_polynomial_w_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_log_ndtr_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_ndtr_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_special_shifted_chebyshev_polynomial_u_cpu_uint8, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sqrt_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_std_mean_unbiased_cpu_float32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_sum_to_size_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_t_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tan_cpu_int32, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tanh_cpu_float64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_tensor_split_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_trunc_cpu_int64, test/inductor/test_torchinductor_opinfo.py::TestInductorOpInfoCPU::test_comprehensive_unfold_copy_cpu_int64 2025-07-17T08:38:44.6587770Z 2025-07-17T08:38:48.0575397Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:38:48.0578331Z import pkg_resources 2025-07-17T08:38:48.2427039Z Running inductor/test_cpu_select_algorithm 6/16 ... [2025-07-17 08:38:48.242307] 2025-07-17T08:38:48.2428000Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:38:48.2431319Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=6', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:38:48.242766] 2025-07-17T08:41:29.9806854Z 2025-07-17T08:41:29.9808084Z inductor/test_cpu_select_algorithm 1/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_1.16_e355f40c1b25b3a8_.log 2025-07-17T08:41:29.9880578Z Running 90 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_aoti_bmm_unique_identifiers_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order2_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order1_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_16_Kdim_32_Ndim_48_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_freezing_bs_1_Mdim_192_Kdim_196_Ndim_64_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_64_epilogue_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_65_epilogue_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_65_epilogue_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_True_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_reshape_a_False_expand_a_scale_True_dynamic_True_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_reshape_a_True_expand_a_scale_False_dynamic_True_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_per_channel_quant_False_reshape_a_False_expand_a_scale_True_dynamic_True_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_reshape_a_False_expand_a_scale_True_dynamic_False_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_reshape_a_False_expand_a_scale_True_dynamic_True_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_reshape_a_True_expand_a_scale_False_dynamic_False_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_reshape_a_True_expand_a_scale_True_dynamic_False_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_False_reshape_a_False_expand_a_scale_True_dynamic_True_M_1_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_grouped_linear_epilogue_batch_size_16_in_features_52_out_features_32_input_3d_True_bias1_epilogue1_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int4_woq_mm_amx_batch_size_1_in_features_128_out_features_1024_group_size_64_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_1_mid_dim_8_in_features_144_out_features_64_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_32_mid_dim_1_in_features_1024_out_features_1024_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_32_mid_dim_8_in_features_128_out_features_64_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_amx_batch_size_1024_in_features_1024_out_features_1024_bias_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_k_slicing_batch_size_2_in_features_1000_out_features_2_bias_True_epilogue_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1000_out_features_1024_bias_False_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1_out_features_1_bias_True_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1000_out_features_1024_bias_False_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1000_out_features_1_bias_False_input_3d_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1_out_features_1024_bias_True_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1_out_features_1_bias_True_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_thread_factors_batch_size_1024_in_features_1024_out_features_1024_bias_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_sigmoid_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_tanh_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_hardtanh_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_hardtanh_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_silu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_384_bias_True_unary_relu_binary_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_385_bias_True_unary_relu_binary_mul_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_16_in_features_128_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_3_in_features_68_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_False_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_epilogue_gelu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_int8_mixed_bf16_False_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_bmm_dynamic_bm_stride_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_bmm_epilogue_dynamic_reshape_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_bmm_with_pointwise_dynamic_shapes_bs_5_Mdim_384_Kdim_96_Ndim_65_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias0_epilogue3_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias3_epilogue2_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1000_out_features_1_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1000_out_features_1024_bias_False_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1_out_features_1024_bias_False_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1_out_features_1_bias_False_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1000_out_features_1024_bias_False_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1_out_features_1024_bias_False_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1_out_features_1_bias_False_input_3d_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1_out_features_1_bias_False_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1_out_features_1_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_k_slicing_dynamic_shapes_batch_size_2_in_features_1000_out_features_2_bias_False_epilogue_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_hardswish_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_hardtanh_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_tanh_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_hardsigmoid_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_hardtanh_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_sigmoid_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_mul_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_unary_relu_binary_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_unary_relu_binary_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_unary_relu_binary_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_unary_relu_binary_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_unary_relu_binary_div_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_unary_relu_binary_div_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_16_in_features_128_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_16_in_features_4_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_3_in_features_68_out_features_65_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_False_int8_mixed_bf16_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_False_int8_mixed_bf16_False_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_epilogue_gelu_cpu_bfloat16 2025-07-17T08:41:29.9955555Z 2025-07-17T08:41:33.0479266Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:41:33.0480948Z import pkg_resources 2025-07-17T08:41:33.1751199Z Running inductor/test_cpu_select_algorithm 16/16 ... [2025-07-17 08:41:33.174794] 2025-07-17T08:41:33.1751743Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:41:33.1753554Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_cpu_select_algorithm.py', '-m', 'not serial', '--shard-id=16', '--num-shards=16', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:41:33.175121] 2025-07-17T08:43:24.9240665Z 2025-07-17T08:43:24.9243845Z inductor/test_cpu_select_algorithm 5/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_5.16_1f2aa32e28c18fe0_.log 2025-07-17T08:43:24.9322694Z Running 97 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order0_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_64_order1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_1024_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_3_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_48_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_bs_1_Mdim_192_Kdim_196_Ndim_385_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_fused_epilogues_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_False_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_True_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_True_dynamic_True_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_True_M_1_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_reshape_a_False_expand_a_scale_True_dynamic_True_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_reshape_a_True_expand_a_scale_False_dynamic_False_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_reshape_a_True_expand_a_scale_True_dynamic_False_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_False_reshape_a_True_expand_a_scale_True_dynamic_False_M_1_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_True_reshape_a_True_expand_a_scale_False_dynamic_False_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int4_woq_mm_amx_batch_size_1_in_features_128_out_features_128_group_size_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int4_woq_mm_amx_batch_size_6_in_features_128_out_features_128_group_size_64_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_17_mid_dim_1_in_features_128_out_features_65_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_17_mid_dim_8_in_features_144_out_features_65_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_32_mid_dim_8_in_features_1024_out_features_1024_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_k_slicing_batch_size_2_in_features_1000_out_features_2_bias_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_k_slicing_batch_size_2_in_features_1000_out_features_2_bias_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1000_out_features_1_bias_False_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1000_out_features_1_bias_True_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1_out_features_1024_bias_True_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1_out_features_1024_bias_True_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1_out_features_1_bias_True_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_thread_factors_batch_size_1024_in_features_1024_out_features_1024_bias_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_permute_batch_size_1_in_features_16_image_size_18_out_features_32_bias_False_has_non_epilogue_users_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_hardsigmoid_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_tanh_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_hardswish_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_leaky_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_sub_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_tanh_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_sub_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_tanh_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_mul_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_sub_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_384_bias_True_unary_relu_binary_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_385_bias_True_unary_relu_binary_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_385_bias_True_unary_relu_binary_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_16_in_features_68_out_features_65_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_32_in_features_4_out_features_65_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_False_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_True_epilogue_gelu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_epilogue_gelu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_epilogue_gelu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_True_int8_mixed_bf16_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_bmm_with_pointwise_dynamic_shapes_bs_5_Mdim_384_Kdim_96_Ndim_64_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias0_epilogue0_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias1_epilogue2_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias2_epilogue2_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1000_out_features_1_bias_False_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1000_out_features_1_bias_True_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_out_features_1024_bias_True_input_3d_False_cpu_float32, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_2_in_features_1_out_features_1_bias_True_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_k_slicing_dynamic_shapes_batch_size_2_in_features_1000_out_features_2_bias_False_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_hardtanh_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_silu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_sub_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_gelu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_div_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_hardsigmoid_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_leaky_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_sub_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_3_in_features_4_out_features_65_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_49_in_features_4_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_49_in_features_4_out_features_65_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_False_int8_mixed_bf16_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_False_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_int8_mixed_bf16_False_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_int8_mixed_bf16_False_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_epilogue_none_cpu_bfloat16 2025-07-17T08:43:24.9400219Z 2025-07-17T08:43:28.3270517Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:43:28.3272245Z import pkg_resources 2025-07-17T08:43:28.4512310Z Running inductor/test_halide 1/1 ... [2025-07-17 08:43:28.450909] 2025-07-17T08:43:28.4512799Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:43:28.4514606Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_halide.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'] ... [2025-07-17 08:43:28.451242] 2025-07-17T08:43:35.7146189Z 2025-07-17T08:43:35.7147423Z inductor/test_halide 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_halide_1.1_9be3fc4cd2806227_.log 2025-07-17T08:43:35.7148114Z 2025-07-17T08:43:39.0826784Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:43:39.0830039Z import pkg_resources 2025-07-17T08:43:39.2063963Z Running inductor/test_inplacing_pass 1/1 ... [2025-07-17 08:43:39.206062] 2025-07-17T08:43:39.2064478Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:43:39.2066830Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inplacing_pass.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'] ... [2025-07-17 08:43:39.206422] 2025-07-17T08:43:46.2666582Z 2025-07-17T08:43:46.2668166Z inductor/test_inplacing_pass 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inplacing_pass_1.1_0183978a75852fd5_.log 2025-07-17T08:43:46.2669659Z 2025-07-17T08:43:49.5919114Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:43:49.5921948Z import pkg_resources 2025-07-17T08:43:49.7207073Z Running inductor/test_perf 1/1 ... [2025-07-17 08:43:49.720339] 2025-07-17T08:43:49.7208193Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:43:49.7210333Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_perf.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'] ... [2025-07-17 08:43:49.720709] 2025-07-17T08:43:56.7443764Z 2025-07-17T08:43:56.7444922Z inductor/test_perf 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_perf_1.1_127b7f33a013ae7d_.log 2025-07-17T08:43:56.7445793Z 2025-07-17T08:43:59.8132810Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:43:59.8135367Z import pkg_resources 2025-07-17T08:43:59.9396195Z Running test_legacy_vmap 1/1 ... [2025-07-17 08:43:59.939323] 2025-07-17T08:43:59.9396607Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:43:59.9398961Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_legacy_vmap.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:43:59.939672] 2025-07-17T08:44:05.7125312Z 2025-07-17T08:44:05.7126223Z test_legacy_vmap 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_legacy_vmap_1.1_ef70c4fd1393187c_.log 2025-07-17T08:44:05.7166389Z Running 124 items in this shard: test/test_legacy_vmap.py::TestVmapAPILegacy::test_accepts_nested_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_backward_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_batched_gradient_basic, test/test_legacy_vmap.py::TestVmapAPILegacy::test_constant_function, test/test_legacy_vmap.py::TestVmapAPILegacy::test_different_map_dim_size_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_atan2, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_does_not_warn_by_default, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_masked_fill, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_multiple_returns, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_warns_when_warnings_are_enabled, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_with_undefined_grad, test/test_legacy_vmap.py::TestVmapAPILegacy::test_fallback_zero_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_func_with_no_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_functools_partial, test/test_legacy_vmap.py::TestVmapAPILegacy::test_grad_unsupported_interaction, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dim_not_in_tensor_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_in_dims_wrong_type_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_different_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_nary_same_levels, test/test_legacy_vmap.py::TestVmapAPILegacy::test_inplace_fallback_unary, test/test_legacy_vmap.py::TestVmapAPILegacy::test_integer_in_dim_but_not_tensor_input_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_inputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs, test/test_legacy_vmap.py::TestVmapAPILegacy::test_multiple_outputs_error_cases, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_non_default_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_different_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nested_with_same_map_dim, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nn_module, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_default_in_dims_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_tensor_output_raises, test/test_legacy_vmap.py::TestVmapAPILegacy::test_non_zero_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_none_in_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_nonzero_out_dims, test/test_legacy_vmap.py::TestVmapAPILegacy::test_noop_in_inner_vmap, test/test_legacy_vmap.py::TestVmapAPILegacy::test_not_enough_in_dims_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dim_out_of_bounds_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_and_num_outputs_mismatch_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_edge_case, test/test_legacy_vmap.py::TestVmapAPILegacy::test_out_dims_must_be_int_or_tuple_of_int_err_msg, test/test_legacy_vmap.py::TestVmapAPILegacy::test_single_input, test/test_legacy_vmap.py::TestVmapAPILegacy::test_unsupported_op_err_msg, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_T_numpy, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_as_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_binary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_bmm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_cat, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_chunk, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clamp, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_clone, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_comparison_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_conj, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_diagonal, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_dot, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_expand_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_fill_and_zero_inplace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_imag, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_contiguous, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_is_floating_point, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mm, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_movedim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_mv, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_narrow, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_empty_strided, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_new_zeros, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_no_random_op_support, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_reshape_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_result_type, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_select, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_slice, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_squeeze, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stack, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_stride, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_sum_dim, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_t, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_tensor_split, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_to, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_trace, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_transpose, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unary_pointwise_ops, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unbind, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_unfold, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_complex, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_view_as_real, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapOperatorsLegacy::test_vmap_fallback_check_ok, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_add_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_binary_cross_entropy_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_diagonal_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_div_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_expand_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_index_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_manyview_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_inplace_on_view_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_lgamma_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log1p_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_log_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_logsumexp_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_max_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_median_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_min_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_mul_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_permute_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_reshape_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_select_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sigmoid_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_slice_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_stack_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_sub_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_threshold_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_trace_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_unrelated_output_multiple_grad_cpu, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check, test/test_legacy_vmap.py::TestVmapBatchedGradientLegacyCPU::test_vmap_fallback_check_ok 2025-07-17T08:44:05.7205345Z 2025-07-17T08:44:09.0315763Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:44:09.0319250Z import pkg_resources 2025-07-17T08:44:09.2242814Z Running nn/test_multihead_attention 1/1 ... [2025-07-17 08:44:09.223919] 2025-07-17T08:44:09.2243738Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:44:09.2246676Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_multihead_attention.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'] ... [2025-07-17 08:44:09.224343] 2025-07-17T08:44:18.4525924Z 2025-07-17T08:44:18.4526901Z nn/test_multihead_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_multihead_attention_1.1_db574de786551974_.log 2025-07-17T08:44:18.4538210Z Running 19 items in this shard: test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attention_average_attn_weights_False, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attention_average_attn_weights_True, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_3d_attn_mask, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_fast_path_invalid_shape, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_invalid_shape, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_nested_tensor_outside_fast_path, test/nn/test_multihead_attention.py::TestMultiheadAttentionNN::test_multihead_attn_no_bias, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_batch_first_cpu_float16, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_batch_first_cpu_float32, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_batch_first_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_cpu_float16, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_cpu_float32, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attention_dtype_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_fast_path_query_and_bias_have_different_dtypes_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_fast_path_small_test_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_in_proj_bias_none_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_attn_in_proj_weight_none_cpu_float64, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_self_attn_two_masks_fast_path_cpu, test/nn/test_multihead_attention.py::TestMultiheadAttentionNNDeviceTypeCPU::test_multihead_self_attn_two_masks_fast_path_mock_cpu 2025-07-17T08:44:18.4549014Z 2025-07-17T08:44:21.6579232Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:44:21.6580824Z import pkg_resources 2025-07-17T08:44:21.7831482Z Running inductor/test_binary_folding 1/1 ... [2025-07-17 08:44:21.782861] 2025-07-17T08:44:21.7831989Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:44:21.7834575Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_binary_folding.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'] ... [2025-07-17 08:44:21.783205] 2025-07-17T08:45:27.3496056Z 2025-07-17T08:45:27.3497276Z inductor/test_binary_folding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_binary_folding_1.1_a243fb23293066eb_.log 2025-07-17T08:45:27.3499203Z Running 3 items in this shard: test/inductor/test_binary_folding.py::FreezingCpuTests::test_conv_binary_folding_cpu, test/inductor/test_binary_folding.py::FreezingCpuTests::test_conv_bn_folding_cpu, test/inductor/test_binary_folding.py::FreezingCpuTests::test_linear_binary_folding_cpu 2025-07-17T08:45:27.3500453Z 2025-07-17T08:45:30.6404099Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:45:30.6407168Z import pkg_resources 2025-07-17T08:45:30.8201687Z Running inductor/test_torchinductor_codegen_config_overrides 1/1 ... [2025-07-17 08:45:30.819776] 2025-07-17T08:45:30.8203158Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:45:30.8205821Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_torchinductor_codegen_config_overrides.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'] ... [2025-07-17 08:45:30.820222] 2025-07-17T08:45:40.9995100Z 2025-07-17T08:45:40.9996485Z inductor/test_torchinductor_codegen_config_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_torchinductor_codegen_config_overrides_1.1_d17f06de0a3121f7_.log 2025-07-17T08:45:40.9999415Z Running 3 items in this shard: test/inductor/test_torchinductor_codegen_config_overrides.py::CodegenInductorTest::test_force_pointwise_cat_force_pointwise_cat_False, test/inductor/test_torchinductor_codegen_config_overrides.py::CodegenInductorTest::test_force_pointwise_cat_force_pointwise_cat_True, test/inductor/test_torchinductor_codegen_config_overrides.py::CodegenInductorTest::test_kernel_fusion_thresholds 2025-07-17T08:45:41.0001629Z 2025-07-17T08:45:44.0844691Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:45:44.0848066Z import pkg_resources 2025-07-17T08:45:44.2358747Z Running torch_np/numpy_tests/core/test_numeric 1/1 ... [2025-07-17 08:45:44.235440] 2025-07-17T08:45:44.2360266Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:45:44.2362895Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/numpy_tests/core/test_numeric.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:45:44.235785] 2025-07-17T08:45:51.5120057Z 2025-07-17T08:45:51.5121208Z torch_np/numpy_tests/core/test_numeric 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_09c8a4f89a1cd575_.log 2025-07-17T08:45:51.5224324Z Running 273 items in this shard: test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_copies, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_negative_resize, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_repeats, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_reshape_from_zero, test/torch_np/numpy_tests/core/test_numeric.py::TestResize::test_zeroresize, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_choose, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_clip, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_compress, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_count_nonzero, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_cumproduct, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_diagonal, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_accuracy, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype2, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype3, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype4, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype5, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype6, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_dtype7, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-1, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-10, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_dunder_round_edgecases_val_2147483647_ndigits_-9, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_mean, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_prod, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_ptp, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_ravel, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_repeat, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_reshape, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round_2, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_round_py_consistency, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_searchsorted, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_size, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_squeeze, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_std, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_sum, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_swapaxes, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_take, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_trace, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_transpose, test/torch_np/numpy_tests/core/test_numeric.py::TestNonarrayArgs::test_var, test/torch_np/numpy_tests/core/test_numeric.py::TestIsscalar::test_isscalar, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_and_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_and_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_or_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_or_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_xor_eq, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_bitwise_xor_is, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolScalar::test_logical, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_all_any, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_logical_and_or_xor, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolArray::test_logical_not_abs, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolCmp::test_double, test/torch_np/numpy_tests/core/test_numeric.py::TestBoolCmp::test_float, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_default, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_divide_err, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_errobj, test/torch_np/numpy_tests/core/test_numeric.py::TestSeterr::test_set, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_D, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_F, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_d, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_e, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_floating_exceptions_typecode_f, test/torch_np/numpy_tests/core/test_numeric.py::TestFloatExceptions::test_warnings, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast_2, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_can_cast_values, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_coercion, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_coercion_2, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_promote_types_endian, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_result_type, test/torch_np/numpy_tests/core/test_numeric.py::TestTypes::test_tesult_type_2, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_2592_dtype0_count_10_error_index_5, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_2592_dtype0_count_10_error_index_9, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_empty_result, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_failed_itemsetting, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_lengths, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_too_few_items, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_types, test/torch_np/numpy_tests/core/test_numeric.py::TestFromiter::test_values, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_?, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_B, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_D, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_F, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_b, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_d, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_e, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_f, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_h, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_i, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_axis_all_dtypes_typecode_l, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_count_nonzero_list, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_countnonzero_axis_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_countnonzero_keepdims, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_onedim, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_onedim_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_trivial, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_trivial_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_twodim, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_zerod, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_nonzero_zerod_differs, test/torch_np/numpy_tests/core/test_numeric.py::TestNonzeroAndCountNonzero::test_sparse, test/torch_np/numpy_tests/core/test_numeric.py::TestIndex::test_boolean, test/torch_np/numpy_tests/core/test_numeric.py::TestIndex::test_boolean_edgecase, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_large_neg_int64, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_neg_width_boundaries, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_negative, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_positive, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_sufficient_width, test/torch_np/numpy_tests/core/test_numeric.py::TestBinaryRepr::test_zero, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_base3, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_base_range, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_negative, test/torch_np/numpy_tests/core/test_numeric.py::TestBaseRepr::test_positive, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equal, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equal_equal_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_array_equiv, test/torch_np/numpy_tests/core/test_numeric.py::TestArrayComparisons::test_none_compares_elementwise, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_array_double, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_func_takes_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_inplace_array, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_inplace_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_non_contig, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_property, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_scalar_nan_propagation_arr0_amin0_amax0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin2_amax2, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin_1_amax1, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_value_min_max_flip_amin_1_amax_0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_array_int32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_array_outint32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_memory_overlap, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple2, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_simple_int32, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_clip_with_out_transposed, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_noncontig_inplace, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_D, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_F, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_2_dtype_e, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_?, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_B, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_b, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_d, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_f, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_h, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_i, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_ones_pathological_dtype_l, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_double, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_inplace_01, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_inplace_02, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_inout_casting0, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_inout_casting_unsafe, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int32_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int64_inout, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_int64_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_nonnative, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_simple_out, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_01, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_02, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_03, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_04, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_05, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_06, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_07, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_08, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_09, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_10, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_11, test/torch_np/numpy_tests/core/test_numeric.py::TestClip::test_type_cast_12, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_equalnan, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_ip_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_ip_not_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_min_int, test/torch_np/numpy_tests/core/test_numeric.py::TestAllclose::test_no_parameter_modification, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_equal_nan, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_all_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_isclose_allclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_ip_none_isclose, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_no_parameter_modification, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_non_finite_scalar, test/torch_np/numpy_tests/core/test_numeric.py::TestIsclose::test_scalar_return, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_basic, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_ddof1, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_ddof2, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_out_scalar, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVar::test_scalars, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVarComplex::test_basic, test/torch_np/numpy_tests/core/test_numeric.py::TestStdVarComplex::test_scalars, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_for_reference_leak, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_full, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_ones, test/torch_np/numpy_tests/core/test_numeric.py::TestCreationFuncs::test_zeros, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc0_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc0_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc1_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc1_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc2_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype0, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_dtype_str_bytes_likefunc3_dtype1, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_empty_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_filled_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_ones_like, test/torch_np/numpy_tests/core/test_numeric.py::TestLikeFuncs::test_zeros_like, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_complex, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_float, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestCorrelate::test_zero_size, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_mode, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_no_overwrite, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_numpy_doc_examples, test/torch_np/numpy_tests/core/test_numeric.py::TestConvolve::test_object, test/torch_np/numpy_tests/core/test_numeric.py::TestDtypePositional::test_dtype_positional, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_2D, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_list, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_0, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_1, test/torch_np/numpy_tests/core/test_numeric.py::TestArgwhere::test_nd_nd_2, test/torch_np/numpy_tests/core/test_numeric.py::TestStringFunction::test_set_string_function, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll1d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll2d, test/torch_np/numpy_tests/core/test_numeric.py::TestRoll::test_roll_empty, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_exceptions, test/torch_np/numpy_tests/core/test_numeric.py::TestRollaxis::test_results, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_errors, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_multiples, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_new_position, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_move_to_end, test/torch_np/numpy_tests/core/test_numeric.py::TestMoveaxis::test_preserve_order, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x2, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_2x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_3x3, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_broadcasting_shapes, test/torch_np/numpy_tests/core/test_numeric.py::TestCross::test_uint8_int32_mixed_dtypes, test/torch_np/numpy_tests/core/test_numeric.py::TestOuterMisc::test_outer_out_param, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype0_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype1_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype2_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims0, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims1, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_return_type_dtype3_dims2, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_scalar_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_simple, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_single_input, test/torch_np/numpy_tests/core/test_numeric.py::TestIndices::test_sparse, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_C_and_F_simul, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_non_array_input, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_require_each, test/torch_np/numpy_tests/core/test_numeric.py::TestRequire::test_unknown_requirement, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_error_kwargs, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_in_args, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_broadcast_single_arg, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_number_of_arguments, test/torch_np/numpy_tests/core/test_numeric.py::TestBroadcast::test_shape_mismatch_error_message, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimension_einsum, test/torch_np/numpy_tests/core/test_numeric.py::TestTensordot::test_zero_dimensional 2025-07-17T08:45:51.5324403Z 2025-07-17T08:45:54.8398490Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:45:54.8400345Z import pkg_resources 2025-07-17T08:45:54.9665211Z Running dynamo/test_backward_higher_order_ops 1/1 ... [2025-07-17 08:45:54.966182] 2025-07-17T08:45:54.9666041Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:45:54.9667927Z 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'] ... [2025-07-17 08:45:54.966552] 2025-07-17T08:46:09.2529468Z 2025-07-17T08:46:09.2531067Z 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_dcd30f2b5e5fc275_.log 2025-07-17T08:46:09.2535901Z Running 7 items in this shard: test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_eager, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2_compiled_autograd, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2_compiled_autograd_graph_breaks, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_in_pt2_compiled_autograd_side_effect, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_make_bw, test/dynamo/test_backward_higher_order_ops.py::BackwardHigherOrderOpTests::test_invoke_make_fx_forward_contrived 2025-07-17T08:46:09.2539954Z 2025-07-17T08:46:12.5952228Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:46:12.5955523Z import pkg_resources 2025-07-17T08:46:12.7194379Z Running dynamo/test_optimizers 1/1 ... [2025-07-17 08:46:12.719063] 2025-07-17T08:46:12.7195415Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:46:12.7197933Z 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'] ... [2025-07-17 08:46:12.719438] 2025-07-17T08:46:17.7413704Z 2025-07-17T08:46:17.7414795Z dynamo/test_optimizers 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_optimizers_1.1_bf60cad60664c806_.log 2025-07-17T08:46:17.7416804Z Running 3 items in this shard: test/dynamo/test_optimizers.py::End2EndTests::test_init_group, test/dynamo/test_optimizers.py::End2EndTests::test_optimizing_over_tensor_with_requires_grad, test/dynamo/test_optimizers.py::End2EndTests::test_state_dict 2025-07-17T08:46:17.7418192Z 2025-07-17T08:46:20.9381230Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:46:20.9384966Z import pkg_resources 2025-07-17T08:46:21.1582776Z Running test_weak 1/1 ... [2025-07-17 08:46:21.157883] 2025-07-17T08:46:21.1583650Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:46:21.1625934Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_weak.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:46:21.162152] 2025-07-17T08:46:28.8883057Z 2025-07-17T08:46:28.8884821Z test_weak 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_weak_1.1_b7298f0a97d13602_.log 2025-07-17T08:46:28.8909068Z Running 39 items in this shard: test/test_weak.py::WeakTest::test_make_weak_keyed_dict_from_dict, test/test_weak.py::WeakTest::test_make_weak_keyed_dict_from_weak_keyed_dict, test/test_weak.py::WeakTest::test_make_weak_keyed_dict_repr, test/test_weak.py::WeakTest::test_threaded_weak_key_dict_copy, test/test_weak.py::WeakTest::test_threaded_weak_key_dict_deepcopy, test/test_weak.py::WeakTest::test_weak_keyed_bad_delitem, test/test_weak.py::WeakTest::test_weak_keyed_delitem, test/test_weak.py::WeakTest::test_weak_keyed_dict_popitem, test/test_weak.py::WeakTest::test_weak_keyed_dict_setdefault, test/test_weak.py::WeakTest::test_weak_keyed_dict_update, test/test_weak.py::WeakTest::test_weak_keyed_union_operators, test/test_weak.py::WeakKeyDictionaryTestCase::test_bool, test/test_weak.py::WeakKeyDictionaryTestCase::test_constructor, test/test_weak.py::WeakKeyDictionaryTestCase::test_get, test/test_weak.py::WeakKeyDictionaryTestCase::test_getitem, test/test_weak.py::WeakKeyDictionaryTestCase::test_items, test/test_weak.py::WeakKeyDictionaryTestCase::test_keys, test/test_weak.py::WeakKeyDictionaryTestCase::test_len, test/test_weak.py::WeakKeyDictionaryTestCase::test_pop, test/test_weak.py::WeakKeyDictionaryTestCase::test_popitem, test/test_weak.py::WeakKeyDictionaryTestCase::test_read, test/test_weak.py::WeakKeyDictionaryTestCase::test_setdefault, test/test_weak.py::WeakKeyDictionaryTestCase::test_update, test/test_weak.py::WeakKeyDictionaryTestCase::test_values, test/test_weak.py::WeakKeyDictionaryTestCase::test_write, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_bool, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_constructor, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_get, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_getitem, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_items, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_keys, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_len, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_pop, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_popitem, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_read, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_setdefault, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_update, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_values, test/test_weak.py::WeakKeyDictionaryScriptObjectTestCase::test_write 2025-07-17T08:46:28.8933049Z 2025-07-17T08:46:32.3342985Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:46:32.3344652Z import pkg_resources 2025-07-17T08:46:32.4600625Z Running dynamo/test_graph_deduplication 1/1 ... [2025-07-17 08:46:32.459711] 2025-07-17T08:46:32.4601470Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:46:32.4603745Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_graph_deduplication.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'] ... [2025-07-17 08:46:32.460111] 2025-07-17T08:46:39.0844206Z 2025-07-17T08:46:39.0845704Z dynamo/test_graph_deduplication 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_graph_deduplication_1.1_0a835b7515fec8fa_.log 2025-07-17T08:46:39.0858641Z Running 15 items in this shard: test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_autocast_ordering, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_cycle_detection_arg_and_additional_deps, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_cycle_detection_complex, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_cycle_detection_no_cycle, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_cycle_detection_simple, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_cycle_detection_single_node, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_cycle_detection_two_node, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_dependent_subgraphs, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_input_aliasing, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_input_mutation, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_multiple_subgraphs, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_mutation_ordering, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_output_nodes_last, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_single_subgraph, test/dynamo/test_graph_deduplication.py::GraphDededuplicationTests::test_single_subgraph2 2025-07-17T08:46:39.0869179Z 2025-07-17T08:46:42.3873734Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:46:42.3875392Z import pkg_resources 2025-07-17T08:46:42.5120358Z Running dynamo/test_sets 1/1 ... [2025-07-17 08:46:42.511710] 2025-07-17T08:46:42.5129098Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:46:42.5130355Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sets.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'] ... [2025-07-17 08:46:42.512105] 2025-07-17T08:46:53.7448614Z 2025-07-17T08:46:53.7449644Z dynamo/test_sets 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sets_1.1_537b7ae70247af21_.log 2025-07-17T08:46:53.7487002Z Running 124 items in this shard: test/dynamo/test_sets.py::CustomSetTests::test_custom_add, test/dynamo/test_sets.py::CustomSetTests::test_custom_contains, test/dynamo/test_sets.py::MiscTests::test_isdisjoint_with_generator, test/dynamo/test_sets.py::TestSetGuards::test_in_guard, test/dynamo/test_sets.py::TestSetGuards::test_set_guard_on_keys_change, test/dynamo/test_sets.py::TestSetGuards::test_set_multiple_types, test/dynamo/test_sets.py::TestSetGuards::test_set_recompile_on_key_change, test/dynamo/test_sets.py::TestSetGuards::test_set_recompile_on_key_pop, test/dynamo/test_sets.py::TestSetGuards::test_set_with_function, test/dynamo/test_sets.py::TestSetGuards::test_set_with_tensors, test/dynamo/test_sets.py::FrozensetTests::test_binop_and, test/dynamo/test_sets.py::FrozensetTests::test_binop_or, test/dynamo/test_sets.py::FrozensetTests::test_binop_sub, test/dynamo/test_sets.py::FrozensetTests::test_binop_xor, test/dynamo/test_sets.py::FrozensetTests::test_cmp_eq, test/dynamo/test_sets.py::FrozensetTests::test_cmp_greater_than, test/dynamo/test_sets.py::FrozensetTests::test_cmp_greater_than_or_equal, test/dynamo/test_sets.py::FrozensetTests::test_cmp_less_than, test/dynamo/test_sets.py::FrozensetTests::test_cmp_less_than_or_equal, test/dynamo/test_sets.py::FrozensetTests::test_cmp_ne, test/dynamo/test_sets.py::FrozensetTests::test_constructor_iterable, test/dynamo/test_sets.py::FrozensetTests::test_contains, test/dynamo/test_sets.py::FrozensetTests::test_copy, test/dynamo/test_sets.py::FrozensetTests::test_difference, test/dynamo/test_sets.py::FrozensetTests::test_equality, test/dynamo/test_sets.py::FrozensetTests::test_in_frozenset, test/dynamo/test_sets.py::FrozensetTests::test_intersection, test/dynamo/test_sets.py::FrozensetTests::test_isdisjoint, test/dynamo/test_sets.py::FrozensetTests::test_issubset, test/dynamo/test_sets.py::FrozensetTests::test_issuperset, test/dynamo/test_sets.py::FrozensetTests::test_symmetric_difference, test/dynamo/test_sets.py::FrozensetTests::test_to_frozenset, test/dynamo/test_sets.py::FrozensetTests::test_to_set, test/dynamo/test_sets.py::FrozensetTests::test_union, test/dynamo/test_sets.py::SetTests::test_add, test/dynamo/test_sets.py::SetTests::test_binop_and, test/dynamo/test_sets.py::SetTests::test_binop_or, test/dynamo/test_sets.py::SetTests::test_binop_sub, test/dynamo/test_sets.py::SetTests::test_binop_xor, test/dynamo/test_sets.py::SetTests::test_clear, test/dynamo/test_sets.py::SetTests::test_cmp_eq, test/dynamo/test_sets.py::SetTests::test_cmp_greater_than, test/dynamo/test_sets.py::SetTests::test_cmp_greater_than_or_equal, test/dynamo/test_sets.py::SetTests::test_cmp_less_than, test/dynamo/test_sets.py::SetTests::test_cmp_less_than_or_equal, test/dynamo/test_sets.py::SetTests::test_cmp_ne, test/dynamo/test_sets.py::SetTests::test_constructor_iterable, test/dynamo/test_sets.py::SetTests::test_contains, test/dynamo/test_sets.py::SetTests::test_copy, test/dynamo/test_sets.py::SetTests::test_difference, test/dynamo/test_sets.py::SetTests::test_difference_update, test/dynamo/test_sets.py::SetTests::test_discard, test/dynamo/test_sets.py::SetTests::test_equality, test/dynamo/test_sets.py::SetTests::test_in_frozenset, test/dynamo/test_sets.py::SetTests::test_intersection, test/dynamo/test_sets.py::SetTests::test_intersection_update, test/dynamo/test_sets.py::SetTests::test_isdisjoint, test/dynamo/test_sets.py::SetTests::test_issubset, test/dynamo/test_sets.py::SetTests::test_issuperset, test/dynamo/test_sets.py::SetTests::test_pop, test/dynamo/test_sets.py::SetTests::test_remove, test/dynamo/test_sets.py::SetTests::test_symmetric_difference, test/dynamo/test_sets.py::SetTests::test_symmetric_difference_update, test/dynamo/test_sets.py::SetTests::test_to_frozenset, test/dynamo/test_sets.py::SetTests::test_to_set, test/dynamo/test_sets.py::SetTests::test_union, test/dynamo/test_sets.py::SetTests::test_update, test/dynamo/test_sets.py::UserDefinedSetTests::test_add, test/dynamo/test_sets.py::UserDefinedSetTests::test_binop_and, test/dynamo/test_sets.py::UserDefinedSetTests::test_binop_or, test/dynamo/test_sets.py::UserDefinedSetTests::test_binop_sub, test/dynamo/test_sets.py::UserDefinedSetTests::test_binop_xor, test/dynamo/test_sets.py::UserDefinedSetTests::test_clear, test/dynamo/test_sets.py::UserDefinedSetTests::test_cmp_eq, test/dynamo/test_sets.py::UserDefinedSetTests::test_cmp_greater_than, test/dynamo/test_sets.py::UserDefinedSetTests::test_cmp_greater_than_or_equal, test/dynamo/test_sets.py::UserDefinedSetTests::test_cmp_less_than, test/dynamo/test_sets.py::UserDefinedSetTests::test_cmp_less_than_or_equal, test/dynamo/test_sets.py::UserDefinedSetTests::test_cmp_ne, test/dynamo/test_sets.py::UserDefinedSetTests::test_constructor_iterable, test/dynamo/test_sets.py::UserDefinedSetTests::test_contains, test/dynamo/test_sets.py::UserDefinedSetTests::test_copy, test/dynamo/test_sets.py::UserDefinedSetTests::test_difference, test/dynamo/test_sets.py::UserDefinedSetTests::test_difference_update, test/dynamo/test_sets.py::UserDefinedSetTests::test_discard, test/dynamo/test_sets.py::UserDefinedSetTests::test_equality, test/dynamo/test_sets.py::UserDefinedSetTests::test_in_frozenset, test/dynamo/test_sets.py::UserDefinedSetTests::test_intersection, test/dynamo/test_sets.py::UserDefinedSetTests::test_intersection_update, test/dynamo/test_sets.py::UserDefinedSetTests::test_isdisjoint, test/dynamo/test_sets.py::UserDefinedSetTests::test_issubset, test/dynamo/test_sets.py::UserDefinedSetTests::test_issuperset, test/dynamo/test_sets.py::UserDefinedSetTests::test_pop, test/dynamo/test_sets.py::UserDefinedSetTests::test_remove, test/dynamo/test_sets.py::UserDefinedSetTests::test_symmetric_difference, test/dynamo/test_sets.py::UserDefinedSetTests::test_symmetric_difference_update, test/dynamo/test_sets.py::UserDefinedSetTests::test_to_frozenset, test/dynamo/test_sets.py::UserDefinedSetTests::test_to_set, test/dynamo/test_sets.py::UserDefinedSetTests::test_union, test/dynamo/test_sets.py::UserDefinedSetTests::test_update, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_binop_and, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_binop_or, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_binop_sub, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_binop_xor, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_cmp_eq, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_cmp_greater_than, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_cmp_greater_than_or_equal, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_cmp_less_than, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_cmp_less_than_or_equal, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_cmp_ne, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_constructor_iterable, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_contains, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_copy, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_difference, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_equality, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_in_frozenset, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_intersection, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_isdisjoint, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_issubset, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_issuperset, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_symmetric_difference, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_to_frozenset, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_to_set, test/dynamo/test_sets.py::UserDefinedFrozensetTests::test_union 2025-07-17T08:46:53.7520332Z 2025-07-17T08:46:56.9155212Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:46:56.9157056Z import pkg_resources 2025-07-17T08:46:57.0409962Z Running distributions/test_constraints 1/1 ... [2025-07-17 08:46:57.040607] 2025-07-17T08:46:57.0410509Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:46:57.0411883Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_constraints.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'] ... [2025-07-17 08:46:57.040965] 2025-07-17T08:47:00.6105907Z 2025-07-17T08:47:00.6107503Z distributions/test_constraints 1/1 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_constraints_1.1_a0dfdc55f9cf0f28_.log 2025-07-17T08:47:00.6204404Z Running 136 items in this shard: test/distributions/test_constraints.py::test_constraint[False-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[False-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[False-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[False-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[False-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[False-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[False-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[False-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[False-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[False-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[False-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[False-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[False-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[False-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[False-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[False-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[False-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[False-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_constraint[True-constraint_fn0-False-value0], test/distributions/test_constraints.py::test_constraint[True-constraint_fn1-False-value1], test/distributions/test_constraints.py::test_constraint[True-constraint_fn2-False-value2], test/distributions/test_constraints.py::test_constraint[True-constraint_fn3-True-value3], test/distributions/test_constraints.py::test_constraint[True-constraint_fn4-False-value4], test/distributions/test_constraints.py::test_constraint[True-constraint_fn5-False-value5], test/distributions/test_constraints.py::test_constraint[True-constraint_fn6-True-value6], test/distributions/test_constraints.py::test_constraint[True-constraint_fn7-True-value7], test/distributions/test_constraints.py::test_constraint[True-constraint_fn8-False-value8], test/distributions/test_constraints.py::test_constraint[True-constraint_fn9-True-value9], test/distributions/test_constraints.py::test_constraint[True-constraint_fn10-False-value10], test/distributions/test_constraints.py::test_constraint[True-constraint_fn11-False-value11], test/distributions/test_constraints.py::test_constraint[True-constraint_fn12-True-value12], test/distributions/test_constraints.py::test_constraint[True-constraint_fn13-True-value13], test/distributions/test_constraints.py::test_constraint[True-constraint_fn14-False-value14], test/distributions/test_constraints.py::test_constraint[True-constraint_fn15-True-value15], test/distributions/test_constraints.py::test_constraint[True-constraint_fn16-True-value16], test/distributions/test_constraints.py::test_constraint[True-constraint_fn17-True-value17], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[False-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_biject_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_biject_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args15], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args16], test/distributions/test_constraints.py::test_biject_to[True-_Interval-args17], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_biject_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_biject_to[True-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[False-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[False-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[False-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[False-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[False-constraint_fn24-args24], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn0-args0], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn1-args1], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn2-args2], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args3], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args4], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args5], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThan-args6], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args7], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args8], test/distributions/test_constraints.py::test_transform_to[True-_GreaterThanEq-args9], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args10], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args11], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args12], test/distributions/test_constraints.py::test_transform_to[True-_LessThan-args13], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn14-args14], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args15], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args16], test/distributions/test_constraints.py::test_transform_to[True-_Interval-args17], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args18], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args19], test/distributions/test_constraints.py::test_transform_to[True-_HalfOpenInterval-args20], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn21-args21], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn22-args22], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn23-args23], test/distributions/test_constraints.py::test_transform_to[True-constraint_fn24-args24] 2025-07-17T08:47:00.6297963Z 2025-07-17T08:47:03.9361476Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:47:03.9363068Z import pkg_resources 2025-07-17T08:47:04.0606037Z Running test_tensorboard 1/1 ... [2025-07-17 08:47:04.060304] 2025-07-17T08:47:04.0606678Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:47:04.0609406Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorboard.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:47:04.060709] 2025-07-17T08:47:13.0884212Z 2025-07-17T08:47:13.0886143Z inductor/test_cpu_select_algorithm 6/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_6.16_093a42f34cb844b2_.log 2025-07-17T08:47:13.1047026Z Running 98 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order4_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_bs_50_Mdim_192_Kdim_196_Ndim_385_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_fused_epilogues_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_with_pointwise_bs_5_Mdim_384_Kdim_96_Ndim_64_epilogue_relu_cpu_float16, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_grouped_linear_epilogue_batch_size_16_in_features_52_out_features_32_input_3d_True_bias2_epilogue4_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_grouped_linear_invalid_batch_size_16_in_features_52_out_features_32_gemm_num_3_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int4_woq_mm_amx_batch_size_4_in_features_128_out_features_128_group_size_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int4_woq_mm_avx512_batch_size_1_in_features_256_out_features_128_group_size_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_32_mid_dim_1_in_features_1024_out_features_64_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_32_mid_dim_8_in_features_128_out_features_1024_cpu_bfloat16, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1000_out_features_1_bias_True_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1000_out_features_1024_bias_True_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1_out_features_1024_bias_True_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1_out_features_1_bias_False_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1_out_features_1_bias_True_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_permute_batch_size_1_in_features_16_image_size_18_out_features_32_bias_False_has_non_epilogue_users_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_leaky_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_tanh_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_leaky_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_mul_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_sub_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_add_cpu_float16, 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test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_epilogue_gelu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_epilogue_gelu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_int8_mixed_bf16_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_False_bias2_epilogue3_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_False_bias2_epilogue4_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_False_bias3_epilogue0_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_True_bias3_epilogue3_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_out_features_1024_bias_False_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_out_features_1024_bias_True_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1_out_features_1024_bias_True_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_k_slicing_dynamic_shapes_batch_size_2_in_features_1000_out_features_2_bias_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_hardswish_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_gelu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_silu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_add_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_sigmoid_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_False_epilogue_mul_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_div_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_div_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_mul_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_unary_relu_binary_add_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_unary_relu_binary_div_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_unary_relu_binary_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_16_in_features_128_out_features_65_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_3_in_features_128_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_49_in_features_4_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_int8_mixed_bf16_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_int8_mixed_bf16_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_epilogue_gelu_cpu_float32 2025-07-17T08:47:13.1206959Z 2025-07-17T08:47:16.2854010Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:47:16.2857694Z import pkg_resources 2025-07-17T08:47:16.4090642Z Running inductor/test_split_cat_fx_aten_passes 1/1 ... [2025-07-17 08:47:16.408649] 2025-07-17T08:47:16.4091605Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:47:16.4094350Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_split_cat_fx_aten_passes.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'] ... [2025-07-17 08:47:16.409045] 2025-07-17T08:47:25.9391863Z 2025-07-17T08:47:25.9393356Z inductor/test_split_cat_fx_aten_passes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_split_cat_fx_aten_passes_1.1_0a8144cf50c2f3a4_.log 2025-07-17T08:47:25.9398126Z Running 5 items in this shard: test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_move_view_after_cat_aten, test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_select_cat_post_grad, test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_split_cat_post_grad, test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAten::test_split_cat_post_grad_singular, test/inductor/test_split_cat_fx_aten_passes.py::TestSplitCatAtenNormalizationPasses::test_split_aten_normalization 2025-07-17T08:47:25.9402017Z 2025-07-17T08:47:29.7367061Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:47:29.7370399Z import pkg_resources 2025-07-17T08:47:29.8607714Z Running dynamo/test_structured_trace 1/1 ... [2025-07-17 08:47:29.860420] 2025-07-17T08:47:29.8608645Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:47:29.8611375Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_structured_trace.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:47:29.860832] 2025-07-17T08:47:30.9164303Z 2025-07-17T08:47:30.9165429Z test_tensorboard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorboard_1.1_2b0dbff7fa52acb4_.log 2025-07-17T08:47:30.9182710Z Running 50 items in this shard: test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_autograd_np, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_histogram, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_histogram_raw, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_np, test/test_tensorboard.py::TestTensorBoardPyTorchNumpy::test_pytorch_write, test/test_tensorboard.py::TestTensorBoardUtils::test_convert_to_HWC_dtype_remains_same, test/test_tensorboard.py::TestTensorBoardUtils::test_numpy_vid_uint8, test/test_tensorboard.py::TestTensorBoardUtils::test_prepare_video, test/test_tensorboard.py::TestTensorBoardUtils::test_to_HWC, test/test_tensorboard.py::TestTensorBoardWriter::test_writer, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_pathlib, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_summary_writer_close, test/test_tensorboard.py::TestTensorBoardSummaryWriter::test_summary_writer_ctx, test/test_tensorboard.py::TestTensorBoardEmbedding::test_embedding, test/test_tensorboard.py::TestTensorBoardEmbedding::test_embedding_64, test/test_tensorboard.py::TestTensorBoardSummary::test_audio, test/test_tensorboard.py::TestTensorBoardSummary::test_custom_scalars, test/test_tensorboard.py::TestTensorBoardSummary::test_empty_input, test/test_tensorboard.py::TestTensorBoardSummary::test_float32_image, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_auto, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_doane, test/test_tensorboard.py::TestTensorBoardSummary::test_histogram_fd, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_3_channel_batched, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_boxes, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_one_channel, test/test_tensorboard.py::TestTensorBoardSummary::test_image_with_one_channel_batched, test/test_tensorboard.py::TestTensorBoardSummary::test_image_without_channel, test/test_tensorboard.py::TestTensorBoardSummary::test_list_input, test/test_tensorboard.py::TestTensorBoardSummary::test_mesh, test/test_tensorboard.py::TestTensorBoardSummary::test_scalar_new_style, test/test_tensorboard.py::TestTensorBoardSummary::test_text, test/test_tensorboard.py::TestTensorBoardSummary::test_uint8_image, test/test_tensorboard.py::TestTensorBoardSummary::test_video, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_mlp_graph, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_nested_nn_squential, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_pytorch_graph, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_pytorch_graph_dict_input, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_torchvision_smoke, test/test_tensorboard.py::TestTensorBoardPytorchGraph::test_wrong_input_size, test/test_tensorboard.py::TestTensorBoardFigure::test_figure, test/test_tensorboard.py::TestTensorBoardFigure::test_figure_list, test/test_tensorboard.py::TestTensorBoardNumpy::test_pytorch_np_expect_fail, test/test_tensorboard.py::TestTensorBoardNumpy::test_scalar, test/test_tensorboard.py::TestTensorProtoSummary::test_complex_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_empty_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_float_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_half_tensor_proto_bfloat16_proto_type_14, test/test_tensorboard.py::TestTensorProtoSummary::test_half_tensor_proto_float16_proto_type_19, test/test_tensorboard.py::TestTensorProtoSummary::test_int_tensor_proto, test/test_tensorboard.py::TestTensorProtoSummary::test_scalar_tensor_proto 2025-07-17T08:47:30.9199108Z 2025-07-17T08:47:34.0974855Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:47:34.0976515Z import pkg_resources 2025-07-17T08:47:34.2236944Z Running profiler/test_cpp_thread 1/1 ... [2025-07-17 08:47:34.223366] 2025-07-17T08:47:34.2237439Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:47:34.2239896Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_cpp_thread.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'] ... [2025-07-17 08:47:34.223771] 2025-07-17T08:47:37.5930146Z 2025-07-17T08:47:37.5931677Z profiler/test_cpp_thread 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_cpp_thread_1.1_489c86cd5676da72_.log 2025-07-17T08:47:37.5937882Z Running 6 items in this shard: test/profiler/test_cpp_thread.py::CppThreadTestCUDA::test_profile_memory_cuda, test/profiler/test_cpp_thread.py::CppThreadTestCUDA::test_with_enable_profiler_in_child_thread_cuda, test/profiler/test_cpp_thread.py::CppThreadTestCUDA::test_without_enable_profiler_in_child_thread_cuda, test/profiler/test_cpp_thread.py::CppThreadTestXPU::test_profile_memory_xpu, test/profiler/test_cpp_thread.py::CppThreadTestXPU::test_with_enable_profiler_in_child_thread_xpu, test/profiler/test_cpp_thread.py::CppThreadTestXPU::test_without_enable_profiler_in_child_thread_xpu 2025-07-17T08:47:37.5942763Z 2025-07-17T08:47:40.6775597Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:47:40.6778514Z import pkg_resources 2025-07-17T08:47:40.8005229Z Running dynamo/test_decorators 1/1 ... [2025-07-17 08:47:40.800123] 2025-07-17T08:47:40.8006339Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:47:40.8009485Z 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'] ... [2025-07-17 08:47:40.800529] 2025-07-17T08:47:55.7134693Z 2025-07-17T08:47:55.7135829Z dynamo/test_structured_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_structured_trace_1.1_22758afb51f6ac70_.log 2025-07-17T08:47:55.7144603Z Running 21 items in this shard: test/dynamo/test_structured_trace.py::StructuredTraceTest::test_chromium_event, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_codecache, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_compile_id_serialization_deserialization, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_compiled_autograd_attribution, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_compiled_autograd_chromium, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_compiled_autograd_id, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_cudagraphs, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_ddp_graphs, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_dump_file, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_dynamo_error, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_example_fn, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_example_training_fn, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_graph_breaks, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_graph_sizes_dynamic, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_guards_recompiles, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_inductor_error, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_make_fx_fail_partial, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_recompile_user_contexts, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_recompile_user_contexts_iteration, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_recompiles, test/dynamo/test_structured_trace.py::StructuredTraceTest::test_schedule 2025-07-17T08:47:55.7152749Z 2025-07-17T08:47:57.7896656Z 2025-07-17T08:47:57.7897656Z dynamo/test_decorators 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_decorators_1.1_738dbeaafa3a4a70_.log 2025-07-17T08:47:57.7921932Z Running 62 items in this shard: test/dynamo/test_decorators.py::DecoratorTests::test_allow_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_allow_in_graph_no_id_reuse, test/dynamo/test_decorators.py::DecoratorTests::test_assume_constant_result_on_computation_with_graph_input, test/dynamo/test_decorators.py::DecoratorTests::test_assume_constant_result_on_user_defined_fn, test/dynamo/test_decorators.py::DecoratorTests::test_class_methods, test/dynamo/test_decorators.py::DecoratorTests::test_disable_for_custom_op, test/dynamo/test_decorators.py::DecoratorTests::test_disable_ignores_outer_wraps, test/dynamo/test_decorators.py::DecoratorTests::test_disable_nn_module_with_class_decorator, test/dynamo/test_decorators.py::DecoratorTests::test_disable_nn_modules_forward_hook, test/dynamo/test_decorators.py::DecoratorTests::test_disable_optimize, test/dynamo/test_decorators.py::DecoratorTests::test_disable_recursive_false, test/dynamo/test_decorators.py::DecoratorTests::test_disable_recursive_false_weird, test/dynamo/test_decorators.py::DecoratorTests::test_disallow_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_dont_skip_tracing, test/dynamo/test_decorators.py::DecoratorTests::test_graph_break, test/dynamo/test_decorators.py::DecoratorTests::test_incorrect_usage_disallow_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_mark_static_address_guarded, test/dynamo/test_decorators.py::DecoratorTests::test_mark_static_address_unguarded, test/dynamo/test_decorators.py::DecoratorTests::test_mark_static_nn_module, test/dynamo/test_decorators.py::DecoratorTests::test_nested_compile_fullgraph, test/dynamo/test_decorators.py::DecoratorTests::test_nested_disable_decorator, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_newly_constructed_trace_register_constant_type_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_captured_external_tensor, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_custom_class_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_inside_compiled_function, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_inside_compiled_function_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_inside_compiled_function_kwarg, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_nested_custom_class, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_nested_custom_class_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_newly_constructed_custom_class_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_newly_constructed_dict_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_no_action_at_a_distance, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_object_in_context_error, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_on_method, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_custom_class, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_custom_class_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_dict, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_dict_with_side_effects, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_pre_existing_register_constant_type_guard, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_tensor_args, test/dynamo/test_decorators.py::DecoratorTests::test_nonstrict_trace_tuple_and_sym_int_output, test/dynamo/test_decorators.py::DecoratorTests::test_patch_dynamo_config_errors, test/dynamo/test_decorators.py::DecoratorTests::test_set_fullgraph, test/dynamo/test_decorators.py::DecoratorTests::test_set_fullgraph_error, test/dynamo/test_decorators.py::DecoratorTests::test_set_fullgraph_export, test/dynamo/test_decorators.py::DecoratorTests::test_set_fullgraph_nested, test/dynamo/test_decorators.py::DecoratorTests::test_set_fullgraph_nested_deep, test/dynamo/test_decorators.py::DecoratorTests::test_set_fullgraph_nested_with_skip, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_aot_eager_then_compile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_eager_on_recompile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_eager_then_compile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_eager_then_compile_with_graph_break, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_fail_on_recompile, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_fail_on_recompile_with_disable, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_forbid_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_force_backend, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_force_backend_with_disable, test/dynamo/test_decorators.py::DecoratorTests::test_set_stance_force_eager, test/dynamo/test_decorators.py::DecoratorTests::test_skip_frame, test/dynamo/test_decorators.py::DecoratorTests::test_substitute_in_graph, test/dynamo/test_decorators.py::DecoratorTests::test_torch_guards_stack_frame_register_inlining_disable, test/dynamo/test_decorators.py::DecoratorTests::test_torch_guards_stack_frame_register_inlining_partially_disable 2025-07-17T08:47:57.7945473Z 2025-07-17T08:47:58.8539695Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:47:58.8543008Z import pkg_resources 2025-07-17T08:47:59.0010104Z Running dynamo/test_debug_utils 1/1 ... [2025-07-17 08:47:59.000715] 2025-07-17T08:47:59.0010581Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:47:59.0013465Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_debug_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:47:59.001109] 2025-07-17T08:48:00.9356530Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:48:00.9358388Z import pkg_resources 2025-07-17T08:48:01.0597474Z Running functorch/test_ac_knapsack 1/1 ... [2025-07-17 08:48:01.059422] 2025-07-17T08:48:01.0598061Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:48:01.0600165Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac_knapsack.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'] ... [2025-07-17 08:48:01.059750] 2025-07-17T08:48:04.7801485Z 2025-07-17T08:48:04.7802459Z functorch/test_ac_knapsack 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_knapsack_1.1_be0acb6bb68c6b86_.log 2025-07-17T08:48:04.7810199Z Running 15 items in this shard: test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_full_joint_nx_graph, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_get_knapsack_memory_input, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_get_knapsack_runtime_input, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_get_non_ac_peak_memory, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_get_theoretical_max_runtime, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_inialize_from_graph, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_recomputable_node_only_graph, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_recomputable_node_only_graph_with_larger_graph_context, test/functorch/test_ac_knapsack.py::TestGraphInfoProvider::test_simplified_fx_joint_graph, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_evaluate_distribution_of_results_for_knapsack_algo, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_evaluate_knapsack_output_accounting_for_backward_pass, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_evaluate_knapsack_output_not_accounting_for_backward_pass, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_evaluate_knapsack_output_with_wrong_sized_values, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_get_backward_memory_from_topologically_sorted_graph, test/functorch/test_ac_knapsack.py::TestKnapsackEvaluator::test_get_knee_point_memory_budget 2025-07-17T08:48:04.7817214Z 2025-07-17T08:48:06.1253786Z 2025-07-17T08:48:06.1255377Z dynamo/test_debug_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_debug_utils_1.1_1e0a55c678dd9236_.log 2025-07-17T08:48:06.1258125Z Running 2 items in this shard: test/dynamo/test_debug_utils.py::TestDebugUtilsCPU::test_cast_model_to_fp64_dtype_args_cpu, test/dynamo/test_debug_utils.py::TestDebugUtilsCPU::test_generate_env_vars_string_cpu 2025-07-17T08:48:06.1259752Z 2025-07-17T08:48:08.0512195Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:48:08.0513745Z import pkg_resources 2025-07-17T08:48:08.1762538Z Running functorch/test_ac 1/1 ... [2025-07-17 08:48:08.175922] 2025-07-17T08:48:08.1762990Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:48:08.1765237Z 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'] ... [2025-07-17 08:48:08.176289] 2025-07-17T08:48:09.3701160Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:48:09.3704445Z import pkg_resources 2025-07-17T08:48:09.4973820Z Running dynamo/test_base_hop 1/1 ... [2025-07-17 08:48:09.496974] 2025-07-17T08:48:09.4974674Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:48:09.4978152Z 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'] ... [2025-07-17 08:48:09.497486] 2025-07-17T08:48:15.0361948Z 2025-07-17T08:48:15.0362916Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_a71eb08642cfecb6_.log 2025-07-17T08:48:15.0363635Z 2025-07-17T08:48:17.9748894Z 2025-07-17T08:48:17.9750528Z 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_1d20a8df5b9b1caa_.log 2025-07-17T08:48:17.9760625Z Running 11 items in this shard: test/dynamo/test_base_hop.py::BaseHOPTest::test_aliasing_mutation_error, test/dynamo/test_base_hop.py::BaseHOPTest::test_aot_eager, test/dynamo/test_base_hop.py::BaseHOPTest::test_auto_functionalize, test/dynamo/test_base_hop.py::BaseHOPTest::test_dynamo, test/dynamo/test_base_hop.py::BaseHOPTest::test_eager_call, test/dynamo/test_base_hop.py::BaseHOPTest::test_int_input, test/dynamo/test_base_hop.py::BaseHOPTest::test_none_input, test/dynamo/test_base_hop.py::BaseHOPTest::test_schema_gen_pytree_in_out, test/dynamo/test_base_hop.py::BaseHOPTest::test_schema_gen_pytree_in_out_with_mutation, test/dynamo/test_base_hop.py::BaseHOPTest::test_schema_gen_single_return, test/dynamo/test_base_hop.py::BaseHOPTest::test_schema_gen_single_return_with_mutation 2025-07-17T08:48:17.9768070Z 2025-07-17T08:48:18.3824486Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:48:18.3827580Z import pkg_resources 2025-07-17T08:48:18.5402898Z Running dynamo/test_modes 1/1 ... [2025-07-17 08:48:18.539941] 2025-07-17T08:48:18.5403406Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:48:18.5405076Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modes.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'] ... [2025-07-17 08:48:18.540284] 2025-07-17T08:48:21.2192391Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:48:21.2194201Z import pkg_resources 2025-07-17T08:48:21.3432844Z Running inductor/test_inductor_freezing 1/1 ... [2025-07-17 08:48:21.342994] 2025-07-17T08:48:21.3433344Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:48:21.3436737Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_inductor_freezing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:48:21.343387] 2025-07-17T08:49:02.3214459Z 2025-07-17T08:49:02.3216199Z inductor/test_inductor_freezing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_inductor_freezing_1.1_3dca9b6f164912f5_.log 2025-07-17T08:49:02.3236570Z Running 24 items in this shard: test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_aliased_param_return_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_autocast_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_bn_with_multi_bn_share_conv_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_functional_bn_with_multi_bn_share_conv_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_layout_convert_with_view_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_multiple_uses_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_weight_layout_convert_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_conv_with_as_strided_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_cpp_wrapper_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_dont_change_dtype_folding_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_error_on_eager_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_hardswish_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_bn_with_module_sharing_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_folded_conv_functional_bn_with_module_sharing_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_mm_concat_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_mutation_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_param_deallocated_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_redundant_clone_for_layout_convert_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_rng_op_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_static_indices_cudagraph_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_symint_not_folded_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_unequal_bias_horizontal_addmm_fusion_cpu, test/inductor/test_inductor_freezing.py::FreezingCpuTests::test_unfolded_bn_cpu 2025-07-17T08:49:02.3254696Z 2025-07-17T08:49:05.5534969Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:05.5538081Z import pkg_resources 2025-07-17T08:49:05.6790754Z Running dynamo/test_profiler 1/1 ... [2025-07-17 08:49:05.678729] 2025-07-17T08:49:05.6791233Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:05.6793401Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:49:05.679104] 2025-07-17T08:49:07.6291510Z 2025-07-17T08:49:07.6292665Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_c01f914070d4f532_.log 2025-07-17T08:49:07.6303044Z Running 24 items in this shard: test/dynamo/test_modes.py::TorchDispatchModeTests::test_skip_torch_dispatch_modes, test/dynamo/test_modes.py::TorchFunctionModeTests::test_builtin_equivalent_funcs, test/dynamo/test_modes.py::TorchFunctionModeTests::test_error_empty_stack_pop_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_expand, test/dynamo/test_modes.py::TorchFunctionModeTests::test_flex_attention, test/dynamo/test_modes.py::TorchFunctionModeTests::test_intermedate_torch_function_mode_construction_mutation, test/dynamo/test_modes.py::TorchFunctionModeTests::test_is_torch_function_all_disabled, test/dynamo/test_modes.py::TorchFunctionModeTests::test_len_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_nested_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_pop_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_push_torch_function_mode, test/dynamo/test_modes.py::TorchFunctionModeTests::test_register_hook, test/dynamo/test_modes.py::TorchFunctionModeTests::test_stack_state_clear_default_device, test/dynamo/test_modes.py::TorchFunctionModeTests::test_stack_state_mutation_default_device, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_and_pop_graph_break, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_and_pop_graph_break_mutation, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_disable, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_enabled_guard, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_enter_exit, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_graph_break, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_guards_cpp, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_guards_py, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_highest_priority, test/dynamo/test_modes.py::TorchFunctionModeTests::test_torch_function_mode_restore_on_exc 2025-07-17T08:49:07.6312662Z 2025-07-17T08:49:10.7832112Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:10.7835584Z import pkg_resources 2025-07-17T08:49:10.9068246Z Running dynamo/test_sources 1/1 ... [2025-07-17 08:49:10.906408] 2025-07-17T08:49:10.9069365Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:10.9072787Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sources.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:49:10.906781] 2025-07-17T08:49:14.5267039Z 2025-07-17T08:49:14.5268256Z dynamo/test_sources 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sources_1.1_5923625eb360a450_.log 2025-07-17T08:49:14.5270629Z Running 3 items in this shard: test/dynamo/test_sources.py::SourceTests::test_is_local, test/dynamo/test_sources.py::SourceTests::test_property_closure, test/dynamo/test_sources.py::SourceTests::test_supported_nodes 2025-07-17T08:49:14.5272417Z 2025-07-17T08:49:17.6071261Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:17.6073800Z import pkg_resources 2025-07-17T08:49:17.7327201Z Running inductor/test_config 1/1 ... [2025-07-17 08:49:17.732386] 2025-07-17T08:49:17.7327685Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:17.7330018Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_config.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:49:17.732769] 2025-07-17T08:49:21.1171327Z 2025-07-17T08:49:21.1173122Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_e9c92f56ced84cd9_.log 2025-07-17T08:49:21.1183706Z Running 11 items in this shard: test/dynamo/test_profiler.py::DynamoProfilerTests::test_dynamo_timed_profiling_backend_compile, test/dynamo/test_profiler.py::DynamoProfilerTests::test_dynamo_timed_profiling_isolated, test/dynamo/test_profiler.py::DynamoProfilerTests::test_execution_trace_dynamic_shapes, test/dynamo/test_profiler.py::DynamoProfilerTests::test_profile_dynamic_shapes_compilation, test/dynamo/test_profiler.py::DynamoProfilerTests::test_profile_dynamic_shapes_list_compilation, test/dynamo/test_profiler.py::DynamoProfilerTests::test_profile_dynamic_shapes_runtime, test/dynamo/test_profiler.py::DynamoProfilerTests::test_profiler_cache_lookup, test/dynamo/test_profiler.py::DynamoProfilerTests::test_profiler_cache_lookup_profiler_step, test/dynamo/test_profiler.py::DynamoProfilerTests::test_profiler_dynamo_compiled_region, test/dynamo/test_profiler.py::DynamoProfilerTests::test_profiler_enabled, test/dynamo/test_profiler.py::DynamoProfilerTests::test_profiler_record_function_ignore 2025-07-17T08:49:21.1192905Z 2025-07-17T08:49:24.3744854Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:24.3746597Z import pkg_resources 2025-07-17T08:49:24.4991972Z Running test_datapipe 1/1 ... [2025-07-17 08:49:24.498881] 2025-07-17T08:49:24.4992442Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:24.4994653Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_datapipe.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'] ... [2025-07-17 08:49:24.499218] 2025-07-17T08:49:29.0647838Z 2025-07-17T08:49:29.0649025Z inductor/test_config 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_config_1.1_08c9346be3296f3b_.log 2025-07-17T08:49:29.0654063Z Running 13 items in this shard: test/inductor/test_config.py::TestInductorConfig::test_api_options, test/inductor/test_config.py::TestInductorConfig::test_codegen_skips_custom_passes, test/inductor/test_config.py::TestInductorConfig::test_compile_api, test/inductor/test_config.py::TestInductorConfig::test_compile_api_passes_config, test/inductor/test_config.py::TestInductorConfig::test_get_compiler_config, test/inductor/test_config.py::TestInductorConfig::test_hasattr, test/inductor/test_config.py::TestInductorConfig::test_invalid_backend, test/inductor/test_config.py::TestInductorConfig::test_invalid_names, test/inductor/test_config.py::TestInductorConfig::test_non_inductor_backend, test/inductor/test_config.py::TestInductorConfig::test_options_do_something, test/inductor/test_config.py::TestInductorConfig::test_patch, test/inductor/test_config.py::TestInductorConfig::test_save_load, test/inductor/test_config.py::TestInductorConfig::test_set 2025-07-17T08:49:29.0658376Z 2025-07-17T08:49:32.3786718Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:32.3789580Z import pkg_resources 2025-07-17T08:49:32.5043009Z Running functorch/test_logging 1/1 ... [2025-07-17 08:49:32.503893] 2025-07-17T08:49:32.5043469Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:32.5044636Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_logging.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:49:32.504225] 2025-07-17T08:49:36.2238258Z 2025-07-17T08:49:36.2239574Z functorch/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_logging_1.1_d6c33045c13cd4f0_.log 2025-07-17T08:49:36.2241404Z Running 1 items in this shard: test/functorch/test_logging.py::TestAOTLogging::test_logging 2025-07-17T08:49:36.2242058Z 2025-07-17T08:49:39.6492171Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:39.6495154Z import pkg_resources 2025-07-17T08:49:39.7784495Z Running dynamo/test_flat_apply 1/1 ... [2025-07-17 08:49:39.778059] 2025-07-17T08:49:39.7784976Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:39.7787205Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_flat_apply.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:49:39.778469] 2025-07-17T08:49:41.2879832Z 2025-07-17T08:49:41.2880785Z test_datapipe 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_datapipe_1.1_8c8ab4ab2b8e4a95_.log 2025-07-17T08:49:41.2912458Z Running 94 items in this shard: test/test_datapipe.py::TestDataChunk::test_as_string, test/test_datapipe.py::TestDataChunk::test_getitem, test/test_datapipe.py::TestDataChunk::test_iter, test/test_datapipe.py::TestDataChunk::test_len, test/test_datapipe.py::TestDataChunk::test_random_shuffle, test/test_datapipe.py::TestDataChunk::test_reverse, test/test_datapipe.py::TestDataChunk::test_sort, test/test_datapipe.py::TestStreamWrapper::test_api, test/test_datapipe.py::TestStreamWrapper::test_dir, test/test_datapipe.py::TestStreamWrapper::test_pickle, test/test_datapipe.py::TestStreamWrapper::test_repr, test/test_datapipe.py::TestIterableDataPipeBasic::test_demux_mux_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_groupby_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_listdirfiles_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_listdirfilesdeterministic_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_map_with_col_file_handle_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_openfilesfromdisk_iterable_datapipe, test/test_datapipe.py::TestIterableDataPipeBasic::test_routeddecoder_iterable_datapipe, test/test_datapipe.py::TestCaptureDataFrame::test_basic_capture, test/test_datapipe.py::TestDataFramesPipes::test_batch, test/test_datapipe.py::TestDataFramesPipes::test_capture, test/test_datapipe.py::TestDataFramesPipes::test_collate, test/test_datapipe.py::TestDataFramesPipes::test_filter, test/test_datapipe.py::TestDataFramesPipes::test_shuffle, test/test_datapipe.py::TestDataFramesPipes::test_unbatch, test/test_datapipe.py::TestFunctionalIterDataPipe::test_batch_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_collate_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_concat_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_demux_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_docstring, test/test_datapipe.py::TestFunctionalIterDataPipe::test_filter_datapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_fork_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_iterable_wrapper_datapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_map_dict_with_col_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_map_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_map_tuple_list_with_col_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_mux_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_sampler_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_serializable, test/test_datapipe.py::TestFunctionalIterDataPipe::test_serializable_with_dill, test/test_datapipe.py::TestFunctionalIterDataPipe::test_shuffler_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_stream_reader_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_unbatch_iterdatapipe, test/test_datapipe.py::TestFunctionalIterDataPipe::test_zip_iterdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_batch_mapdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_concat_mapdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_docstring, test/test_datapipe.py::TestFunctionalMapDataPipe::test_map_mapdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_sequence_wrapper_datapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_serializable, test/test_datapipe.py::TestFunctionalMapDataPipe::test_serializable_with_dill, test/test_datapipe.py::TestFunctionalMapDataPipe::test_shuffler_mapdatapipe, test/test_datapipe.py::TestFunctionalMapDataPipe::test_zip_mapdatapipe, test/test_datapipe.py::TestTyping::test_compile_time, test/test_datapipe.py::TestTyping::test_construct_time, test/test_datapipe.py::TestTyping::test_isinstance, test/test_datapipe.py::TestTyping::test_issubinstance, test/test_datapipe.py::TestTyping::test_protocol, test/test_datapipe.py::TestTyping::test_reinforce, test/test_datapipe.py::TestTyping::test_runtime, test/test_datapipe.py::TestTyping::test_subtype, test/test_datapipe.py::TestGraph::test_simple_traverse, test/test_datapipe.py::TestGraph::test_traverse_circular_datapipe, test/test_datapipe.py::TestGraph::test_traverse_forked, test/test_datapipe.py::TestGraph::test_traverse_mapdatapipe, test/test_datapipe.py::TestGraph::test_traverse_mixdatapipe, test/test_datapipe.py::TestGraph::test_traverse_unhashable_datapipe, test/test_datapipe.py::TestSerialization::test_spawn_lambdas_iter, test/test_datapipe.py::TestSerialization::test_spawn_lambdas_map, test/test_datapipe.py::TestCircularSerialization::test_circular_serialization_with_dill, test/test_datapipe.py::TestCircularSerialization::test_circular_serialization_with_pickle, test/test_datapipe.py::TestSharding::test_legacy_custom_sharding, test/test_datapipe.py::TestSharding::test_legacy_custom_sharding_with_old_dataloader, test/test_datapipe.py::TestSharding::test_multi_sharding, test/test_datapipe.py::TestSharding::test_old_dataloader, test/test_datapipe.py::TestSharding::test_sharding_groups, test/test_datapipe.py::TestSharding::test_sharding_groups_in_legacy_grouping_package, test/test_datapipe.py::TestSharding::test_sharding_length, test/test_datapipe.py::TestSharding::test_simple_sharding, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_buggy, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_constraint_multiple_outputs, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_generator, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_new_object, test/test_datapipe.py::TestIterDataPipeSingletonConstraint::test_iterdatapipe_singleton_self_next, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_generator_function, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_generator_function_exception, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_next, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_next_exception, test/test_datapipe.py::TestIterDataPipeCountSampleYielded::test_iterdatapipe_sample_yielded_return_self, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_custom_non_generator, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_custom_self_next, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_graph, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_graph_repeated, test/test_datapipe.py::TestIterDataPipeGraphFastForward::test_simple_snapshot_graph_with_serialization 2025-07-17T08:49:41.2943111Z 2025-07-17T08:49:43.7485490Z 2025-07-17T08:49:43.7486485Z dynamo/test_flat_apply 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_flat_apply_1.1_015ceccfde42ee2a_.log 2025-07-17T08:49:43.7488792Z Running 4 items in this shard: test/dynamo/test_flat_apply.py::FlatApplyTests::test_non_tensor_output, test/dynamo/test_flat_apply.py::FlatApplyTests::test_nonstrict_trace_captured_tensor_post_aot_graph, test/dynamo/test_flat_apply.py::FlatApplyTests::test_nonstrict_trace_dynamo_graph, test/dynamo/test_flat_apply.py::FlatApplyTests::test_simple 2025-07-17T08:49:43.7490327Z 2025-07-17T08:49:44.3764773Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:44.3766372Z import pkg_resources 2025-07-17T08:49:44.5021130Z Running torch_np/test_indexing 1/1 ... [2025-07-17 08:49:44.501798] 2025-07-17T08:49:44.5021601Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:44.5024195Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_indexing.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'] ... [2025-07-17 08:49:44.502188] 2025-07-17T08:49:47.1283906Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:47.1286760Z import pkg_resources 2025-07-17T08:49:47.2565208Z Running dynamo/test_deviceguard 1/1 ... [2025-07-17 08:49:47.256042] 2025-07-17T08:49:47.2566124Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:47.2568603Z 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'] ... [2025-07-17 08:49:47.256409] 2025-07-17T08:49:50.3755773Z 2025-07-17T08:49:50.3756714Z torch_np/test_indexing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_indexing_1.1_b194784d0e5ab46e_.log 2025-07-17T08:49:50.3759281Z Running 5 items in this shard: test/torch_np/test_indexing.py::TestAdvancedIndexing::test_advanced_separation_patterns, test/torch_np/test_indexing.py::TestAdvancedIndexing::test_broadcast_and_numpy_compatibility, test/torch_np/test_indexing.py::TestAdvancedIndexing::test_comprehensive_indexing, test/torch_np/test_indexing.py::TestAdvancedIndexing::test_ellipsis, test/torch_np/test_indexing.py::TestAdvancedIndexing::test_special_index_types 2025-07-17T08:49:50.3761482Z 2025-07-17T08:49:51.4271952Z 2025-07-17T08:49:51.4273005Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_bbe32bb7ad7c639c_.log 2025-07-17T08:49:51.4275127Z Running 4 items in this shard: test/dynamo/test_deviceguard.py::TestDeviceGuard::test_device_guard, test/dynamo/test_deviceguard.py::TestDeviceGuard::test_device_guard_no_index, test/dynamo/test_deviceguard.py::TestCUDADeviceGuard::test_device_guard, test/dynamo/test_deviceguard.py::TestCUDADeviceGuard::test_device_guard_no_index 2025-07-17T08:49:51.4276595Z 2025-07-17T08:49:53.6249704Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:53.6251563Z import pkg_resources 2025-07-17T08:49:53.7492921Z Running test_content_store 1/1 ... [2025-07-17 08:49:53.748986] 2025-07-17T08:49:53.7493423Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:53.7495612Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_content_store.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'] ... [2025-07-17 08:49:53.749312] 2025-07-17T08:49:54.5482600Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:49:54.5484195Z import pkg_resources 2025-07-17T08:49:54.6728025Z Running export/test_unflatten 1/1 ... [2025-07-17 08:49:54.672449] 2025-07-17T08:49:54.6728647Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:49:54.6730430Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'export/test_unflatten.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:49:54.672791] 2025-07-17T08:50:01.8753896Z 2025-07-17T08:50:01.8754800Z test_content_store 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_content_store_1.1_ba7f4c5932efb893_.log 2025-07-17T08:50:01.8756685Z Running 4 items in this shard: test/test_content_store.py::TestContentStoreCPU::test_basic_cpu, test/test_content_store.py::TestContentStoreCPU::test_load_tensor_cpu, test/test_content_store.py::TestContentStoreCPU::test_repeated_hash_cpu, test/test_content_store.py::TestContentStoreCPU::test_scalar_cpu 2025-07-17T08:50:01.8758034Z 2025-07-17T08:50:05.0535164Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:50:05.0536835Z import pkg_resources 2025-07-17T08:50:05.1782276Z Running lazy/test_step_closures 1/1 ... [2025-07-17 08:50:05.177936] 2025-07-17T08:50:05.1782746Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:50:05.1784939Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_step_closures.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:50:05.178265] 2025-07-17T08:50:10.5000610Z 2025-07-17T08:50:10.5002035Z lazy/test_step_closures 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_step_closures_1.1_8ab27a57ad8e8580_.log 2025-07-17T08:50:10.5006033Z Running 4 items in this shard: test/lazy/test_step_closures.py::ClosuresTest::test_asynchronous, test/lazy/test_step_closures.py::ClosuresTest::test_asynchronous_exception, test/lazy/test_step_closures.py::ClosuresTest::test_synchronous, test/lazy/test_step_closures.py::ClosuresTest::test_synchronous_exception 2025-07-17T08:50:10.5008614Z 2025-07-17T08:50:13.8396207Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:50:13.8397765Z import pkg_resources 2025-07-17T08:50:13.9664112Z Running inductor/test_fused_attention 1/1 ... [2025-07-17 08:50:13.966080] 2025-07-17T08:50:13.9664910Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:50:13.9666779Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_fused_attention.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'] ... [2025-07-17 08:50:13.966430] 2025-07-17T08:50:15.8196162Z 2025-07-17T08:50:15.8197630Z export/test_unflatten 1/1 was successful, full logs can be found in artifacts with path test/test-reports/export.test_unflatten_1.1_265ce844b5f46fe6_.log 2025-07-17T08:50:15.8215861Z Running 27 items in this shard: test/export/test_unflatten.py::TestUnflatten::test_attr_as_submod_input, test/export/test_unflatten.py::TestUnflatten::test_dedup_sym_size, test/export/test_unflatten.py::TestUnflatten::test_double_nested_submodule, test/export/test_unflatten.py::TestUnflatten::test_duplicate_placeholder, test/export/test_unflatten.py::TestUnflatten::test_fx_trace, test/export/test_unflatten.py::TestUnflatten::test_nested_leaf_non_strict, test/export/test_unflatten.py::TestUnflatten::test_placeholder_and_get_attr_ordering_after_unflattened, test/export/test_unflatten.py::TestUnflatten::test_simple_alias, test/export/test_unflatten.py::TestUnflatten::test_unflatten_buffer_mutation, test/export/test_unflatten.py::TestUnflatten::test_unflatten_constant_obj, test/export/test_unflatten.py::TestUnflatten::test_unflatten_constant_tensor, test/export/test_unflatten.py::TestUnflatten::test_unflatten_container_type, test/export/test_unflatten.py::TestUnflatten::test_unflatten_eager, test/export/test_unflatten.py::TestUnflatten::test_unflatten_empty_branch, test/export/test_unflatten.py::TestUnflatten::test_unflatten_nested, test/export/test_unflatten.py::TestUnflatten::test_unflatten_nested_access, test/export/test_unflatten.py::TestUnflatten::test_unflatten_none, test/export/test_unflatten.py::TestUnflatten::test_unflatten_param_list_dict, test/export/test_unflatten.py::TestUnflatten::test_unflatten_preserve_signature, test/export/test_unflatten.py::TestUnflatten::test_unflatten_preserve_with_unused_input, test/export/test_unflatten.py::TestUnflatten::test_unflatten_requires_grad_param, test/export/test_unflatten.py::TestUnflatten::test_unflatten_shared_submodule, test/export/test_unflatten.py::TestUnflatten::test_unflatten_skipped_call_module, test/export/test_unflatten.py::TestUnflatten::test_unflatten_submodule_ordering, test/export/test_unflatten.py::TestUnflatten::test_unflatten_with_inplace_compile, test/export/test_unflatten.py::TestUnflatten::test_unflatten_wrong_input, test/export/test_unflatten.py::TestUnflatten::test_unflattened_module_nodes_has_meta_val 2025-07-17T08:50:15.8232959Z 2025-07-17T08:50:19.2074025Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:50:19.2077215Z import pkg_resources 2025-07-17T08:50:19.3329311Z Running test_ops_gradients 1/2 ... [2025-07-17 08:50:19.332593] 2025-07-17T08:50:19.3330156Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:50:19.3332172Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ops_gradients.py', '-m', 'not serial', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:50:19.332930] 2025-07-17T08:50:28.8659709Z 2025-07-17T08:50:28.8661024Z inductor/test_cpu_select_algorithm 16/16 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_cpu_select_algorithm_16.16_fef683961e21f6de_.log 2025-07-17T08:50:28.8741596Z Running 99 items in this shard: test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_2d_permute_Ndim_61_order0_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_16_Kdim_32_Ndim_3_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_1024_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_1025_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_amx_bs_2_Mdim_32_Kdim_32_Ndim_48_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_freezing_bs_1_Mdim_192_Kdim_196_Ndim_65_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_bmm_freezing_bs_1_Mdim_192_Kdim_196_Ndim_65_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_bfloat16_per_channel_quant_True_reshape_a_True_expand_a_scale_False_dynamic_False_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_False_float32_per_channel_quant_False_reshape_a_False_expand_a_scale_False_dynamic_False_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_False_reshape_a_False_expand_a_scale_True_dynamic_False_M_1_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_bfloat16_per_channel_quant_True_reshape_a_True_expand_a_scale_False_dynamic_True_M_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_False_reshape_a_False_expand_a_scale_False_dynamic_False_M_1_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_False_reshape_a_True_expand_a_scale_True_dynamic_True_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_da8w8_sym_act_sym_wgt_with_int_mm_has_bias_True_float32_per_channel_quant_True_reshape_a_False_expand_a_scale_False_dynamic_False_M_32_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_grouped_linear_epilogue_batch_size_16_in_features_52_out_features_32_input_3d_False_bias0_epilogue1_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_grouped_linear_invalid_batch_size_16_in_features_52_out_features_32_gemm_num_2_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int4_woq_mm_amx_batch_size_1_in_features_128_out_features_128_group_size_128_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int4_woq_mm_amx_batch_size_4_in_features_1024_out_features_128_group_size_32_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int4_woq_mm_avx512_batch_size_1_in_features_128_out_features_64_group_size_64_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_17_mid_dim_1_in_features_128_out_features_1024_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_17_mid_dim_8_in_features_144_out_features_64_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_1_mid_dim_8_in_features_1024_out_features_65_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_int8_woq_mm_batch_size_32_mid_dim_8_in_features_144_out_features_65_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_amx_batch_size_1024_in_features_1024_out_features_1025_bias_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_cache_blocking_batch_size_512_in_features_1024_out_features_1024_bias_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_k_slicing_batch_size_2_in_features_1000_out_features_2_bias_False_epilogue_relu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1000_out_features_1024_bias_False_input_3d_True_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1000_in_features_1_out_features_1024_bias_True_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1000_out_features_1_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1000_out_features_1_bias_True_input_3d_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1_out_features_1024_bias_False_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1_out_features_1024_bias_True_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_1_in_features_1_out_features_1_bias_True_input_3d_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1000_out_features_1024_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1000_out_features_1_bias_False_input_3d_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1_out_features_1024_bias_False_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_static_shapes_batch_size_2_in_features_1_out_features_1_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_binary_input_3d_batch_size_384_in_features_196_out_features_384_bias_False_binary_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_embedding_batch_size_384_in_features_196_out_features_384_bias_False_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_sigmoid_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_tanh_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_gelu_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_silu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_hardswish_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_hardtanh_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_pointwise_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_hardsigmoid_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_mul_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_sub_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_transpose_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_linear_with_unary_binary_batch_size_384_in_features_196_out_features_384_bias_True_unary_relu_binary_div_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_32_in_features_128_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_3_in_features_128_out_features_64_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_amx_batch_size_3_in_features_4_out_features_65_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_False_epilogue_gelu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_int8_mixed_bf16_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmCPU::test_quantized_linear_with_pointwise_binary_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_True_int8_mixed_bf16_True_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_bmm_with_pointwise_with_reshape_dynamic_shapes_bs_5_Mdim_384_Kdim_96_Ndim_65_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_False_bias2_epilogue1_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_grouped_linear_epilogue_dynamic_shapes_batch_size_16_in_features_52_out_features_32_input_3d_False_bias3_epilogue1_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_cache_blocking_dynamic_shapes_batch_size_512_in_features_1024_out_features_1024_bias_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1000_out_features_1_bias_False_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_out_features_1024_bias_False_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1000_in_features_1_out_features_1_bias_False_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1000_out_features_1024_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1000_out_features_1024_bias_True_input_3d_False_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1_out_features_1024_bias_False_input_3d_True_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1_out_features_1_bias_False_input_3d_True_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_dynamic_shapes_batch_size_1_in_features_1_out_features_1_bias_True_input_3d_False_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_epilogue_leaky_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_mul_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_epilogue_sigmoid_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_hardtanh_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_False_epilogue_tanh_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_pointwise_dynamic_shapes_batch_size_384_in_features_196_out_features_385_bias_True_epilogue_div_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_128_bias_True_epilogue_mul_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_False_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_transpose_dynamic_shapes_batch_size_384_in_features_196_out_features_129_bias_True_epilogue_sub_cpu_float16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_add_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_False_unary_relu_binary_sub_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_unary_relu_binary_mul_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_linear_with_unary_binary_dynamic_shapes_batch_size_384_in_features_196_out_features_384_bias_True_unary_relu_binary_sub_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_16_in_features_4_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_3_in_features_128_out_features_65_bias_True_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_amx_dynamic_shapes_batch_size_3_in_features_4_out_features_64_bias_False_cpu, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_int8_mixed_bf16_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_int8_mixed_bf16_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_True_input_3d_True_int8_mixed_bf16_True_epilogue_relu_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_False_int8_mixed_bf16_False_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_int8_mixed_bf16_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_int8_mixed_bf16_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_int8_mixed_bf16_False_epilogue_none_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_binary_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_int8_mixed_bf16_False_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_64_bias_False_input_3d_True_epilogue_relu_cpu_float32, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_False_input_3d_True_epilogue_none_cpu_bfloat16, test/inductor/test_cpu_select_algorithm.py::TestSelectAlgorithmDynamicShapesCPU::test_quantized_linear_with_pointwise_dynamic_shapes_batch_size_32_in_features_128_out_features_65_bias_True_input_3d_False_epilogue_none_cpu_bfloat16 2025-07-17T08:50:28.8820973Z 2025-07-17T08:50:29.8536434Z Uploading artifacts took 0.99 seconds 2025-07-17T08:50:32.5428307Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:50:32.5431152Z import pkg_resources 2025-07-17T08:50:32.6665807Z Running inductor/test_padding 1/1 ... [2025-07-17 08:50:32.666243] 2025-07-17T08:50:32.6666939Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:50:32.6669314Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'inductor/test_padding.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'] ... [2025-07-17 08:50:32.666616] 2025-07-17T08:50:39.5943080Z 2025-07-17T08:50:39.5943966Z inductor/test_padding 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_padding_1.1_51559056c49bee6d_.log 2025-07-17T08:50:39.5944662Z 2025-07-17T08:50:43.0270952Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:50:43.0273647Z import pkg_resources 2025-07-17T08:50:43.1528675Z Running dynamo/test_torchrec 1/1 ... [2025-07-17 08:50:43.152494] 2025-07-17T08:50:43.1529837Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:50:43.1532590Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_torchrec.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:50:43.152867] 2025-07-17T08:50:46.8630445Z 2025-07-17T08:50:46.8632079Z dynamo/test_torchrec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_torchrec_1.1_c987a4b4e03c7879_.log 2025-07-17T08:50:46.8713514Z Running 0 items in this shard: 2025-07-17T08:50:46.8713914Z 2025-07-17T08:50:50.3699992Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:50:50.3701640Z import pkg_resources 2025-07-17T08:50:50.4918588Z Running torch_np/test_reductions 1/1 ... [2025-07-17 08:50:50.491555] 2025-07-17T08:50:50.4919069Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:50:50.4921193Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'torch_np/test_reductions.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:50:50.491874] 2025-07-17T08:50:57.6674812Z 2025-07-17T08:50:57.6676271Z torch_np/test_reductions 1/1 was successful, full logs can be found in artifacts with path test/test-reports/torch_np.test_reductions_1.1_399f636f38a9dcb5_.log 2025-07-17T08:50:57.7207480Z Running 966 items in this shard: test/torch_np/test_reductions.py::TestFlatnonzero::test_basic, test/torch_np/test_reductions.py::TestAny::test_basic, test/torch_np/test_reductions.py::TestAny::test_method_vs_function, test/torch_np/test_reductions.py::TestAny::test_nd, test/torch_np/test_reductions.py::TestAll::test_basic, test/torch_np/test_reductions.py::TestAll::test_method_vs_function, test/torch_np/test_reductions.py::TestAll::test_nd, test/torch_np/test_reductions.py::TestMean::test_mean, test/torch_np/test_reductions.py::TestMean::test_mean_float16, test/torch_np/test_reductions.py::TestMean::test_mean_values, test/torch_np/test_reductions.py::TestMean::test_mean_where, test/torch_np/test_reductions.py::TestSum::test_sum, test/torch_np/test_reductions.py::TestSum::test_sum_boolean, test/torch_np/test_reductions.py::TestSum::test_sum_complex_1_dt0, test/torch_np/test_reductions.py::TestSum::test_sum_complex_1_dt1, test/torch_np/test_reductions.py::TestSum::test_sum_complex_2_dt0, test/torch_np/test_reductions.py::TestSum::test_sum_complex_2_dt1, test/torch_np/test_reductions.py::TestSum::test_sum_dtypes_2, test/torch_np/test_reductions.py::TestSum::test_sum_dtypes_warnings, test/torch_np/test_reductions.py::TestSum::test_sum_initial, test/torch_np/test_reductions.py::TestSum::test_sum_stability, test/torch_np/test_reductions.py::TestSum::test_sum_where, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_array_axis_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_bad_tuple_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_axis_empty_generic_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_bad_axis_func9, test/torch_np/test_reductions.py::TestGenericReductions::test_keepdims_generic_axis5_func0, 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test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func8_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_-1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_-2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_axis_keepdims_True_dtype_int32_func9_axis_2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func0, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func1, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func10, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func11, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func2, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func3, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func4, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func5, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func6, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func7, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func8, test/torch_np/test_reductions.py::TestGenericReductions::test_out_scalar_func9, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_array_axis_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_array_axis_func1, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_bad_tuple_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_bad_tuple_func1, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_empty_generic_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_axis_empty_generic_func1, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_bad_axis_func0, test/torch_np/test_reductions.py::TestGenericCumSumProd::test_bad_axis_func1 2025-07-17T08:50:57.7625716Z 2025-07-17T08:51:01.0451383Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:51:01.0454581Z import pkg_resources 2025-07-17T08:51:01.1690298Z Running profiler/test_profiler_tree 1/1 ... [2025-07-17 08:51:01.168657] 2025-07-17T08:51:01.1690877Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:51:01.1692410Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_profiler_tree.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'] ... [2025-07-17 08:51:01.168980] 2025-07-17T08:51:04.9385008Z 2025-07-17T08:51:04.9386263Z profiler/test_profiler_tree 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_profiler_tree_1.1_b007f66bd362f6f1_.log 2025-07-17T08:51:04.9391696Z Running 10 items in this shard: test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda_detailed, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_cuda_with_stream, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_memory, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_memory_and_stack, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_record_function, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_modules, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_torch_dispatch, test/profiler/test_profiler_tree.py::TestProfilerTree::test_profiler_experimental_tree_with_stack_and_torch_function 2025-07-17T08:51:04.9396703Z 2025-07-17T08:51:08.8608466Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:51:08.8611445Z import pkg_resources 2025-07-17T08:51:09.0421251Z Running test_type_promotion 1/1 ... [2025-07-17 08:51:09.041745] 2025-07-17T08:51:09.0422127Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:51:09.0425297Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_type_promotion.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'] ... [2025-07-17 08:51:09.042193] 2025-07-17T08:51:16.4176849Z 2025-07-17T08:51:16.4178758Z test_type_promotion 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_type_promotion_1.1_abfbb14cc2344323_.log 2025-07-17T08:51:16.4533368Z Running 423 items in this shard: test/test_type_promotion.py::TestTypePromotionCPU::test_add_wrapped_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_alpha_mismatch_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_alternate_result_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_bfloat16_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_booleans_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_can_cast_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_cat_different_dtypes_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_cat_out_different_dtypes_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_bool_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float32_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_float64_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_float64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_clamp_type_promotion_cpu_int32_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_comparison_ops_with_type_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_assertraises_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_half_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_complex_scalar_mult_tensor_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_computation_ignores_out_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_create_bool_tensors_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_inplace_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_div_promotion_out_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_float_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_from_issue_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_half_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_indexing_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_indexing_fail_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_inplace_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_int_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_int_to_float_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_integer_addcdiv_deprecated_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_lt_with_type_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_many_promotions_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_mixed_type_backward_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_non_promoting_ops_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_bool_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex128_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_numpy_array_binary_ufunc_promotion_cpu_complex64_complex64, 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test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int16_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int32_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int64_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_int8_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_bfloat16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_float16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_int8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_cpu_uint8_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_result_type_tensor_vs_scalar_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_add_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_bool, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_int16, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_int32, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_div_promotion_cpu_uint8, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_mul_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_sparse_sub_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_ternary_out_promotion_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_transpose_cpu, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex128_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_complex64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float32_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_float64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_complex128, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_complex64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_float32, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_float64, test/test_type_promotion.py::TestTypePromotionCPU::test_unary_op_out_casting_cpu_int64_int64, test/test_type_promotion.py::TestTypePromotionCPU::test_unsigned_cpu 2025-07-17T08:51:16.4868794Z 2025-07-17T08:51:19.6376241Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:51:19.6379254Z import pkg_resources 2025-07-17T08:51:19.7616374Z Running dynamo/test_python_autograd 1/1 ... [2025-07-17 08:51:19.761233] 2025-07-17T08:51:19.7617287Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:51:19.7619624Z 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'] ... [2025-07-17 08:51:19.761590] 2025-07-17T08:51:24.5828105Z 2025-07-17T08:51:24.5829685Z 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_1cfc287e10e58d9b_.log 2025-07-17T08:51:24.5834632Z Running 5 items in this shard: test/dynamo/test_python_autograd.py::TestPythonAutograd::test_backwards1, test/dynamo/test_python_autograd.py::TestPythonAutograd::test_backwards2, test/dynamo/test_python_autograd.py::TestPythonAutograd::test_forwards1, test/dynamo/test_python_autograd.py::TestPythonAutograd::test_forwards2, test/dynamo/test_python_autograd.py::TestPythonAutograd::test_split 2025-07-17T08:51:24.5838208Z 2025-07-17T08:51:27.9491642Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:51:27.9495048Z import pkg_resources 2025-07-17T08:51:28.0734679Z Running test_jit_autocast 1/1 ... [2025-07-17 08:51:28.073092] 2025-07-17T08:51:28.0735709Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:51:28.0738485Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_autocast.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'] ... [2025-07-17 08:51:28.073447] 2025-07-17T08:51:43.4137696Z 2025-07-17T08:51:43.4139015Z test_jit_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_autocast_1.1_732f82b3273f4ba5_.log 2025-07-17T08:51:43.4162215Z Running 54 items in this shard: test/test_jit_autocast.py::TestAutocast::test_autocast_api, test/test_jit_autocast.py::TestAutocast::test_autocast_api_not_supported, test/test_jit_autocast.py::TestAutocast::test_autocast_autodiff, test/test_jit_autocast.py::TestAutocast::test_autocast_decorator, test/test_jit_autocast.py::TestAutocast::test_autocast_decorator_outside_jit, test/test_jit_autocast.py::TestAutocast::test_autocast_mixed_dtypes, test/test_jit_autocast.py::TestAutocast::test_callees, test/test_jit_autocast.py::TestAutocast::test_callees_with_autocast_off, test/test_jit_autocast.py::TestAutocast::test_callees_with_autocast_on, test/test_jit_autocast.py::TestAutocast::test_conditional_autocast, test/test_jit_autocast.py::TestAutocast::test_control_flow, test/test_jit_autocast.py::TestAutocast::test_divergent_autocast, test/test_jit_autocast.py::TestAutocast::test_divergent_types, test/test_jit_autocast.py::TestAutocast::test_duplicate_inputs, test/test_jit_autocast.py::TestAutocast::test_eager_and_script, test/test_jit_autocast.py::TestAutocast::test_explicit_casts, test/test_jit_autocast.py::TestAutocast::test_fp32_policy, test/test_jit_autocast.py::TestAutocast::test_fp32_policy_with_fp64, test/test_jit_autocast.py::TestAutocast::test_fp32_set_opt_dtype_policy, test/test_jit_autocast.py::TestAutocast::test_fp32_set_opt_dtype_policy_fp64, test/test_jit_autocast.py::TestAutocast::test_ignore_amp, test/test_jit_autocast.py::TestAutocast::test_implicitly_nested_autocast, test/test_jit_autocast.py::TestAutocast::test_inplace, test/test_jit_autocast.py::TestAutocast::test_jit_autocast_softmax_cpu, test/test_jit_autocast.py::TestAutocast::test_jit_autocast_softmax_gpu, test/test_jit_autocast.py::TestAutocast::test_jit_call_method_under_autocast, test/test_jit_autocast.py::TestAutocast::test_jit_executor_under_autocast, test/test_jit_autocast.py::TestAutocast::test_jit_freeze_autocast_basic, test/test_jit_autocast.py::TestAutocast::test_jit_freeze_autocast_constants, test/test_jit_autocast.py::TestAutocast::test_jit_generic_autocast, test/test_jit_autocast.py::TestAutocast::test_linear_bf16, test/test_jit_autocast.py::TestAutocast::test_minimal, test/test_jit_autocast.py::TestAutocast::test_minimal_cpu, test/test_jit_autocast.py::TestAutocast::test_minimal_off, test/test_jit_autocast.py::TestAutocast::test_nested_autocast, test/test_jit_autocast.py::TestAutocast::test_promote_policy, test/test_jit_autocast.py::TestAutocast::test_promote_policy_fp64, test/test_jit_autocast.py::TestAutocast::test_reused_autocast, test/test_jit_autocast.py::TestAutocast::test_reused_autocast_expr, test/test_jit_autocast.py::TestAutocast::test_runtime_autocast_state, test/test_jit_autocast.py::TestAutocast::test_runtime_autocast_state_expr, test/test_jit_autocast.py::TestAutocast::test_script_and_tracing, test/test_jit_autocast.py::TestAutocast::test_script_and_tracing_with_autocast, test/test_jit_autocast.py::TestAutocast::test_script_module, test/test_jit_autocast.py::TestAutocast::test_tracing_and_script, test/test_jit_autocast.py::TestAutocast::test_tracing_with_autocast_and_script, test/test_jit_autocast.py::TestJitTraceAutocast::test_cat_promote, test/test_jit_autocast.py::TestJitTraceAutocast::test_generate_autocast_jit_trace_model, test/test_jit_autocast.py::TestJitTraceAutocast::test_nchw_autocast_jit_trace_model, test/test_jit_autocast.py::TestJitTraceAutocast::test_nhwc_autocast_jit_trace_model, test/test_jit_autocast.py::TestJitTraceAutocast::test_script_autocast_cpu, test/test_jit_autocast.py::TestJitTraceAutocast::test_script_autocast_cuda, test/test_jit_autocast.py::TestJitTraceAutocast::test_script_autocast_enable_and_check, test/test_jit_autocast.py::TestJitTraceAutocast::test_scripted_aliasing 2025-07-17T08:51:43.4178151Z 2025-07-17T08:51:46.5979796Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:51:46.5981352Z import pkg_resources 2025-07-17T08:51:46.7237513Z Running profiler/test_record_function 1/1 ... [2025-07-17 08:51:46.723418] 2025-07-17T08:51:46.7238067Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:51:46.7240962Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_record_function.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:51:46.723768] 2025-07-17T08:51:50.3433494Z 2025-07-17T08:51:50.3434744Z profiler/test_record_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_record_function_1.1_cba6b47811a187f7_.log 2025-07-17T08:51:50.3438757Z Running 4 items in this shard: test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_delegation_with_profiler, test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_with_record_function, test/profiler/test_record_function.py::TestRecordFunction::test_datapipe_with_record_function_fork, test/profiler/test_record_function.py::TestRecordFunction::test_record_function 2025-07-17T08:51:50.3441648Z 2025-07-17T08:51:53.9906123Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:51:53.9909486Z import pkg_resources 2025-07-17T08:51:54.1549286Z Running test_prims 1/1 ... [2025-07-17 08:51:54.154554] 2025-07-17T08:51:54.1550068Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:51:54.1553747Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_prims.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'] ... [2025-07-17 08:51:54.155003] 2025-07-17T08:51:59.1270532Z 2025-07-17T08:51:59.1271883Z test_prims 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_prims_1.1_38885a18e7135583_.log 2025-07-17T08:51:59.1286624Z Running 24 items in this shard: test/test_prims.py::TestPrimsBasic::test_check_deprecation_warning, test/test_prims.py::TestPrimsBasic::test_clone_complex, test/test_prims.py::TestPrimsBasic::test_mul_complex, test/test_prims.py::TestPrimsBasic::test_torch_ops, test/test_prims.py::TestPrimsCPU::test_aten_overload_to_prims_cpu, test/test_prims.py::TestPrimsCPU::test_broadcast_in_dim_cpu_float32, test/test_prims.py::TestPrimsCPU::test_broadcast_in_dim_sum_cpu_float32, test/test_prims.py::TestPrimsCPU::test_cbrt_prim_cpu_float64, test/test_prims.py::TestPrimsCPU::test_cbrt_prim_cpu_int64, test/test_prims.py::TestPrimsCPU::test_collapse_cpu_float32, test/test_prims.py::TestPrimsCPU::test_functional_rng_wrappers_cpu_float32, test/test_prims.py::TestPrimsCPU::test_memory_format_strides_cpu_float32, test/test_prims.py::TestPrimsCPU::test_philox_rand_cpu_float32, test/test_prims.py::TestPrimsCPU::test_reshape_view_method_cpu_float32, test/test_prims.py::TestPrimsCPU::test_var_correction_0_cpu_float32, test/test_prims.py::TestPrimsCPU::test_var_correction_1_cpu_float32, test/test_prims.py::TestRefsCPU::test_constant_pad_nd_memory_format_cpu_float32, test/test_prims.py::TestRefsCPU::test_inferred_tags_cpu, test/test_prims.py::TestRefsCPU::test_infinite_loop_from_py_dispatcher_cpu, test/test_prims.py::TestRefsCPU::test_linspace_with_complex_input_cpu, test/test_prims.py::TestRefsCPU::test_logspace_with_complex_input_cpu, test/test_prims.py::TestRefsCPU::test_unbind_cpu, test/test_prims.py::TestDecompCPU::test_decomposition_method_vararg_ones_cpu_float32, test/test_prims.py::TestDecompCPU::test_decomposition_method_vararg_permute_cpu_float32 2025-07-17T08:51:59.1300643Z 2025-07-17T08:52:03.0892317Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:52:03.0893922Z import pkg_resources 2025-07-17T08:52:03.2174879Z Running lazy/test_reuse_ir 1/1 ... [2025-07-17 08:52:03.217148] 2025-07-17T08:52:03.2175414Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:52:03.2178120Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'lazy/test_reuse_ir.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'] ... [2025-07-17 08:52:03.217571] 2025-07-17T08:52:07.1380322Z 2025-07-17T08:52:07.1381729Z lazy/test_reuse_ir 1/1 was successful, full logs can be found in artifacts with path test/test-reports/lazy.test_reuse_ir_1.1_614bb6151f1a9136_.log 2025-07-17T08:52:07.1385147Z Running 4 items in this shard: test/lazy/test_reuse_ir.py::TestLazyReuseIr::testAdd, test/lazy/test_reuse_ir.py::TestLazyReuseIr::testAddSub, test/lazy/test_reuse_ir.py::TestLazyReuseIr::testAddSubFallback, test/lazy/test_reuse_ir.py::TestLazyReuseIr::testBatchNorm 2025-07-17T08:52:07.1387769Z 2025-07-17T08:52:10.6465146Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:52:10.6468226Z import pkg_resources 2025-07-17T08:52:10.7705151Z Running profiler/test_execution_trace 1/1 ... [2025-07-17 08:52:10.770176] 2025-07-17T08:52:10.7706096Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:52:10.7709015Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_execution_trace.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-07-17 08:52:10.770542] 2025-07-17T08:52:14.6408293Z 2025-07-17T08:52:14.6409461Z profiler/test_execution_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_execution_trace_1.1_26e8a4f4e944f685_.log 2025-07-17T08:52:14.6415985Z Running 12 items in this shard: test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_alone_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_env_disabled_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_env_enabled_with_kineto_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_env_enabled_with_pt2_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_nested_tensor_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_no_capture_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_record_integral_tensor_data_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_record_integral_tensor_range_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_repeat_in_loop_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_start_stop_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_with_kineto_cpu, test/profiler/test_execution_trace.py::TestExecutionTraceCPU::test_execution_trace_with_pt2_cpu 2025-07-17T08:52:14.6421807Z 2025-07-17T08:52:18.1040985Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:52:18.1044044Z import pkg_resources 2025-07-17T08:52:18.2268612Z Running profiler/test_torch_tidy 1/1 ... [2025-07-17 08:52:18.226482] 2025-07-17T08:52:18.2269544Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:52:18.2272143Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'profiler/test_torch_tidy.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'] ... [2025-07-17 08:52:18.226844] 2025-07-17T08:52:27.9074282Z 2025-07-17T08:52:27.9075922Z profiler/test_torch_tidy 1/1 was successful, full logs can be found in artifacts with path test/test-reports/profiler.test_torch_tidy_1.1_cfa97be53beff433_.log 2025-07-17T08:52:27.9094013Z Running 22 items in this shard: test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_allocation_id_uniqueness, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_allocation_ids, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_allocation_ids_with_other_ops, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_allocations, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_extra_fields, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_impl_reuse, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_mkldnn_tensors, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_module_and_optimizer_ids, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_nnmodule_params, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_optimizer, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_optimizer_parameters_adam, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_optimizer_parameters_sgd, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_pointers_and_ids, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_refcounts, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_scalar_ins, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_sparse_tensors, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensor_lists, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensor_properties, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensorimpl_invalidation_full, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensorimpl_invalidation_keep_alive, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensorimpl_invalidation_scalar_args, test/profiler/test_torch_tidy.py::TestTorchTidyProfiler::test_tensorimpl_invalidation_set 2025-07-17T08:52:27.9111200Z 2025-07-17T08:52:31.3135403Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:52:31.3138443Z import pkg_resources 2025-07-17T08:52:31.4988536Z Running test_rename_privateuse1_to_existing_device 1/1 ... [2025-07-17 08:52:31.498506] 2025-07-17T08:52:31.4989902Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:52:31.4993401Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_rename_privateuse1_to_existing_device.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'] ... [2025-07-17 08:52:31.498967] 2025-07-17T08:52:32.5977438Z 2025-07-17T08:52:32.5978541Z inductor/test_fused_attention 1/1 was successful, full logs can be found in artifacts with path test/test-reports/inductor.test_fused_attention_1.1_2dc55e20e528441d_.log 2025-07-17T08:52:32.6001433Z Running 48 items in this shard: test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_pattern_fails_with_reuse_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_pattern_fails_with_tensor_factor_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_pattern_fails_with_unsupported_mask_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_prev_13_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_prev_14_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_prev_15_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_11_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_12_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_13_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_14_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_15_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_16_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_16_fp32_mask_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_17_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_18_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_19_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_1_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_20_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_21_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_22_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_23_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_24_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_2_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuTests::test_sdpa_rewriter_5_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_pattern_fails_with_reuse_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_pattern_fails_with_tensor_factor_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_pattern_fails_with_unsupported_mask_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_prev_13_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_prev_14_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_prev_15_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_11_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_12_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_13_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_14_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_15_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_16_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_16_fp32_mask_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_17_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_18_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_19_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_1_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_20_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_21_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_22_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_23_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_24_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_2_cpu, test/inductor/test_fused_attention.py::SDPAPatternRewriterCpuDynamicTests::test_sdpa_rewriter_5_cpu 2025-07-17T08:52:32.6023550Z 2025-07-17T08:52:35.1177868Z 2025-07-17T08:52:35.1179760Z test_rename_privateuse1_to_existing_device 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_rename_privateuse1_to_existing_device_1.1_b8668c84b100d097_.log 2025-07-17T08:52:35.1182942Z Running 1 items in this shard: test/test_rename_privateuse1_to_existing_device.py::TestRenamePrivateuseoneToExistingBackend::test_external_module_register_with_existing_backend 2025-07-17T08:52:35.1184366Z 2025-07-17T08:52:35.7702525Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:52:35.7706401Z import pkg_resources 2025-07-17T08:52:39.0188394Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:52:39.0191287Z import pkg_resources 2025-07-17T08:57:00.7602544Z 2025-07-17T08:57:00.7603528Z test_ops_gradients 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_ops_gradients_1.2_d52c0df0a72daed1_.log 2025-07-17T08:57:00.8708619Z Running 2723 items in this shard: test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_H_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyCubeCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyMulScalarCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyNMSCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyNonzeroCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpySortCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpySplitCopyWithIntCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyTakeCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_NumpyViewCopyCustomOp_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_T_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___getitem___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___radd___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rdiv___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rmatmul___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rmod___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rmul___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rpow___cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rpow___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad___rsub___cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__chunk_cat_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__chunk_cat_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__native_batch_norm_legit_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__segment_reduce_lengths_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__softmax_backward_data_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad__upsample_bilinear2d_aa_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_abs_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_acos_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_acosh_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_add_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addbmm_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmm_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmv_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addmv_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_addr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_all_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_fn_fail_gradgrad_all_cpu_float64, 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test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_modified_bessel_k1_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_ndtr_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_ndtri_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_scaled_modified_bessel_k0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_u_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_shifted_chebyshev_polynomial_v_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_spherical_bessel_j0_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_xlog1py_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_special_zeta_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_list_args_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_split_with_sizes_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sqrt_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sqrt_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_square_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_squeeze_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_unbiased_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_mean_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_std_unbiased_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_sub_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_svd_lowrank_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_t_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tan_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tanh_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tensor_split_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_sparse_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_to_sparse_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_copy_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_transpose_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_triangular_solve_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tril_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_tril_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_true_divide_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unbind_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unflatten_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unflatten_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unfold_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsafe_chunk_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsafe_chunk_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsafe_split_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsafe_split_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_unsqueeze_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_var_mean_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vdot_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vdot_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_complex_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_as_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_copy_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_view_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vsplit_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_vstack_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_where_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_where_cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zero__cpu_float64, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_cpu_complex128, test/test_ops_gradients.py::TestBwdGradientsCPU::test_inplace_gradgrad_zeros_like_cpu_complex128 2025-07-17T08:57:00.9786056Z 2025-07-17T08:57:01.5306150Z Running test batch 'tests to run' cost 6570.73 seconds 2025-07-17T08:57:02.5328948Z 2025-07-17T08:57:02.5329425Z real 109m35.587s 2025-07-17T08:57:02.5329717Z user 548m30.688s 2025-07-17T08:57:02.5329936Z sys 43m42.333s 2025-07-17T08:57:02.5330175Z + assert_git_not_dirty 2025-07-17T08:57:02.5330474Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-07-17T08:57:02.5331106Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-07-17T08:57:02.5335699Z ++ git status --porcelain 2025-07-17T08:57:02.5336080Z ++ grep -v '?? third_party' 2025-07-17T08:57:34.4008333Z ++ true 2025-07-17T08:57:34.4008909Z + git_status= 2025-07-17T08:57:34.4009215Z + [[ -n '' ]] 2025-07-17T08:57:34.4009563Z + test_aten 2025-07-17T08:57:34.4010476Z + echo 'Running ATen tests with pytorch lib' 2025-07-17T08:57:34.4010914Z Running ATen tests with pytorch lib 2025-07-17T08:57:34.4011259Z + [[ -n '' ]] 2025-07-17T08:57:34.4011521Z + echo 'Running test with the build folder' 2025-07-17T08:57:34.4011876Z Running test with the build folder 2025-07-17T08:57:34.4012175Z + TEST_BASE_DIR=build/bin 2025-07-17T08:57:34.4012656Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so build/bin 2025-07-17T08:57:34.4035639Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2*' build/bin 2025-07-17T08:57:34.4046134Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libmkldnn*' build/bin 2025-07-17T08:57:34.4056883Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnccl*' build/bin 2025-07-17T08:57:34.4070093Z + 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 2025-07-17T08:57:34.4079355Z + ls build/bin 2025-07-17T08:57:34.4120206Z BackoffTest cpu_allocator_test 2025-07-17T08:57:34.4120797Z CMakeFiles cpu_generator_test 2025-07-17T08:57:34.4121370Z CTestTestfile.cmake cpu_profiling_allocator_test 2025-07-17T08:57:34.4121857Z CppSignature_test cpu_rng_test 2025-07-17T08:57:34.4122206Z Dict_test dlconvertor_test 2025-07-17T08:57:34.4122549Z Dimname_test example_allreduce 2025-07-17T08:57:34.4122953Z FileStoreTest extension_backend_test 2025-07-17T08:57:34.4123726Z HashStoreTest half_test 2025-07-17T08:57:34.4124270Z IListRef_test inline_container_test 2025-07-17T08:57:34.4124862Z KernelFunction_test ivalue_test 2025-07-17T08:57:34.4125546Z List_test kernel_function_legacy_test 2025-07-17T08:57:34.4125936Z MaybeOwned_test kernel_function_test 2025-07-17T08:57:34.4126318Z NamedTensor_test kernel_lambda_legacy_test 2025-07-17T08:57:34.4126735Z ProcessGroupGlooTest kernel_lambda_test 2025-07-17T08:57:34.4127146Z StorageUtils_test kernel_stackbased_test 2025-07-17T08:57:34.4127521Z TCPStoreTest lazy_tensor_test 2025-07-17T08:57:34.4127867Z aot_model_compiler_test legacy_vmap_test 2025-07-17T08:57:34.4128248Z apply_utils_test libc10.so 2025-07-17T08:57:34.4128555Z atest 'libcaffe2*' 2025-07-17T08:57:34.4128853Z backend_fallback_test 'libmkldnn*' 2025-07-17T08:57:34.4129180Z basic 'libnccl*' 2025-07-17T08:57:34.4129482Z broadcast_test libtorch.so 2025-07-17T08:57:34.4129813Z c10_ArrayRef_test libtorch_cpu.so 2025-07-17T08:57:34.4130163Z c10_Bitset_test libtorch_global_deps.so 2025-07-17T08:57:34.4130600Z c10_CompileTimeFunctionPointer_test libtorch_python.so 2025-07-17T08:57:34.4131047Z c10_ConstexprCrc_test libtorchbind_test.so 2025-07-17T08:57:34.4131529Z c10_DeadlockDetection_test make_boxed_from_unboxed_functor_test 2025-07-17T08:57:34.4132012Z c10_DeviceGuard_test math_kernel_test 2025-07-17T08:57:34.4132374Z c10_Device_test memory_format_test 2025-07-17T08:57:34.4132755Z c10_DispatchKeySet_test memory_overlapping_test 2025-07-17T08:57:34.4133174Z c10_Enumerate_test mobile_memory_cleanup 2025-07-17T08:57:34.4133535Z c10_Half_test native_test 2025-07-17T08:57:34.4133895Z c10_InlineDeviceGuard_test op_allowlist_test 2025-07-17T08:57:34.4134389Z c10_InlineStreamGuard_test op_registration_test 2025-07-17T08:57:34.4134815Z c10_IntrusiveList_test operator_name_test 2025-07-17T08:57:34.4135279Z c10_LeftRight_test operators_test 2025-07-17T08:57:34.4135690Z c10_Metaprogramming_test packedtensoraccessor_test 2025-07-17T08:57:34.4136183Z c10_NetworkFlow_test parallel_benchmark 2025-07-17T08:57:34.4136538Z c10_Scalar_test pow_test 2025-07-17T08:57:34.4136835Z c10_Semaphore_test protoc 2025-07-17T08:57:34.4137178Z c10_SizesAndStrides_test protoc-3.13.0.0 2025-07-17T08:57:34.4137551Z c10_StreamGuard_test quantized_test 2025-07-17T08:57:34.4137884Z c10_SymInt_test reduce_ops_test 2025-07-17T08:57:34.4138269Z c10_Synchronized_test reportMemoryUsage_test 2025-07-17T08:57:34.4138673Z c10_ThreadLocal_test scalar_tensor_test 2025-07-17T08:57:34.4139093Z c10_TypeIndex_test scalar_test 2025-07-17T08:57:34.4139477Z c10_TypeList_test static_runtime_bench 2025-07-17T08:57:34.4139852Z c10_TypeTraits_test static_runtime_test 2025-07-17T08:57:34.4140243Z c10_accumulate_test stride_properties_test 2025-07-17T08:57:34.4140619Z c10_bfloat16_test tensor_iterator_test 2025-07-17T08:57:34.4140969Z c10_bit_cast_test test_api 2025-07-17T08:57:34.4141294Z c10_complex_math_test test_cpp_rpc 2025-07-17T08:57:34.4141630Z c10_complex_test test_dist_autograd 2025-07-17T08:57:34.4141960Z c10_cow_test test_jit 2025-07-17T08:57:34.4142250Z c10_error_test test_lazy 2025-07-17T08:57:34.4142569Z c10_exception_test test_mobile_nnc 2025-07-17T08:57:34.4142893Z c10_flags_test test_nativert 2025-07-17T08:57:34.4143228Z c10_generic_math_test test_parallel 2025-07-17T08:57:34.4143601Z c10_intrusive_ptr_benchmark test_tensorexpr 2025-07-17T08:57:34.4143983Z c10_intrusive_ptr_test thread_init_test 2025-07-17T08:57:34.4144345Z c10_irange_test torch_shm_manager 2025-07-17T08:57:34.4144685Z c10_lazy_test tutorial_tensorexpr 2025-07-17T08:57:34.4145044Z c10_logging_test type_ptr_test 2025-07-17T08:57:34.4145367Z c10_optional_test type_test 2025-07-17T08:57:34.4145795Z c10_ordered_preserving_dict_test undefined_tensor_test 2025-07-17T08:57:34.4146224Z c10_registry_test vec_test_all_types_AVX2 2025-07-17T08:57:34.4146633Z c10_small_vector_test vec_test_all_types_AVX512 2025-07-17T08:57:34.4147040Z c10_ssize_test vec_test_all_types_DEFAULT 2025-07-17T08:57:34.4147434Z c10_string_util_test verify_api_visibility 2025-07-17T08:57:34.4147797Z c10_string_view_test weakref_test 2025-07-17T08:57:34.4148137Z c10_tempfile_test wrapdim_test 2025-07-17T08:57:34.4148471Z c10_typeid_test xla_tensor_test 2025-07-17T08:57:34.4148776Z cmake_install.cmake 2025-07-17T08:57:34.4149046Z + aten/tools/run_tests.sh build/bin 2025-07-17T08:57:34.4149355Z + set -e 2025-07-17T08:57:34.4149921Z ++ dirname aten/tools/run_tests.sh 2025-07-17T08:57:34.4159040Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2025-07-17T08:57:34.4159545Z + export CPP_TESTS_DIR=build/bin 2025-07-17T08:57:34.4159851Z + CPP_TESTS_DIR=build/bin 2025-07-17T08:57:34.4160105Z + VALGRIND=ON 2025-07-17T08:57:34.4161951Z + 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 2025-07-17T08:57:36.6368100Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:57:36.6369954Z import pkg_resources 2025-07-17T08:57:38.0519004Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-07-17T08:57:38.0781263Z Found test times from artifacts 2025-07-17T08:57:38.1814616Z Found test times from artifacts 2025-07-17T08:57:38.1849455Z Running all tests 2025-07-17T08:57:38.1853742Z Running parallel tests on 3 processes 2025-07-17T08:57:38.1855290Z Name: tests to run (est. time: 0.0min) 2025-07-17T08:57:38.1855713Z Serial tests (0): 2025-07-17T08:57:38.1855974Z Parallel tests (19): 2025-07-17T08:57:38.1856235Z cpp/Dict_test 1/1 2025-07-17T08:57:38.1856480Z cpp/Dimname_test 1/1 2025-07-17T08:57:38.1856750Z cpp/NamedTensor_test 1/1 2025-07-17T08:57:38.1857037Z cpp/apply_utils_test 1/1 2025-07-17T08:57:38.1857299Z cpp/atest 1/1 2025-07-17T08:57:38.1857554Z cpp/basic 1/1 2025-07-17T08:57:38.1857799Z cpp/broadcast_test 1/1 2025-07-17T08:57:38.1858079Z cpp/cpu_generator_test 1/1 2025-07-17T08:57:38.1858374Z cpp/dlconvertor_test 1/1 2025-07-17T08:57:38.1858671Z cpp/extension_backend_test 1/1 2025-07-17T08:57:38.1858968Z cpp/lazy_tensor_test 1/1 2025-07-17T08:57:38.1859267Z cpp/legacy_vmap_test 1/1 2025-07-17T08:57:38.1859546Z cpp/native_test 1/1 2025-07-17T08:57:38.1859797Z cpp/operators_test 1/1 2025-07-17T08:57:38.1868666Z cpp/scalar_tensor_test 1/1 2025-07-17T08:57:38.1869104Z cpp/scalar_test 1/1 2025-07-17T08:57:38.1869391Z cpp/tensor_iterator_test 1/1 2025-07-17T08:57:38.1869707Z cpp/undefined_tensor_test 1/1 2025-07-17T08:57:38.1870003Z cpp/wrapdim_test 1/1 2025-07-17T08:57:38.1870292Z Name: excluded (est. time: 0.0min) 2025-07-17T08:57:38.1870593Z Serial tests (0): 2025-07-17T08:57:38.1870830Z Parallel tests (0): 2025-07-17T08:57:38.1871176Z Running cpp/Dict_test 1/1 ... [2025-07-17 08:57:38.185956] 2025-07-17T08:57:38.1871594Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:38.1873513Z 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-e70f0941f0017f93.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:38.187111] 2025-07-17T08:57:39.6043900Z 2025-07-17T08:57:39.6044755Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_03e1db6b20b47e4b_.log 2025-07-17T08:57:39.6045377Z 2025-07-17T08:57:40.9923365Z Uploading artifacts took 1.39 seconds 2025-07-17T08:57:40.9923841Z Running cpp/Dimname_test 1/1 ... [2025-07-17 08:57:40.992154] 2025-07-17T08:57:40.9924263Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:40.9927160Z 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-e139304733f80769.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:40.992510] 2025-07-17T08:57:42.4090258Z 2025-07-17T08:57:42.4091122Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_cae8a50b7a4dbf97_.log 2025-07-17T08:57:42.4091797Z 2025-07-17T08:57:42.4092040Z Running cpp/NamedTensor_test 1/1 ... [2025-07-17 08:57:42.408933] 2025-07-17T08:57:42.4092477Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:42.4094949Z 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-ffd65fc93798a3e5.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:42.409280] 2025-07-17T08:57:43.8257791Z 2025-07-17T08:57:43.8258697Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_798d128c13d925f4_.log 2025-07-17T08:57:43.8259579Z 2025-07-17T08:57:43.8259805Z Running cpp/apply_utils_test 1/1 ... [2025-07-17 08:57:43.825688] 2025-07-17T08:57:43.8260233Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:43.8262927Z 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-a5290f5b1f0e8c9a.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:43.826056] 2025-07-17T08:57:45.2425981Z 2025-07-17T08:57:45.2427040Z 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_33f3bddc84ab845b_.log 2025-07-17T08:57:45.2427747Z 2025-07-17T08:57:45.2427905Z Running cpp/atest 1/1 ... [2025-07-17 08:57:45.242423] 2025-07-17T08:57:45.2428309Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:45.2429964Z 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-2a77e2c12fa7ba43.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:45.242779] 2025-07-17T08:57:46.6592098Z 2025-07-17T08:57:46.6593051Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_f9edd993fa83612f_.log 2025-07-17T08:57:46.6593656Z 2025-07-17T08:57:46.6593825Z Running cpp/basic 1/1 ... [2025-07-17 08:57:46.659106] 2025-07-17T08:57:46.6594214Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:46.6596917Z 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-a74a2ca1eba31f92.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:46.659484] 2025-07-17T08:57:48.0759721Z 2025-07-17T08:57:48.0760673Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_8e9f06e06ee2ccc3_.log 2025-07-17T08:57:48.0761307Z 2025-07-17T08:57:48.0761513Z Running cpp/broadcast_test 1/1 ... [2025-07-17 08:57:48.075878] 2025-07-17T08:57:48.0762117Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:48.0764337Z 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-13c5f089c8cdaea2.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:48.076212] 2025-07-17T08:57:49.4925705Z 2025-07-17T08:57:49.4926662Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_255c7b60c38d2c55_.log 2025-07-17T08:57:49.4927380Z 2025-07-17T08:57:49.4927664Z Running cpp/cpu_generator_test 1/1 ... [2025-07-17 08:57:49.492466] 2025-07-17T08:57:49.4928115Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:49.4930045Z 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-dfc684c0cae75615.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:49.492797] 2025-07-17T08:57:50.9093155Z 2025-07-17T08:57:50.9094167Z 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_4521a04d6254d18d_.log 2025-07-17T08:57:50.9094884Z 2025-07-17T08:57:50.9095104Z Running cpp/dlconvertor_test 1/1 ... [2025-07-17 08:57:50.909216] 2025-07-17T08:57:50.9095590Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:50.9097875Z 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-ff81a9966bca80f3.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:50.909562] 2025-07-17T08:57:52.3262403Z 2025-07-17T08:57:52.3263353Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_74fabfa3c571062f_.log 2025-07-17T08:57:52.3264294Z 2025-07-17T08:57:52.3264549Z Running cpp/extension_backend_test 1/1 ... [2025-07-17 08:57:52.326086] 2025-07-17T08:57:52.3265178Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:52.3267045Z 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-827bdcde43bb4a7b.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:52.326423] 2025-07-17T08:57:53.7429143Z 2025-07-17T08:57:53.7430146Z 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_0d9d9835df7d905c_.log 2025-07-17T08:57:53.7430956Z 2025-07-17T08:57:53.7431202Z Running cpp/lazy_tensor_test 1/1 ... [2025-07-17 08:57:53.742678] 2025-07-17T08:57:53.7431647Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:53.7432991Z 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-0d66f8ef3f18f9fe.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:53.742983] 2025-07-17T08:57:55.1594081Z 2025-07-17T08:57:55.1594993Z 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_b0520b5fc91e2bd1_.log 2025-07-17T08:57:55.1595668Z 2025-07-17T08:57:55.1595894Z Running cpp/legacy_vmap_test 1/1 ... [2025-07-17 08:57:55.159312] 2025-07-17T08:57:55.1596315Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:55.1598987Z 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-c8d30a1d672f960d.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:55.159661] 2025-07-17T08:57:56.5762692Z 2025-07-17T08:57:56.5763580Z 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_61387141dffb4f8f_.log 2025-07-17T08:57:56.5764537Z 2025-07-17T08:57:56.5764732Z Running cpp/native_test 1/1 ... [2025-07-17 08:57:56.576119] 2025-07-17T08:57:56.5765200Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:56.5766870Z 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-e90827439af05976.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:56.576461] 2025-07-17T08:57:57.9930970Z 2025-07-17T08:57:57.9932076Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_8d9505e73a48115b_.log 2025-07-17T08:57:57.9932712Z 2025-07-17T08:57:57.9932906Z Running cpp/operators_test 1/1 ... [2025-07-17 08:57:57.992974] 2025-07-17T08:57:57.9933362Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:57.9935722Z 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-e122654a80c84eda.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:57.993345] 2025-07-17T08:57:59.4599035Z 2025-07-17T08:57:59.4600051Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_015edbf49e83a48a_.log 2025-07-17T08:57:59.4600784Z 2025-07-17T08:57:59.4601001Z Running cpp/scalar_tensor_test 1/1 ... [2025-07-17 08:57:59.459691] 2025-07-17T08:57:59.4601475Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:57:59.4602797Z 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-32866a3e21a3d76d.xml', '-x', '--reruns=2'] ... [2025-07-17 08:57:59.460066] 2025-07-17T08:58:00.8765328Z 2025-07-17T08:58:00.8766621Z 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_b8ef22b80043af0c_.log 2025-07-17T08:58:00.8767344Z 2025-07-17T08:58:00.8767776Z Running cpp/scalar_test 1/1 ... [2025-07-17 08:58:00.876398] 2025-07-17T08:58:00.8768201Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:00.8769923Z 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-2a23112e805ca22c.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:00.876748] 2025-07-17T08:58:02.2932721Z 2025-07-17T08:58:02.2933716Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_1fa55b9c41531211_.log 2025-07-17T08:58:02.2934381Z 2025-07-17T08:58:02.2934596Z Running cpp/tensor_iterator_test 1/1 ... [2025-07-17 08:58:02.293147] 2025-07-17T08:58:02.2935079Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:02.2937543Z 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-50a1801522634c4a.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:02.293507] 2025-07-17T08:58:03.7098423Z 2025-07-17T08:58:03.7099650Z 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_6756af664dff35ce_.log 2025-07-17T08:58:03.7100364Z 2025-07-17T08:58:03.7100607Z Running cpp/undefined_tensor_test 1/1 ... [2025-07-17 08:58:03.709727] 2025-07-17T08:58:03.7101108Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:03.7103221Z 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-711ecb1d72897da4.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:03.710073] 2025-07-17T08:58:05.1264476Z 2025-07-17T08:58:05.1266123Z 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_59685f5f52998e60_.log 2025-07-17T08:58:05.1266887Z 2025-07-17T08:58:05.1267085Z Running cpp/wrapdim_test 1/1 ... [2025-07-17 08:58:05.126370] 2025-07-17T08:58:05.1267521Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:05.1269724Z 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-468f51aad972f96e.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:05.126721] 2025-07-17T08:58:06.4930180Z 2025-07-17T08:58:06.4931036Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_917ec4327fca379a_.log 2025-07-17T08:58:06.4931722Z 2025-07-17T08:58:08.9058161Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:08.9059727Z import pkg_resources 2025-07-17T08:58:08.9460981Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:08.9462649Z import pkg_resources 2025-07-17T08:58:08.9555178Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:08.9556940Z import pkg_resources 2025-07-17T08:58:09.0303231Z Running cpp/Dict_test 1/1 ... [2025-07-17 08:58:09.030058] 2025-07-17T08:58:09.0303922Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:09.0307140Z 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-4579c3a91a23bc42.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:09.030488] 2025-07-17T08:58:09.0708959Z Running cpp/Dimname_test 1/1 ... [2025-07-17 08:58:09.070663] 2025-07-17T08:58:09.0709390Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:09.0713193Z 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-718f1905c1a21c8e.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:09.071086] 2025-07-17T08:58:09.0796905Z Running cpp/NamedTensor_test 1/1 ... [2025-07-17 08:58:09.079549] 2025-07-17T08:58:09.0797366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:09.0802091Z 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-a57686d8feceefdb.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:09.079991] 2025-07-17T08:58:11.2397787Z 2025-07-17T08:58:11.2399062Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_b213c79d6e538326_.log 2025-07-17T08:58:11.2400116Z 2025-07-17T08:58:11.9497359Z 2025-07-17T08:58:11.9498326Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_282cfd162e1f9158_.log 2025-07-17T08:58:11.9499061Z 2025-07-17T08:58:14.6729399Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:14.6731176Z import pkg_resources 2025-07-17T08:58:14.8105633Z Running cpp/apply_utils_test 1/1 ... [2025-07-17 08:58:14.810237] 2025-07-17T08:58:14.8106118Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:14.8108984Z 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-e85f46ab31c94cdf.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:14.810669] 2025-07-17T08:58:14.8564440Z 2025-07-17T08:58:14.8565642Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_4ac3febed9b9f6cd_.log 2025-07-17T08:58:14.8566392Z 2025-07-17T08:58:15.1973703Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:15.1975397Z import pkg_resources 2025-07-17T08:58:15.3253322Z Running cpp/atest 1/1 ... [2025-07-17 08:58:15.324990] 2025-07-17T08:58:15.3253768Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:15.3256679Z 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-f7f3ece125d6cc18.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:15.325421] 2025-07-17T08:58:17.1290685Z 2025-07-17T08:58:17.1291927Z 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_c763681e8b0ead47_.log 2025-07-17T08:58:17.1292658Z 2025-07-17T08:58:18.1310870Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:18.1312448Z import pkg_resources 2025-07-17T08:58:18.2576391Z Running cpp/basic 1/1 ... [2025-07-17 08:58:18.257164] 2025-07-17T08:58:18.2576849Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:18.2580354Z 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-f3e35660ca2fff34.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:18.257775] 2025-07-17T08:58:18.4955817Z 2025-07-17T08:58:18.4959255Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_38ce4094fe8764c9_.log 2025-07-17T08:58:18.4959881Z 2025-07-17T08:58:20.4259180Z 2025-07-17T08:58:20.4260422Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_d7735345c2c12300_.log 2025-07-17T08:58:20.4261491Z 2025-07-17T08:58:20.5646504Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:20.5649321Z import pkg_resources 2025-07-17T08:58:20.6915425Z Running cpp/broadcast_test 1/1 ... [2025-07-17 08:58:20.691169] 2025-07-17T08:58:20.6916226Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:20.6919773Z 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-3b2ee7c99e98cf2a.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:20.691608] 2025-07-17T08:58:21.7832051Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:21.7833623Z import pkg_resources 2025-07-17T08:58:21.9098456Z Running cpp/cpu_generator_test 1/1 ... [2025-07-17 08:58:21.909484] 2025-07-17T08:58:21.9098990Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:21.9101945Z 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-8b2041c50b49a4a8.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:21.909942] 2025-07-17T08:58:22.5092230Z 2025-07-17T08:58:22.5093132Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_6f7ec74c92c027cb_.log 2025-07-17T08:58:22.5093814Z 2025-07-17T08:58:23.6434581Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:23.6437615Z import pkg_resources 2025-07-17T08:58:23.7811036Z Running cpp/dlconvertor_test 1/1 ... [2025-07-17 08:58:23.780756] 2025-07-17T08:58:23.7811515Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:23.7814411Z 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-a38b283e2336d62e.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:23.781188] 2025-07-17T08:58:24.9793925Z 2025-07-17T08:58:24.9796259Z 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_dcd50f4b86570eb6_.log 2025-07-17T08:58:24.9796997Z 2025-07-17T08:58:25.7622489Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:25.7625338Z import pkg_resources 2025-07-17T08:58:25.7991690Z 2025-07-17T08:58:25.7992589Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_0a8745fe81b3f2fd_.log 2025-07-17T08:58:25.7993429Z 2025-07-17T08:58:25.9088564Z Running cpp/extension_backend_test 1/1 ... [2025-07-17 08:58:25.908548] 2025-07-17T08:58:25.9089055Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:25.9092306Z 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-8e881b26647b1a15.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:25.908994] 2025-07-17T08:58:27.6763152Z 2025-07-17T08:58:27.6764150Z 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_30a9ba5ddb63e5e3_.log 2025-07-17T08:58:27.6764915Z 2025-07-17T08:58:28.3004809Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:28.3006640Z import pkg_resources 2025-07-17T08:58:28.4270852Z Running cpp/lazy_tensor_test 1/1 ... [2025-07-17 08:58:28.426786] 2025-07-17T08:58:28.4271323Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:28.4274705Z 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-145b84a7ab7757a5.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:28.427235] 2025-07-17T08:58:28.9993510Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:28.9995102Z import pkg_resources 2025-07-17T08:58:29.1253726Z Running cpp/legacy_vmap_test 1/1 ... [2025-07-17 08:58:29.124988] 2025-07-17T08:58:29.1254508Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:29.1257459Z 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-c9e6732d8988b819.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:29.125428] 2025-07-17T08:58:30.2447621Z 2025-07-17T08:58:30.2449014Z 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_c5345901cc9bb7ef_.log 2025-07-17T08:58:30.2450308Z 2025-07-17T08:58:30.8929755Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:30.8933038Z import pkg_resources 2025-07-17T08:58:31.0222441Z Running cpp/native_test 1/1 ... [2025-07-17 08:58:31.021823] 2025-07-17T08:58:31.0223446Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:31.0225871Z 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-5aa671bbeb25b043.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:31.022257] 2025-07-17T08:58:32.7460930Z 2025-07-17T08:58:32.7461935Z 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_4e0e400dcbbc9614_.log 2025-07-17T08:58:32.7462631Z 2025-07-17T08:58:32.9903708Z 2025-07-17T08:58:32.9904616Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_1aee2a06ce5c56bf_.log 2025-07-17T08:58:32.9905272Z 2025-07-17T08:58:33.5175591Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:33.5178652Z import pkg_resources 2025-07-17T08:58:33.6440973Z Running cpp/operators_test 1/1 ... [2025-07-17 08:58:33.643717] 2025-07-17T08:58:33.6441813Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:33.6445264Z 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-e300d9038e8dfc43.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:33.644175] 2025-07-17T08:58:35.7118197Z 2025-07-17T08:58:35.7119117Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_50151dc500cd5a0f_.log 2025-07-17T08:58:35.7120009Z 2025-07-17T08:58:35.9383518Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:35.9385098Z import pkg_resources 2025-07-17T08:58:36.0647652Z Running cpp/scalar_tensor_test 1/1 ... [2025-07-17 08:58:36.064476] 2025-07-17T08:58:36.0648104Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:36.0651599Z 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-11ff739a1d37653b.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:36.064907] 2025-07-17T08:58:36.2218801Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:36.2220355Z import pkg_resources 2025-07-17T08:58:36.3481357Z Running cpp/scalar_test 1/1 ... [2025-07-17 08:58:36.347855] 2025-07-17T08:58:36.3481814Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:36.3484980Z 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-8f968d839e63adb9.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:36.348275] 2025-07-17T08:58:37.9324953Z 2025-07-17T08:58:37.9326070Z 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_aec55a84f8a26207_.log 2025-07-17T08:58:37.9326820Z 2025-07-17T08:58:38.5162384Z 2025-07-17T08:58:38.5163516Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_2c14dfe410c68aa1_.log 2025-07-17T08:58:38.5164203Z 2025-07-17T08:58:39.0064830Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:39.0066413Z import pkg_resources 2025-07-17T08:58:39.1560884Z Running cpp/tensor_iterator_test 1/1 ... [2025-07-17 08:58:39.155784] 2025-07-17T08:58:39.1561596Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:39.1565856Z 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-3b2d990a31752ac0.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:39.156245] 2025-07-17T08:58:41.1516772Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:41.1519597Z import pkg_resources 2025-07-17T08:58:41.2799804Z Running cpp/undefined_tensor_test 1/1 ... [2025-07-17 08:58:41.279556] 2025-07-17T08:58:41.2800706Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:41.2803266Z 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-0fc1388c827ffffb.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:41.279989] 2025-07-17T08:58:41.7444864Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:41.7447785Z import pkg_resources 2025-07-17T08:58:41.8730832Z Running cpp/wrapdim_test 1/1 ... [2025-07-17 08:58:41.872656] 2025-07-17T08:58:41.8731650Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:58:41.8734980Z 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-0051c941f08467c9.xml', '-x', '--reruns=2'] ... [2025-07-17 08:58:41.873130] 2025-07-17T08:58:43.0977927Z 2025-07-17T08:58:43.0979434Z 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_0937195d2b70b399_.log 2025-07-17T08:58:43.0980858Z 2025-07-17T08:58:43.6911791Z 2025-07-17T08:58:43.6912771Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_9b17c23e4206b312_.log 2025-07-17T08:58:43.6913443Z 2025-07-17T08:58:46.4954401Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:46.4957209Z import pkg_resources 2025-07-17T08:58:46.8343027Z 2025-07-17T08:58:46.8343990Z 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_614d0f516d39b12f_.log 2025-07-17T08:58:46.8344979Z 2025-07-17T08:58:47.0337058Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:58:47.0338637Z import pkg_resources 2025-07-17T08:58:47.8822540Z Running test batch 'tests to run' cost 69.7 seconds 2025-07-17T08:58:48.5610019Z + run_if_exists tensor_interop_test 2025-07-17T08:58:48.5610507Z + local test_name=tensor_interop_test 2025-07-17T08:58:48.5610867Z + [[ -x build/bin/tensor_interop_test ]] 2025-07-17T08:58:48.5611400Z + echo 'Warning: tensor_interop_test does not exist.' 2025-07-17T08:58:48.5611978Z Warning: tensor_interop_test does not exist. 2025-07-17T08:58:48.5612407Z + run_if_exists cudnn_test 2025-07-17T08:58:48.5612722Z + local test_name=cudnn_test 2025-07-17T08:58:48.5613054Z + [[ -x build/bin/cudnn_test ]] 2025-07-17T08:58:48.5613494Z + echo 'Warning: cudnn_test does not exist.' 2025-07-17T08:58:48.5614209Z Warning: cudnn_test does not exist. 2025-07-17T08:58:48.5614545Z + run_if_exists cuda_generator_test 2025-07-17T08:58:48.5614929Z + local test_name=cuda_generator_test 2025-07-17T08:58:48.5615332Z + [[ -x build/bin/cuda_generator_test ]] 2025-07-17T08:58:48.5615766Z + echo 'Warning: cuda_generator_test does not exist.' 2025-07-17T08:58:48.5616150Z Warning: cuda_generator_test does not exist. 2025-07-17T08:58:48.5616500Z + run_if_exists apply_test 2025-07-17T08:58:48.5616789Z + local test_name=apply_test 2025-07-17T08:58:48.5617073Z + [[ -x build/bin/apply_test ]] 2025-07-17T08:58:48.5617396Z + echo 'Warning: apply_test does not exist.' 2025-07-17T08:58:48.5617739Z Warning: apply_test does not exist. 2025-07-17T08:58:48.5618037Z + run_if_exists stream_test 2025-07-17T08:58:48.5618329Z + local test_name=stream_test 2025-07-17T08:58:48.5618620Z + [[ -x build/bin/stream_test ]] 2025-07-17T08:58:48.5618927Z + echo 'Warning: stream_test does not exist.' 2025-07-17T08:58:48.5619376Z Warning: stream_test does not exist. 2025-07-17T08:58:48.5619699Z + run_if_exists cuda_half_test 2025-07-17T08:58:48.5619993Z + local test_name=cuda_half_test 2025-07-17T08:58:48.5620281Z + [[ -x build/bin/cuda_half_test ]] 2025-07-17T08:58:48.5620645Z + echo 'Warning: cuda_half_test does not exist.' 2025-07-17T08:58:48.5621010Z Warning: cuda_half_test does not exist. 2025-07-17T08:58:48.5621331Z + run_if_exists cuda_vectorized_test 2025-07-17T08:58:48.5621657Z + local test_name=cuda_vectorized_test 2025-07-17T08:58:48.5621995Z + [[ -x build/bin/cuda_vectorized_test ]] 2025-07-17T08:58:48.5622380Z + echo 'Warning: cuda_vectorized_test does not exist.' 2025-07-17T08:58:48.5622772Z Warning: cuda_vectorized_test does not exist. 2025-07-17T08:58:48.5623135Z + run_if_exists cuda_distributions_test 2025-07-17T08:58:48.5623481Z + local test_name=cuda_distributions_test 2025-07-17T08:58:48.5623823Z + [[ -x build/bin/cuda_distributions_test ]] 2025-07-17T08:58:48.5624227Z + echo 'Warning: cuda_distributions_test does not exist.' 2025-07-17T08:58:48.5624659Z Warning: cuda_distributions_test does not exist. 2025-07-17T08:58:48.5625016Z + run_if_exists cuda_optional_test 2025-07-17T08:58:48.5625334Z + local test_name=cuda_optional_test 2025-07-17T08:58:48.5625659Z + [[ -x build/bin/cuda_optional_test ]] 2025-07-17T08:58:48.5626027Z + echo 'Warning: cuda_optional_test does not exist.' 2025-07-17T08:58:48.5626405Z Warning: cuda_optional_test does not exist. 2025-07-17T08:58:48.5626759Z + run_if_exists cuda_tensor_interop_test 2025-07-17T08:58:48.5627105Z + local test_name=cuda_tensor_interop_test 2025-07-17T08:58:48.5627447Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2025-07-17T08:58:48.5627852Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2025-07-17T08:58:48.5628282Z Warning: cuda_tensor_interop_test does not exist. 2025-07-17T08:58:48.5628704Z + run_if_exists cuda_complex_test 2025-07-17T08:58:48.5629020Z + local test_name=cuda_complex_test 2025-07-17T08:58:48.5629348Z + [[ -x build/bin/cuda_complex_test ]] 2025-07-17T08:58:48.5629697Z + echo 'Warning: cuda_complex_test does not exist.' 2025-07-17T08:58:48.5630084Z Warning: cuda_complex_test does not exist. 2025-07-17T08:58:48.5630487Z + run_if_exists cuda_complex_math_test 2025-07-17T08:58:48.5630819Z + local test_name=cuda_complex_math_test 2025-07-17T08:58:48.5631147Z + [[ -x build/bin/cuda_complex_math_test ]] 2025-07-17T08:58:48.5631541Z + echo 'Warning: cuda_complex_math_test does not exist.' 2025-07-17T08:58:48.5631958Z Warning: cuda_complex_math_test does not exist. 2025-07-17T08:58:48.5632301Z + run_if_exists cuda_cub_test 2025-07-17T08:58:48.5632598Z + local test_name=cuda_cub_test 2025-07-17T08:58:48.5632895Z + [[ -x build/bin/cuda_cub_test ]] 2025-07-17T08:58:48.5633218Z + echo 'Warning: cuda_cub_test does not exist.' 2025-07-17T08:58:48.5633578Z Warning: cuda_cub_test does not exist. 2025-07-17T08:58:48.5633906Z + run_if_exists cuda_atomic_ops_test 2025-07-17T08:58:48.5634220Z + local test_name=cuda_atomic_ops_test 2025-07-17T08:58:48.5634551Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2025-07-17T08:58:48.5634959Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2025-07-17T08:58:48.5635363Z Warning: cuda_atomic_ops_test does not exist. 2025-07-17T08:58:48.5635679Z + '[' ON == ON ']' 2025-07-17T08:58:48.5636386Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2025-07-17T08:58:48.5859513Z ==176662== Memcheck, a memory error detector 2025-07-17T08:58:48.5859991Z ==176662== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2025-07-17T08:58:48.5860581Z ==176662== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2025-07-17T08:58:48.5861106Z ==176662== Command: build/bin/basic --gtest_filter=-*CUDA 2025-07-17T08:58:48.5861469Z ==176662== 2025-07-17T08:58:49.1281217Z ==176662== Warning: set address range perms: large range [0x4a4c000, 0x1c88c000) (defined) 2025-07-17T08:58:49.1282162Z ==176662== Warning: set address range perms: large range [0x5cc8000, 0x1830f000) (defined) 2025-07-17T08:59:13.4262686Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2025-07-17T08:59:13.4553533Z Note: Google Test filter = -*CUDA 2025-07-17T08:59:13.4603603Z [==========] Running 4 tests from 1 test suite. 2025-07-17T08:59:13.4630189Z [----------] Global test environment set-up. 2025-07-17T08:59:13.4700124Z [----------] 4 tests from BasicTest 2025-07-17T08:59:13.4723519Z [ RUN ] BasicTest.BasicTestCPU 2025-07-17T08:59:14.0286732Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-07-17T08:59:14.0287248Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-07-17T08:59:14.0287892Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-07-17T08:59:14.0837079Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-07-17T08:59:14.0837667Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-07-17T08:59:14.0838143Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-07-17T08:59:14.1629629Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-07-17T08:59:14.1630124Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-07-17T08:59:14.1630597Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-07-17T08:59:15.2620671Z 661 ms 2025-07-17T08:59:15.3460157Z 55 ms 2025-07-17T08:59:15.4193478Z 65 ms 2025-07-17T08:59:16.5018525Z [ OK ] BasicTest.BasicTestCPU (3027 ms) 2025-07-17T08:59:16.5043242Z [ RUN ] BasicTest.BasicTestHalfCPU 2025-07-17T08:59:16.6954678Z 138 ms 2025-07-17T08:59:16.7711900Z 46 ms 2025-07-17T08:59:16.8372409Z 63 ms 2025-07-17T08:59:16.8899793Z [ OK ] BasicTest.BasicTestHalfCPU (378 ms) 2025-07-17T08:59:16.8900543Z [ RUN ] BasicTest.FactoryMethodsTest 2025-07-17T08:59:16.9225800Z [ OK ] BasicTest.FactoryMethodsTest (32 ms) 2025-07-17T08:59:16.9226529Z [ RUN ] BasicTest.BasicStdTestCPU 2025-07-17T08:59:17.0018753Z Simple example: called once 2025-07-17T08:59:17.0879852Z Didn't throw, call_once will not attempt again 2025-07-17T08:59:17.1280191Z [ OK ] BasicTest.BasicStdTestCPU (205 ms) 2025-07-17T08:59:17.1302721Z [----------] 4 tests from BasicTest (3656 ms total) 2025-07-17T08:59:17.1303234Z 2025-07-17T08:59:17.1315039Z [----------] Global test environment tear-down 2025-07-17T08:59:17.1344554Z [==========] 4 tests from 1 test suite ran. (3683 ms total) 2025-07-17T08:59:17.1357782Z [ PASSED ] 4 tests. 2025-07-17T08:59:18.9539169Z ==176662== 2025-07-17T08:59:18.9543489Z ==176662== HEAP SUMMARY: 2025-07-17T08:59:18.9544074Z ==176662== in use at exit: 481,032 bytes in 7,158 blocks 2025-07-17T08:59:18.9544964Z ==176662== total heap usage: 652,174 allocs, 645,016 frees, 195,612,526 bytes allocated 2025-07-17T08:59:18.9545739Z ==176662== 2025-07-17T08:59:18.9947056Z ==176662== LEAK SUMMARY: 2025-07-17T08:59:18.9947658Z ==176662== definitely lost: 0 bytes in 0 blocks 2025-07-17T08:59:18.9948380Z ==176662== indirectly lost: 0 bytes in 0 blocks 2025-07-17T08:59:18.9949366Z ==176662== possibly lost: 69,920 bytes in 2 blocks 2025-07-17T08:59:18.9950110Z ==176662== still reachable: 411,112 bytes in 7,156 blocks 2025-07-17T08:59:18.9950865Z ==176662== suppressed: 0 bytes in 0 blocks 2025-07-17T08:59:18.9951705Z ==176662== Rerun with --leak-check=full to see details of leaked memory 2025-07-17T08:59:18.9952471Z ==176662== 2025-07-17T08:59:18.9953050Z ==176662== For lists of detected and suppressed errors, rerun with: -s 2025-07-17T08:59:18.9954036Z ==176662== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2025-07-17T08:59:19.0396501Z + [[ -x build/bin/tensor_interop_test ]] 2025-07-17T08:59:19.0398434Z + [[ -n '' ]] 2025-07-17T08:59:19.0398671Z + assert_git_not_dirty 2025-07-17T08:59:19.0398974Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-07-17T08:59:19.0399356Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-07-17T08:59:19.0404942Z ++ git status --porcelain 2025-07-17T08:59:19.0406255Z ++ grep -v '?? third_party' 2025-07-17T08:59:19.3052232Z ++ true 2025-07-17T08:59:19.3052905Z + git_status= 2025-07-17T08:59:19.3053149Z + [[ -n '' ]] 2025-07-17T08:59:19.3053378Z + test_libtorch 1 2025-07-17T08:59:19.3053622Z + local SHARD=1 2025-07-17T08:59:19.3053852Z + [[ default != \s\l\o\w ]] 2025-07-17T08:59:19.3054142Z + echo 'Testing libtorch' 2025-07-17T08:59:19.3054407Z Testing libtorch 2025-07-17T08:59:19.3055249Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libbackend_with_compiler.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T08:59:19.3078780Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libjitbackend_test.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T08:59:19.3090033Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2_nvrtc.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T08:59:19.3100200Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T08:59:19.3112028Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libshm /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libshm.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libshm_windows /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T08:59:19.3124674Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorchbind_test.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T08:59:19.3134349Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnvfuser*' /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T08:59:19.3144787Z + export CPP_TESTS_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T08:59:19.3145511Z + CPP_TESTS_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-07-17T08:59:19.3145979Z + [[ -z 1 ]] 2025-07-17T08:59:19.3146204Z + [[ 1 == \1 ]] 2025-07-17T08:59:19.3146440Z + test_libtorch_api 2025-07-17T08:59:19.3146754Z + MNIST_DIR=/var/lib/jenkins/workspace/test/cpp/api/mnist 2025-07-17T08:59:19.3147342Z + python tools/download_mnist.py --quiet -d /var/lib/jenkins/workspace/test/cpp/api/mnist 2025-07-17T08:59:19.3634979Z Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz ... 2025-07-17T08:59:19.3839165Z Failed to download (trying next): 2025-07-17T08:59:19.3839520Z HTTP Error 404: Not Found 2025-07-17T08:59:19.3840034Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz ... 2025-07-17T08:59:19.7158192Z Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz ... 2025-07-17T08:59:19.7209936Z Failed to download (trying next): 2025-07-17T08:59:19.7210269Z HTTP Error 404: Not Found 2025-07-17T08:59:19.7210797Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz ... 2025-07-17T08:59:19.7546989Z Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz ... 2025-07-17T08:59:19.7598486Z Failed to download (trying next): 2025-07-17T08:59:19.7598824Z HTTP Error 404: Not Found 2025-07-17T08:59:19.7599319Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz ... 2025-07-17T08:59:19.8249798Z Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz ... 2025-07-17T08:59:19.8302530Z Failed to download (trying next): 2025-07-17T08:59:19.8302857Z HTTP Error 404: Not Found 2025-07-17T08:59:19.8303369Z Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz ... 2025-07-17T08:59:19.8790439Z + [[ linux-jammy-py3.13-clang12 == *asan* ]] 2025-07-17T08:59:19.8790886Z + [[ linux-jammy-py3.13-clang12 == *slow-gradcheck* ]] 2025-07-17T08:59:19.8791259Z + OMP_NUM_THREADS=2 2025-07-17T08:59:19.8791637Z + TORCH_CPP_TEST_MNIST_PATH=/var/lib/jenkins/workspace/test/cpp/api/mnist 2025-07-17T08:59:19.8792252Z + python test/run_test.py --cpp --verbose -i cpp/test_api -k 'not IMethodTest' 2025-07-17T08:59:22.1761698Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:59:22.1763275Z import pkg_resources 2025-07-17T08:59:23.6007375Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-07-17T08:59:23.6269505Z Found test times from artifacts 2025-07-17T08:59:23.7300941Z Found test times from artifacts 2025-07-17T08:59:23.7336363Z Running all tests 2025-07-17T08:59:23.7339359Z Running parallel tests on 3 processes 2025-07-17T08:59:23.7339715Z Name: tests to run (est. time: 0.0min) 2025-07-17T08:59:23.7340028Z Serial tests (0): 2025-07-17T08:59:23.7340277Z Parallel tests (1): 2025-07-17T08:59:23.7340519Z cpp/test_api 1/1 2025-07-17T08:59:23.7340782Z Name: excluded (est. time: 0.0min) 2025-07-17T08:59:23.7341081Z Serial tests (0): 2025-07-17T08:59:23.7341313Z Parallel tests (0): 2025-07-17T08:59:23.7344593Z Running cpp/test_api 1/1 ... [2025-07-17 08:59:23.734326] 2025-07-17T08:59:23.7344992Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:59:23.7349480Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_api', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-6662f8755b5c9abd.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2025-07-17 08:59:23.734721] 2025-07-17T08:59:25.7018621Z 2025-07-17T08:59:25.7019656Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_2cc7cdc748cad679_.log 2025-07-17T08:59:25.7020292Z 2025-07-17T08:59:26.8460593Z Uploading artifacts took 1.14 seconds 2025-07-17T08:59:29.1859497Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:59:29.1861055Z import pkg_resources 2025-07-17T08:59:29.1951481Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:59:29.1953295Z import pkg_resources 2025-07-17T08:59:29.1995137Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T08:59:29.1996672Z import pkg_resources 2025-07-17T08:59:29.3090004Z Running cpp/test_api 1/1 ... [2025-07-17 08:59:29.308721] 2025-07-17T08:59:29.3090443Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T08:59:29.3094159Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_api', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9c44ba02338a10e2.xml', '-k', 'not IMethodTest', '-x', '--reruns=2'] ... [2025-07-17 08:59:29.309132] 2025-07-17T09:01:23.4060008Z 2025-07-17T09:01:23.4060923Z cpp/test_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_api_1.1_66722138e76d66e9_.log 2025-07-17T09:01:23.4072694Z 2025-07-17T09:01:24.1438913Z Running test batch 'tests to run' cost 120.41 seconds 2025-07-17T09:01:24.8124613Z + [[ linux-jammy-py3.13-clang12 != *s390x* ]] 2025-07-17T09:01:24.8125130Z + python test/run_test.py --cpp --verbose -i cpp/test_tensorexpr 2025-07-17T09:01:27.0619811Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:01:27.0621494Z import pkg_resources 2025-07-17T09:01:28.4810142Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-07-17T09:01:28.5071000Z Found test times from artifacts 2025-07-17T09:01:28.6107850Z Found test times from artifacts 2025-07-17T09:01:28.6144658Z Running all tests 2025-07-17T09:01:28.6147762Z Running parallel tests on 3 processes 2025-07-17T09:01:28.6148498Z Name: tests to run (est. time: 0.0min) 2025-07-17T09:01:28.6148849Z Serial tests (0): 2025-07-17T09:01:28.6149110Z Parallel tests (1): 2025-07-17T09:01:28.6149389Z cpp/test_tensorexpr 1/1 2025-07-17T09:01:28.6149678Z Name: excluded (est. time: 0.0min) 2025-07-17T09:01:28.6149986Z Serial tests (0): 2025-07-17T09:01:28.6150242Z Parallel tests (0): 2025-07-17T09:01:28.6150612Z Running cpp/test_tensorexpr 1/1 ... [2025-07-17 09:01:28.614895] 2025-07-17T09:01:28.6151035Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T09:01:28.6155503Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_tensorexpr', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-052d35878f39391b.xml', '-x', '--reruns=2'] ... [2025-07-17 09:01:28.615314] 2025-07-17T09:01:30.1317640Z 2025-07-17T09:01:30.1318806Z cpp/test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_tensorexpr_1.1_a4dbb42aaf14e4a1_.log 2025-07-17T09:01:30.1319513Z 2025-07-17T09:01:31.3530393Z Uploading artifacts took 1.22 seconds 2025-07-17T09:01:33.7675955Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:01:33.7678878Z import pkg_resources 2025-07-17T09:01:33.7693151Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:01:33.7696098Z import pkg_resources 2025-07-17T09:01:33.7698886Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:01:33.7701788Z import pkg_resources 2025-07-17T09:01:33.8901214Z Running cpp/test_tensorexpr 1/1 ... [2025-07-17 09:01:33.889764] 2025-07-17T09:01:33.8901893Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T09:01:33.8905946Z Executing ['pytest', '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin/test_tensorexpr', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d647ab9acd4c9d8f.xml', '-x', '--reruns=2'] ... [2025-07-17 09:01:33.890207] 2025-07-17T09:02:59.2909991Z 2025-07-17T09:02:59.2910926Z cpp/test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.test_tensorexpr_1.1_851cb2d2e33d8fa3_.log 2025-07-17T09:02:59.2916359Z 2025-07-17T09:03:00.0328851Z Running test batch 'tests to run' cost 91.42 seconds 2025-07-17T09:03:00.6764868Z + [[ linux-jammy-py3.13-clang12 != *android* ]] 2025-07-17T09:03:00.6765298Z + [[ linux-jammy-py3.13-clang12 != *cuda* ]] 2025-07-17T09:03:00.6765658Z + [[ linux-jammy-py3.13-clang12 != *asan* ]] 2025-07-17T09:03:00.6766001Z + [[ linux-jammy-py3.13-clang12 != *s390x* ]] 2025-07-17T09:03:00.6766350Z + export CPP_TESTS_DIR=build/bin 2025-07-17T09:03:00.6766655Z + CPP_TESTS_DIR=build/bin 2025-07-17T09:03:00.6767063Z + python test/run_test.py --cpp --verbose -i cpp/static_runtime_test 2025-07-17T09:03:02.9335210Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:03:02.9336828Z import pkg_resources 2025-07-17T09:03:04.3814772Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-07-17T09:03:04.4075057Z Found test times from artifacts 2025-07-17T09:03:04.5106959Z Found test times from artifacts 2025-07-17T09:03:04.5143413Z Running all tests 2025-07-17T09:03:04.5146656Z Running parallel tests on 3 processes 2025-07-17T09:03:04.5147012Z Name: tests to run (est. time: 0.0min) 2025-07-17T09:03:04.5147325Z Serial tests (0): 2025-07-17T09:03:04.5147898Z Parallel tests (1): 2025-07-17T09:03:04.5148253Z cpp/static_runtime_test 1/1 2025-07-17T09:03:04.5148644Z Name: excluded (est. time: 0.0min) 2025-07-17T09:03:04.5148951Z Serial tests (0): 2025-07-17T09:03:04.5149199Z Parallel tests (0): 2025-07-17T09:03:04.5149685Z Running cpp/static_runtime_test 1/1 ... [2025-07-17 09:03:04.514744] 2025-07-17T09:03:04.5150137Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T09:03:04.5153982Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/static_runtime_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-c76977c9db6a0b5d.xml', '-x', '--reruns=2'] ... [2025-07-17 09:03:04.515179] 2025-07-17T09:03:05.9315296Z 2025-07-17T09:03:05.9316265Z cpp/static_runtime_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.static_runtime_test_1.1_0f7e312418963635_.log 2025-07-17T09:03:05.9316994Z 2025-07-17T09:03:07.1352927Z Uploading artifacts took 1.20 seconds 2025-07-17T09:03:09.5627389Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:03:09.5628986Z import pkg_resources 2025-07-17T09:03:09.6018380Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:03:09.6019948Z import pkg_resources 2025-07-17T09:03:09.6037242Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-07-17T09:03:09.6039014Z import pkg_resources 2025-07-17T09:03:09.6885149Z Running cpp/static_runtime_test 1/1 ... [2025-07-17 09:03:09.688203] 2025-07-17T09:03:09.6885648Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-07-17T09:03:09.6888683Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/static_runtime_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1b8a92f4a9a45e4e.xml', '-x', '--reruns=2'] ... [2025-07-17 09:03:09.688613] 2025-07-17T09:10:04.0423437Z 2025-07-17T09:10:04.0424871Z cpp/static_runtime_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.static_runtime_test_1.1_7712e5f75b44c61f_.log 2025-07-17T09:10:04.0428554Z 2025-07-17T09:10:04.7846378Z Running test batch 'tests to run' cost 420.27 seconds 2025-07-17T09:10:05.4769672Z + [[ -z 1 ]] 2025-07-17T09:10:05.4769971Z + [[ 1 == \2 ]] 2025-07-17T09:10:05.4770232Z + assert_git_not_dirty 2025-07-17T09:10:05.4770530Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-07-17T09:10:05.4770908Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-07-17T09:10:05.4775157Z ++ git status --porcelain 2025-07-17T09:10:05.4776022Z ++ grep -v '?? third_party' 2025-07-17T09:10:05.7437256Z ++ true 2025-07-17T09:10:05.7437734Z + git_status= 2025-07-17T09:10:05.7437981Z + [[ -n '' ]] 2025-07-17T09:10:05.7438244Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-07-17T09:10:05.7439251Z + sccache_epilogue 2025-07-17T09:10:05.7439562Z + echo '::group::Sccache Compilation Log' 2025-07-17T09:10:05.7440194Z ##[group]Sccache Compilation Log 2025-07-17T09:10:05.7440568Z + echo '=================== sccache compilation log ===================' 2025-07-17T09:10:05.7440990Z =================== sccache compilation log =================== 2025-07-17T09:10:05.7442754Z + python /var/lib/jenkins/workspace/.ci/pytorch/print_sccache_log.py /var/lib/jenkins/sccache_error.log 2025-07-17T09:10:05.7578842Z + echo '=========== If your build fails, please take a look at the log above for possible reasons ===========' 2025-07-17T09:10:05.7579743Z =========== If your build fails, please take a look at the log above for possible reasons =========== 2025-07-17T09:10:05.7580263Z + sccache --show-stats 2025-07-17T09:10:05.7607456Z Compile requests 6683 2025-07-17T09:10:05.7607831Z Compile requests executed 181 2025-07-17T09:10:05.7608160Z Cache hits 128 2025-07-17T09:10:05.7608474Z Cache hits (C/C++) 128 2025-07-17T09:10:05.7608794Z Cache misses 52 2025-07-17T09:10:05.7609095Z Cache misses (C/C++) 52 2025-07-17T09:10:05.7609415Z Cache hits rate 71.11 % 2025-07-17T09:10:05.7609743Z Cache hits rate (C/C++) 71.11 % 2025-07-17T09:10:05.7610125Z Cache timeouts 0 2025-07-17T09:10:05.7610534Z Cache read errors 0 2025-07-17T09:10:05.7610856Z Forced recaches 0 2025-07-17T09:10:05.7611180Z Cache write errors 0 2025-07-17T09:10:05.7611597Z Cache errors 0 2025-07-17T09:10:05.7611916Z Compilations 52 2025-07-17T09:10:05.7612249Z Compilation failures 1 2025-07-17T09:10:05.7612567Z Non-cacheable compilations 0 2025-07-17T09:10:05.7612897Z Non-cacheable calls 69 2025-07-17T09:10:05.7613224Z Non-compilation calls 6433 2025-07-17T09:10:05.7613575Z Unsupported compiler calls 0 2025-07-17T09:10:05.7613896Z Average cache write 0.055 s 2025-07-17T09:10:05.7614226Z Average compiler 9.904 s 2025-07-17T09:10:05.7614559Z Average cache read hit 0.036 s 2025-07-17T09:10:05.7614886Z Failed distributed compilations 0 2025-07-17T09:10:05.7615126Z 2025-07-17T09:10:05.7615299Z Non-cacheable reasons: 2025-07-17T09:10:05.7615570Z -E 47 2025-07-17T09:10:05.7615974Z unknown source language 22 2025-07-17T09:10:05.7616201Z 2025-07-17T09:10:05.7616449Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-07-17T09:10:05.7616928Z Version (client) 0.10.0 2025-07-17T09:10:05.7617246Z + sccache --stop-server 2025-07-17T09:10:05.7629473Z Stopping sccache server... 2025-07-17T09:10:05.7632283Z Compile requests 6683 2025-07-17T09:10:05.7632622Z Compile requests executed 181 2025-07-17T09:10:05.7632933Z Cache hits 128 2025-07-17T09:10:05.7640719Z Cache hits (C/C++) 128 2025-07-17T09:10:05.7641185Z Cache misses 52 2025-07-17T09:10:05.7641515Z Cache misses (C/C++) 52 2025-07-17T09:10:05.7641849Z Cache hits rate 71.11 % 2025-07-17T09:10:05.7642184Z Cache hits rate (C/C++) 71.11 % 2025-07-17T09:10:05.7642515Z Cache timeouts 0 2025-07-17T09:10:05.7642840Z Cache read errors 0 2025-07-17T09:10:05.7643146Z Forced recaches 0 2025-07-17T09:10:05.7643474Z Cache write errors 0 2025-07-17T09:10:05.7643801Z Cache errors 0 2025-07-17T09:10:05.7644128Z Compilations 52 2025-07-17T09:10:05.7644456Z Compilation failures 1 2025-07-17T09:10:05.7644792Z Non-cacheable compilations 0 2025-07-17T09:10:05.7645127Z Non-cacheable calls 69 2025-07-17T09:10:05.7645570Z Non-compilation calls 6433 2025-07-17T09:10:05.7646073Z Unsupported compiler calls 0 2025-07-17T09:10:05.7646570Z Average cache write 0.055 s 2025-07-17T09:10:05.7646919Z Average compiler 9.904 s 2025-07-17T09:10:05.7647422Z Average cache read hit 0.036 s 2025-07-17T09:10:05.7647909Z Failed distributed compilations 0 2025-07-17T09:10:05.7648132Z 2025-07-17T09:10:05.7648232Z Non-cacheable reasons: 2025-07-17T09:10:05.7648564Z -E 47 2025-07-17T09:10:05.7649054Z unknown source language 22 2025-07-17T09:10:05.7649390Z 2025-07-17T09:10:05.7649860Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-07-17T09:10:05.7650483Z Version (client) 0.10.0 2025-07-17T09:10:05.7650811Z + echo ::endgroup:: 2025-07-17T09:10:05.7651300Z ##[endgroup] 2025-07-17T09:10:05.7651533Z + cleanup_workspace 2025-07-17T09:10:05.7652033Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2025-07-17T09:10:05.7652852Z sudo may print the following warning message that can be ignored. The chown command will still run. 2025-07-17T09:10:05.7653503Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2025-07-17T09:10:05.7653994Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2025-07-17T09:10:05.7654545Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2025-07-17T09:10:05.7655318Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2025-07-17T09:10:05.7655823Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2025-07-17T09:10:07.4819901Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main 2025-07-17T09:10:07.4820410Z with: 2025-07-17T09:10:07.4820653Z benchmark-results-dir: test/test-reports 2025-07-17T09:10:07.4820987Z dry-run: false 2025-07-17T09:10:07.4821231Z schema-version: v3 2025-07-17T09:10:07.4821716Z github-token: *** 2025-07-17T09:10:07.4821939Z env: 2025-07-17T09:10:07.4822159Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:07.4822640Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:07.4823149Z ##[endgroup] 2025-07-17T09:10:07.4849298Z ##[group]Run set -eux 2025-07-17T09:10:07.4849610Z set -eux 2025-07-17T09:10:07.4849978Z python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-07-17T09:10:07.4850490Z  2025-07-17T09:10:07.4850719Z DEVICE_NAME="" 2025-07-17T09:10:07.4850992Z DEVICE_TYPE="" 2025-07-17T09:10:07.4851240Z  2025-07-17T09:10:07.4851526Z if command -v nvidia-smi; then 2025-07-17T09:10:07.4851981Z  # NB: I'm using PyTorch here to get the device name, however, it needs to 2025-07-17T09:10:07.4852588Z  # install the correct version of PyTorch manually for now. Any PyTorch 2025-07-17T09:10:07.4853148Z  # version is fine, I just use 2.7.1 to satify PYPIDEP linter 2025-07-17T09:10:07.4853590Z  python3 -mpip install torch==2.7.1 2025-07-17T09:10:07.4853935Z elif command -v rocminfo; then 2025-07-17T09:10:07.4854391Z  # NB: Installing torch on ROCm runner with pip here causes CI to fail 2025-07-17T09:10:07.4854978Z  # with a memoryview is too large error only on MI300 runners. Is pip 2025-07-17T09:10:07.4855654Z  # version on ROCm runner there too old? As a workaround, let's use the 2025-07-17T09:10:07.4856157Z  # GPU device name coming from rocminfo instead 2025-07-17T09:10:07.4856532Z  DEVICE_NAME=rocm 2025-07-17T09:10:07.4857037Z  DEVICE_TYPE=$(rocminfo | grep "Marketing Name" | tail -n1 | awk -F':' '{print $2}' | xargs) 2025-07-17T09:10:07.4857544Z fi 2025-07-17T09:10:07.4857759Z  2025-07-17T09:10:07.4858038Z echo "DEVICE_NAME=$DEVICE_NAME" >> $GITHUB_ENV 2025-07-17T09:10:07.4858464Z echo "DEVICE_TYPE=$DEVICE_TYPE" >> $GITHUB_ENV 2025-07-17T09:10:07.4867864Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:07.4868260Z env: 2025-07-17T09:10:07.4868485Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:07.4868954Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:07.4869568Z ##[endgroup] 2025-07-17T09:10:07.4900454Z + python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-07-17T09:10:07.7595976Z Defaulting to user installation because normal site-packages is not writeable 2025-07-17T09:10:08.8026894Z Collecting boto3==1.35.33 2025-07-17T09:10:08.8187954Z Downloading boto3-1.35.33-py3-none-any.whl (139 kB) 2025-07-17T09:10:09.1198514Z Collecting psutil==7.0.0 2025-07-17T09:10:09.1233872Z Downloading psutil-7.0.0-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (277 kB) 2025-07-17T09:10:09.1500314Z Collecting pynvml==12.0.0 2025-07-17T09:10:09.1528328Z Downloading pynvml-12.0.0-py3-none-any.whl (26 kB) 2025-07-17T09:10:09.1961380Z Collecting s3transfer<0.11.0,>=0.10.0 2025-07-17T09:10:09.1990510Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2025-07-17T09:10:09.2039818Z 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) 2025-07-17T09:10:10.3545150Z Collecting botocore<1.36.0,>=1.35.33 2025-07-17T09:10:10.3579083Z Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB) 2025-07-17T09:10:10.5470790Z Collecting nvidia-ml-py<13.0.0a0,>=12.0.0 2025-07-17T09:10:10.5500942Z Downloading nvidia_ml_py-12.575.51-py3-none-any.whl (47 kB) 2025-07-17T09:10:10.5587920Z 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) 2025-07-17T09:10:10.5594657Z 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) 2025-07-17T09:10:10.7451624Z 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) 2025-07-17T09:10:10.8760516Z Installing collected packages: botocore, s3transfer, nvidia-ml-py, pynvml, psutil, boto3 2025-07-17T09:10:11.3749905Z Attempting uninstall: nvidia-ml-py 2025-07-17T09:10:11.3751196Z Found existing installation: nvidia-ml-py 11.525.84 2025-07-17T09:10:11.3764740Z Uninstalling nvidia-ml-py-11.525.84: 2025-07-17T09:10:11.3951838Z Successfully uninstalled nvidia-ml-py-11.525.84 2025-07-17T09:10:11.4506370Z Attempting uninstall: psutil 2025-07-17T09:10:11.4506923Z Found existing installation: psutil 5.9.1 2025-07-17T09:10:11.4573296Z Uninstalling psutil-5.9.1: 2025-07-17T09:10:11.4579499Z Successfully uninstalled psutil-5.9.1 2025-07-17T09:10:11.6204493Z Successfully installed boto3-1.35.33 botocore-1.35.99 nvidia-ml-py-12.575.51 psutil-7.0.0 pynvml-12.0.0 s3transfer-0.10.4 2025-07-17T09:10:11.7164310Z + DEVICE_NAME= 2025-07-17T09:10:11.7164598Z + DEVICE_TYPE= 2025-07-17T09:10:11.7164850Z + command -v nvidia-smi 2025-07-17T09:10:11.7165116Z + command -v rocminfo 2025-07-17T09:10:11.7165367Z + echo DEVICE_NAME= 2025-07-17T09:10:11.7166409Z + echo DEVICE_TYPE= 2025-07-17T09:10:11.7194342Z ##[group]Run set -eux 2025-07-17T09:10:11.7194622Z set -eux 2025-07-17T09:10:11.7194841Z  2025-07-17T09:10:11.7195104Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-07-17T09:10:11.7195476Z  echo "Missing github-token input" 2025-07-17T09:10:11.7195791Z  exit 1 2025-07-17T09:10:11.7196037Z fi 2025-07-17T09:10:11.7202300Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:11.7202678Z env: 2025-07-17T09:10:11.7202909Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:11.7203390Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:11.7203913Z DEVICE_NAME: 2025-07-17T09:10:11.7204135Z DEVICE_TYPE: 2025-07-17T09:10:11.7204619Z GITHUB_TOKEN: *** 2025-07-17T09:10:11.7204859Z ##[endgroup] 2025-07-17T09:10:11.7231471Z + [[ -z *** ]] 2025-07-17T09:10:11.7282157Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-07-17T09:10:11.7282704Z with: 2025-07-17T09:10:11.7283078Z github-token: *** 2025-07-17T09:10:11.7283305Z env: 2025-07-17T09:10:11.7283524Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:11.7284015Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:11.7284525Z DEVICE_NAME: 2025-07-17T09:10:11.7284759Z DEVICE_TYPE: 2025-07-17T09:10:11.7285025Z ##[endgroup] 2025-07-17T09:10:11.7307677Z ##[group]Run set -eux 2025-07-17T09:10:11.7307954Z set -eux 2025-07-17T09:10:11.7308186Z  2025-07-17T09:10:11.7308669Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-07-17T09:10:11.7314632Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:11.7315019Z env: 2025-07-17T09:10:11.7315267Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:11.7315737Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:11.7316257Z DEVICE_NAME: 2025-07-17T09:10:11.7316494Z DEVICE_TYPE: 2025-07-17T09:10:11.7316895Z GITHUB_TOKEN: *** 2025-07-17T09:10:11.7317122Z ##[endgroup] 2025-07-17T09:10:11.7340642Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 16337959907 i-075a75ec57b65bb84 2025-07-17T09:10:14.6638290Z setting job-id=46154839625 2025-07-17T09:10:14.6638855Z setting job-name=linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T09:10:14.6756277Z ##[group]Run set -eux 2025-07-17T09:10:14.6756556Z set -eux 2025-07-17T09:10:14.6756787Z  2025-07-17T09:10:14.6757186Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-07-17T09:10:14.6757715Z  --schema-version "${SCHEMA_VERSION}" \ 2025-07-17T09:10:14.6758078Z  --repo "${REPO}" \ 2025-07-17T09:10:14.6758391Z  --head-branch "${HEAD_BRANCH}" \ 2025-07-17T09:10:14.6758723Z  --head-sha "${HEAD_SHA}" \ 2025-07-17T09:10:14.6759081Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-07-17T09:10:14.6759535Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-07-17T09:10:14.6759879Z  --job-id "${JOB_ID}" \ 2025-07-17T09:10:14.6760176Z  --job-name "${JOB_NAME}" 2025-07-17T09:10:14.6766063Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:14.6766453Z env: 2025-07-17T09:10:14.6766664Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:14.6767141Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:14.6767668Z DEVICE_NAME: 2025-07-17T09:10:14.6767888Z DEVICE_TYPE: 2025-07-17T09:10:14.6768123Z SCHEMA_VERSION: v3 2025-07-17T09:10:14.6768381Z REPO: pytorch/pytorch 2025-07-17T09:10:14.6768640Z HEAD_BRANCH: refs/heads/main 2025-07-17T09:10:14.6768973Z HEAD_SHA: a38f433be2e94a64b095a44ba39879d02d0c2316 2025-07-17T09:10:14.6769334Z WORKFLOW_RUN_ID: 16337959907 2025-07-17T09:10:14.6769622Z RUN_ATTEMPT: 1 2025-07-17T09:10:14.6769844Z JOB_ID: 46154839625 2025-07-17T09:10:14.6770235Z JOB_NAME: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T09:10:14.6770698Z ##[endgroup] 2025-07-17T09:10:14.6800811Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/main --head-sha a38f433be2e94a64b095a44ba39879d02d0c2316 --workflow-id 16337959907 --run-attempt 1 --job-id 46154839625 --job-name 'linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)' 2025-07-17T09:10:14.7123035Z ##[group]Run set -eux 2025-07-17T09:10:14.7123348Z set -eux 2025-07-17T09:10:14.7123584Z  2025-07-17T09:10:14.7124032Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_runners_info.py" 2025-07-17T09:10:14.7129743Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:14.7130221Z env: 2025-07-17T09:10:14.7130433Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:14.7130916Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:14.7131442Z DEVICE_NAME: 2025-07-17T09:10:14.7131680Z DEVICE_TYPE: 2025-07-17T09:10:14.7131899Z ##[endgroup] 2025-07-17T09:10:14.7155557Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_runners_info.py 2025-07-17T09:10:14.7550194Z INFO:root:Fail to import torch to get the device name 2025-07-17T09:10:14.7650518Z ##[group]Run set -eux 2025-07-17T09:10:14.7650796Z set -eux 2025-07-17T09:10:14.7651028Z  2025-07-17T09:10:14.7651276Z # TODO (huydhn): Implement this part 2025-07-17T09:10:14.7651677Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-07-17T09:10:14.7657092Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:14.7657487Z env: 2025-07-17T09:10:14.7657714Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:14.7658194Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:14.7658713Z DEVICE_NAME: 2025-07-17T09:10:14.7658956Z DEVICE_TYPE: 2025-07-17T09:10:14.7659173Z ##[endgroup] 2025-07-17T09:10:14.7681024Z + echo 'dependencies={}' 2025-07-17T09:10:14.7707851Z ##[group]Run set -eux 2025-07-17T09:10:14.7708143Z set -eux 2025-07-17T09:10:14.7708378Z  2025-07-17T09:10:14.7708653Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-07-17T09:10:14.7709120Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-07-17T09:10:14.7709709Z  # We don't want the job to fail if the directory doesn't exist 2025-07-17T09:10:14.7710119Z  exit 0 2025-07-17T09:10:14.7710353Z fi 2025-07-17T09:10:14.7710559Z  2025-07-17T09:10:14.7710805Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-07-17T09:10:14.7711311Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-07-17T09:10:14.7711968Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-07-17T09:10:14.7712415Z  --metadata "${BENCHMARK_METADATA}" \ 2025-07-17T09:10:14.7712792Z  --runners "${RUNNER_INFO}" \ 2025-07-17T09:10:14.7713155Z  --dependencies "${DEPENDENCIES}" \ 2025-07-17T09:10:14.7713483Z  --dry-run 2025-07-17T09:10:14.7713737Z else 2025-07-17T09:10:14.7714129Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-07-17T09:10:14.7714694Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-07-17T09:10:14.7715134Z  --metadata "${BENCHMARK_METADATA}" \ 2025-07-17T09:10:14.7715500Z  --runners "${RUNNER_INFO}" \ 2025-07-17T09:10:14.7715854Z  --dependencies "${DEPENDENCIES}" 2025-07-17T09:10:14.7716175Z fi 2025-07-17T09:10:14.7721285Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:14.7721682Z env: 2025-07-17T09:10:14.7721898Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:14.7722382Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:14.7722902Z DEVICE_NAME: 2025-07-17T09:10:14.7723130Z DEVICE_TYPE: 2025-07-17T09:10:14.7723395Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-07-17T09:10:14.7723720Z DRY_RUN: false 2025-07-17T09:10:14.7725029Z BENCHMARK_METADATA: {"timestamp": 1752743414, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "a38f433be2e94a64b095a44ba39879d02d0c2316", "workflow_id": 16337959907, "run_attempt": 1, "job_id": 46154839625} 2025-07-17T09:10:14.7726777Z RUNNER_INFO: [{"cpu_info": "x86_64", "cpu_count": 16, "avail_mem_in_gb": 30, "extra_info": {"hostname": "ip-10-0-72-236.ec2.internal"}, "name": "", "type": ""}] 2025-07-17T09:10:14.7727554Z DEPENDENCIES: {} 2025-07-17T09:10:14.7727870Z ##[endgroup] 2025-07-17T09:10:14.7764045Z + [[ ! -d test/test-reports ]] 2025-07-17T09:10:14.7764358Z + [[ false == \t\r\u\e ]] 2025-07-17T09:10:14.7767484Z + 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": 1752743414, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "a38f433be2e94a64b095a44ba39879d02d0c2316", "workflow_id": 16337959907, "run_attempt": 1, "job_id": 46154839625}' --runners '[{"cpu_info": "x86_64", "cpu_count": 16, "avail_mem_in_gb": 30, "extra_info": {"hostname": "ip-10-0-72-236.ec2.internal"}, "name": "", "type": ""}]' --dependencies '{}' 2025-07-17T09:10:14.9125512Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'lazy/test_ts_opinfo'}], 'excluded': []} from test/test-reports/td_exclusions-0a3d43ad3b25c7703484.json is not a benchmark record, skipping 2025-07-17T09:10:14.9127537Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-07-17T09:10:14.9206615Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'functorch/test_eager_transforms'}, {'test_file': 'functorch/test_ops'}, {'test_file': 'inductor/test_aot_inductor_arrayref'}, {'test_file': 'test_transformers'}, {'test_file': 'dynamo/cpython/3_13/test_generators'}, {'test_file': 'dynamo/cpython/3_13/test_contextlib'}, {'test_file': 'dynamo/cpython/3_13/test_raise'}, {'test_file': 'dynamo/cpython/3_13/test_exceptions'}, {'test_file': 'dynamo/cpython/3_13/test_sys'}, {'test_file': 'test_jit_fuser_te'}, {'test_file': 'test_ci_sanity_check_fail'}, {'test_file': 'inductor/test_flex_decoding'}, {'test_file': 'inductor/test_cudagraph_trees'}, {'test_file': 'inductor/test_gpu_cpp_wrapper'}, {'test_file': 'inductor/test_compile_subprocess'}, {'test_file': 'inductor/test_alignment'}, {'test_file': 'inductor/test_inplace_padding'}, {'test_file': 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{'test_file': 'test_scatter_gather_ops'}, {'test_file': 'nn/test_pooling'}, {'test_file': 'profiler/test_memory_profiler'}, {'test_file': 'test_unary_ufuncs'}, {'test_file': 'nn/test_init'}, {'test_file': 'test_function_schema'}, {'test_file': 'test_cuda_multigpu'}, {'test_file': 'test_mobile_optimizer'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_ctors'}, {'test_file': 'test_sparse'}, {'test_file': 'torch_np/test_reductions'}, {'test_file': 'profiler/test_profiler_tree'}, {'test_file': 'nn/test_convolution'}, {'test_file': 'test_type_promotion'}, {'test_file': 'dynamo/test_python_autograd'}, {'test_file': 'test_jit_autocast'}, {'test_file': 'profiler/test_record_function'}, {'test_file': 'test_prims'}, {'test_file': 'lazy/test_reuse_ir'}, {'test_file': 'profiler/test_execution_trace'}, {'test_file': 'profiler/test_torch_tidy'}, {'test_file': 'test_autoload_disable'}, {'test_file': 'test_autoload_enable'}, {'test_file': 'test_cpp_extensions_aot_ninja'}, {'test_file': 'test_cpp_extensions_aot_no_ninja'}, {'test_file': 'test_rename_privateuse1_to_existing_device'}], 'excluded': []} from test/test-reports/td_exclusions-855b639637c0470447a9.json is not a benchmark record, skipping 2025-07-17T09:10:14.9284631Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-07-17T09:10:14.9288629Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/Dict_test'}, {'test_file': 'cpp/Dimname_test'}, {'test_file': 'cpp/NamedTensor_test'}, {'test_file': 'cpp/apply_utils_test'}, {'test_file': 'cpp/atest'}, {'test_file': 'cpp/basic'}, {'test_file': 'cpp/broadcast_test'}, {'test_file': 'cpp/cpu_generator_test'}, {'test_file': 'cpp/dlconvertor_test'}, {'test_file': 'cpp/extension_backend_test'}, {'test_file': 'cpp/lazy_tensor_test'}, {'test_file': 'cpp/legacy_vmap_test'}, {'test_file': 'cpp/native_test'}, {'test_file': 'cpp/operators_test'}, {'test_file': 'cpp/scalar_tensor_test'}, {'test_file': 'cpp/scalar_test'}, {'test_file': 'cpp/tensor_iterator_test'}, {'test_file': 'cpp/undefined_tensor_test'}, {'test_file': 'cpp/wrapdim_test'}], 'excluded': []} from test/test-reports/td_exclusions-53b909c2993574149791.json is not a benchmark record, skipping 2025-07-17T09:10:14.9293262Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-07-17T09:10:14.9294947Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/test_api'}], 'excluded': []} from test/test-reports/td_exclusions-fd7d5d44ef48a6fbf96c.json is not a benchmark record, skipping 2025-07-17T09:10:14.9296676Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-07-17T09:10:14.9298371Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/test_tensorexpr'}], 'excluded': []} from test/test-reports/td_exclusions-8f5693616ee2fdab69b6.json is not a benchmark record, skipping 2025-07-17T09:10:14.9300049Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-07-17T09:10:14.9301834Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/static_runtime_test'}], 'excluded': []} from test/test-reports/td_exclusions-5aeeca6333acc44f6686.json is not a benchmark record, skipping 2025-07-17T09:10:14.9303542Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-07-17T09:10:14.9354486Z ##[group]Run cat test/**/*_toprint.log || true 2025-07-17T09:10:14.9354890Z cat test/**/*_toprint.log || true 2025-07-17T09:10:14.9360541Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:14.9360915Z env: 2025-07-17T09:10:14.9361141Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:14.9361623Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:14.9362145Z DEVICE_NAME: 2025-07-17T09:10:14.9362383Z DEVICE_TYPE: 2025-07-17T09:10:14.9362611Z ##[endgroup] 2025-07-17T09:10:14.9448103Z cat: 'test/**/*_toprint.log': No such file or directory 2025-07-17T09:10:14.9482871Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2025-07-17T09:10:14.9483240Z kill "$MONITOR_SCRIPT_PID" 2025-07-17T09:10:14.9488678Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:14.9489061Z env: 2025-07-17T09:10:14.9489281Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:14.9489747Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:14.9490266Z DEVICE_NAME: 2025-07-17T09:10:14.9490497Z DEVICE_TYPE: 2025-07-17T09:10:14.9490738Z MONITOR_SCRIPT_PID: 44041 2025-07-17T09:10:14.9491000Z ##[endgroup] 2025-07-17T09:10:14.9639401Z Prepare all required actions 2025-07-17T09:10:14.9639900Z Getting action download info 2025-07-17T09:10:15.1480909Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-07-17T09:10:15.3981618Z Download action repository 'actions/upload-artifact@v4' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-07-17T09:10:15.8222702Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-07-17T09:10:15.8223076Z with: 2025-07-17T09:10:15.8223381Z file-suffix: test-default-1-5-linux.4xlarge_46154839625 2025-07-17T09:10:15.8223768Z s3-bucket: gha-artifacts 2025-07-17T09:10:15.8224031Z env: 2025-07-17T09:10:15.8224251Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:15.8224730Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:15.8225245Z DEVICE_NAME: 2025-07-17T09:10:15.8225478Z DEVICE_TYPE: 2025-07-17T09:10:15.8225704Z ##[endgroup] 2025-07-17T09:10:15.8252710Z ##[group]Run # Remove any previous test jsons if they exist 2025-07-17T09:10:15.8253187Z # Remove any previous test jsons if they exist 2025-07-17T09:10:15.8253567Z rm -f test-jsons-*.zip 2025-07-17T09:10:15.8254092Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2025-07-17T09:10:15.8260102Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:15.8260488Z env: 2025-07-17T09:10:15.8260712Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:15.8261193Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:15.8261700Z DEVICE_NAME: 2025-07-17T09:10:15.8261931Z DEVICE_TYPE: 2025-07-17T09:10:15.8262237Z FILE_SUFFIX: test-default-1-5-linux.4xlarge_46154839625 2025-07-17T09:10:15.8262607Z ##[endgroup] 2025-07-17T09:10:15.8428570Z adding: test/test-reports/td_exclusions-0a3d43ad3b25c7703484.json (deflated 16%) 2025-07-17T09:10:15.8432901Z adding: test/test-reports/td_exclusions-855b639637c0470447a9.json (deflated 82%) 2025-07-17T09:10:15.8433566Z adding: test/test-reports/td_exclusions-53b909c2993574149791.json (deflated 73%) 2025-07-17T09:10:15.8434220Z adding: test/test-reports/td_exclusions-fd7d5d44ef48a6fbf96c.json (deflated 18%) 2025-07-17T09:10:15.8434871Z adding: test/test-reports/td_exclusions-8f5693616ee2fdab69b6.json (deflated 16%) 2025-07-17T09:10:15.8435528Z adding: test/test-reports/td_exclusions-5aeeca6333acc44f6686.json (deflated 14%) 2025-07-17T09:10:15.8459164Z ##[group]Run # Remove any previous test reports if they exist 2025-07-17T09:10:15.8459643Z # Remove any previous test reports if they exist 2025-07-17T09:10:15.8460031Z rm -f test-reports-*.zip 2025-07-17T09:10:15.8460517Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2025-07-17T09:10:15.8466058Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:15.8466435Z env: 2025-07-17T09:10:15.8466651Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:15.8467137Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:15.8467639Z DEVICE_NAME: 2025-07-17T09:10:15.8467868Z DEVICE_TYPE: 2025-07-17T09:10:15.8468175Z FILE_SUFFIX: test-default-1-5-linux.4xlarge_46154839625 2025-07-17T09:10:15.8468528Z ##[endgroup] 2025-07-17T09:10:15.8582890Z adding: test/test-reports/python-pytest/lazy.test_ts_opinfo/lazy.test_ts_opinfo-afd55208d4d66d22.xml (deflated 28%) 2025-07-17T09:10:15.8586354Z adding: test/test-reports/python-pytest/lazy.test_ts_opinfo/lazy.test_ts_opinfo-3eeb231a55c27bfa.xml (deflated 94%) 2025-07-17T09:10:15.8587983Z adding: test/test-reports/python-pytest/inductor.test_distributed_patterns/inductor.test_distributed_patterns-186a3da0c202fa1e.xml (deflated 85%) 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2025-07-17T09:10:16.1189134Z adding: test/test-reports/dynamo.test_autograd_function_1.1_e4d1aebfa4bdc9e3_.log (deflated 82%) 2025-07-17T09:10:16.1195619Z adding: test/test-reports/inductor.test_codecache_1.1_d670c339e85c0b0e_.log (deflated 93%) 2025-07-17T09:10:16.1196588Z adding: test/test-reports/inductor.test_cooperative_reductions_1.1_37e410db41b17607_.log (deflated 35%) 2025-07-17T09:10:16.1201380Z adding: test/test-reports/inductor.test_torchinductor_opinfo_22.26_7c1330f6228b9017_.log (deflated 90%) 2025-07-17T09:10:16.1205057Z adding: test/test-reports/inductor.test_cpu_select_algorithm_1.16_e355f40c1b25b3a8_.log (deflated 92%) 2025-07-17T09:10:16.1209088Z adding: test/test-reports/inductor.test_cpu_select_algorithm_5.16_1f2aa32e28c18fe0_.log (deflated 92%) 2025-07-17T09:10:16.1209990Z adding: test/test-reports/inductor.test_halide_1.1_9be3fc4cd2806227_.log (deflated 35%) 2025-07-17T09:10:16.1210889Z adding: test/test-reports/inductor.test_inplacing_pass_1.1_0183978a75852fd5_.log (deflated 35%) 2025-07-17T09:10:16.1211811Z adding: test/test-reports/nn.test_multihead_attention_1.1_db574de786551974_.log (deflated 82%) 2025-07-17T09:10:16.1212703Z adding: test/test-reports/inductor.test_binary_folding_1.1_a243fb23293066eb_.log (deflated 59%) 2025-07-17T09:10:16.1213735Z adding: test/test-reports/inductor.test_torchinductor_codegen_config_overrides_1.1_d17f06de0a3121f7_.log (deflated 63%) 2025-07-17T09:10:16.1218649Z adding: test/test-reports/torch_np.numpy_tests.core.test_numeric_1.1_09c8a4f89a1cd575_.log (deflated 90%) 2025-07-17T09:10:16.1219588Z adding: test/test-reports/dynamo.test_backward_higher_order_ops_1.1_dcd30f2b5e5fc275_.log (deflated 70%) 2025-07-17T09:10:16.1220540Z adding: test/test-reports/dynamo.test_optimizers_1.1_bf60cad60664c806_.log (deflated 55%) 2025-07-17T09:10:16.1221431Z adding: test/test-reports/dynamo.test_graph_deduplication_1.1_0a835b7515fec8fa_.log (deflated 77%) 2025-07-17T09:10:16.1223990Z adding: test/test-reports/distributions.test_constraints_1.1_a0dfdc55f9cf0f28_.log (deflated 91%) 2025-07-17T09:10:16.1228028Z adding: test/test-reports/inductor.test_cpu_select_algorithm_6.16_093a42f34cb844b2_.log (deflated 92%) 2025-07-17T09:10:16.1229023Z adding: test/test-reports/inductor.test_split_cat_fx_aten_passes_1.1_0a8144cf50c2f3a4_.log (deflated 74%) 2025-07-17T09:10:16.1229914Z adding: test/test-reports/profiler.test_cpp_thread_1.1_489c86cd5676da72_.log (deflated 69%) 2025-07-17T09:10:16.1231431Z adding: test/test-reports/dynamo.test_structured_trace_1.1_22758afb51f6ac70_.log (deflated 83%) 2025-07-17T09:10:16.1233553Z adding: test/test-reports/dynamo.test_decorators_1.1_738dbeaafa3a4a70_.log (deflated 83%) 2025-07-17T09:10:16.1234537Z adding: test/test-reports/functorch.test_ac_knapsack_1.1_be0acb6bb68c6b86_.log (deflated 75%) 2025-07-17T09:10:16.1235392Z adding: test/test-reports/dynamo.test_debug_utils_1.1_1e0a55c678dd9236_.log (deflated 55%) 2025-07-17T09:10:16.1236523Z adding: test/test-reports/inductor.test_inductor_freezing_1.1_3dca9b6f164912f5_.log (deflated 79%) 2025-07-17T09:10:16.1237551Z adding: test/test-reports/dynamo.test_sources_1.1_5923625eb360a450_.log (deflated 56%) 2025-07-17T09:10:16.1238333Z adding: test/test-reports/dynamo.test_profiler_1.1_e9c92f56ced84cd9_.log (deflated 70%) 2025-07-17T09:10:16.1239154Z adding: test/test-reports/inductor.test_config_1.1_08c9346be3296f3b_.log (deflated 70%) 2025-07-17T09:10:16.1240030Z adding: test/test-reports/functorch.test_logging_1.1_d6c33045c13cd4f0_.log (deflated 52%) 2025-07-17T09:10:16.1240868Z adding: test/test-reports/dynamo.test_flat_apply_1.1_015ceccfde42ee2a_.log (deflated 60%) 2025-07-17T09:10:16.1241657Z adding: test/test-reports/torch_np.test_indexing_1.1_b194784d0e5ab46e_.log (deflated 62%) 2025-07-17T09:10:16.1242528Z adding: test/test-reports/dynamo.test_deviceguard_1.1_bbe32bb7ad7c639c_.log (deflated 60%) 2025-07-17T09:10:16.1243363Z adding: test/test-reports/lazy.test_step_closures_1.1_8ab27a57ad8e8580_.log (deflated 60%) 2025-07-17T09:10:16.1244241Z adding: test/test-reports/export.test_unflatten_1.1_265ce844b5f46fe6_.log (deflated 78%) 2025-07-17T09:10:16.1247189Z adding: test/test-reports/inductor.test_cpu_select_algorithm_16.16_fef683961e21f6de_.log (deflated 92%) 2025-07-17T09:10:16.1248101Z adding: test/test-reports/inductor.test_padding_1.1_51559056c49bee6d_.log (deflated 35%) 2025-07-17T09:10:16.1248908Z adding: test/test-reports/dynamo.test_torchrec_1.1_c987a4b4e03c7879_.log (deflated 50%) 2025-07-17T09:10:16.1265739Z adding: test/test-reports/torch_np.test_reductions_1.1_399f636f38a9dcb5_.log (deflated 96%) 2025-07-17T09:10:16.1266615Z adding: test/test-reports/profiler.test_profiler_tree_1.1_b007f66bd362f6f1_.log (deflated 75%) 2025-07-17T09:10:16.1275946Z adding: test/test-reports/test_type_promotion_1.1_abfbb14cc2344323_.log (deflated 94%) 2025-07-17T09:10:16.1276805Z adding: test/test-reports/dynamo.test_python_autograd_1.1_1cfc287e10e58d9b_.log (deflated 63%) 2025-07-17T09:10:16.1277708Z adding: test/test-reports/profiler.test_record_function_1.1_cba6b47811a187f7_.log (deflated 63%) 2025-07-17T09:10:16.1278636Z adding: test/test-reports/profiler.test_execution_trace_1.1_26e8a4f4e944f685_.log (deflated 79%) 2025-07-17T09:10:16.1294392Z adding: test/test-reports/profiler.test_torch_tidy_1.1_cfa97be53beff433_.log (deflated 75%) 2025-07-17T09:10:16.1295201Z adding: test/test-reports/inductor.test_fused_attention_1.1_2dc55e20e528441d_.log (deflated 88%) 2025-07-17T09:10:16.1296135Z adding: test/test-reports/test_rename_privateuse1_to_existing_device_1.1_b8668c84b100d097_.log (deflated 55%) 2025-07-17T09:10:16.1296938Z adding: test/test-reports/cpp.NamedTensor_test_1.1_798d128c13d925f4_.log (deflated 73%) 2025-07-17T09:10:16.1297654Z adding: test/test-reports/cpp.apply_utils_test_1.1_33f3bddc84ab845b_.log (deflated 73%) 2025-07-17T09:10:16.1298362Z adding: test/test-reports/cpp.cpu_generator_test_1.1_4521a04d6254d18d_.log (deflated 73%) 2025-07-17T09:10:16.1299071Z adding: test/test-reports/cpp.dlconvertor_test_1.1_74fabfa3c571062f_.log (deflated 73%) 2025-07-17T09:10:16.1299806Z adding: test/test-reports/cpp.extension_backend_test_1.1_0d9d9835df7d905c_.log (deflated 73%) 2025-07-17T09:10:16.1300620Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_b0520b5fc91e2bd1_.log (deflated 73%) 2025-07-17T09:10:16.1301313Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_61387141dffb4f8f_.log (deflated 73%) 2025-07-17T09:10:16.1302018Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_b8ef22b80043af0c_.log (deflated 73%) 2025-07-17T09:10:16.1302749Z adding: test/test-reports/cpp.tensor_iterator_test_1.1_6756af664dff35ce_.log (deflated 73%) 2025-07-17T09:10:16.1303484Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_59685f5f52998e60_.log (deflated 73%) 2025-07-17T09:10:16.1304294Z adding: test/test-reports/cpp.NamedTensor_test_1.1_282cfd162e1f9158_.log (deflated 77%) 2025-07-17T09:10:16.1304993Z adding: test/test-reports/cpp.apply_utils_test_1.1_c763681e8b0ead47_.log (deflated 75%) 2025-07-17T09:10:16.1305678Z adding: test/test-reports/cpp.broadcast_test_1.1_6f7ec74c92c027cb_.log (deflated 72%) 2025-07-17T09:10:16.1306386Z adding: test/test-reports/cpp.cpu_generator_test_1.1_dcd50f4b86570eb6_.log (deflated 80%) 2025-07-17T09:10:16.1307101Z adding: test/test-reports/cpp.dlconvertor_test_1.1_0a8745fe81b3f2fd_.log (deflated 75%) 2025-07-17T09:10:16.1307838Z adding: test/test-reports/cpp.extension_backend_test_1.1_30a9ba5ddb63e5e3_.log (deflated 72%) 2025-07-17T09:10:16.1308555Z adding: test/test-reports/cpp.lazy_tensor_test_1.1_c5345901cc9bb7ef_.log (deflated 72%) 2025-07-17T09:10:16.1309250Z adding: test/test-reports/cpp.legacy_vmap_test_1.1_4e0e400dcbbc9614_.log (deflated 82%) 2025-07-17T09:10:16.1309932Z adding: test/test-reports/cpp.native_test_1.1_1aee2a06ce5c56bf_.log (deflated 72%) 2025-07-17T09:10:16.1310609Z adding: test/test-reports/cpp.operators_test_1.1_50151dc500cd5a0f_.log (deflated 73%) 2025-07-17T09:10:16.1311314Z adding: test/test-reports/cpp.scalar_tensor_test_1.1_aec55a84f8a26207_.log (deflated 74%) 2025-07-17T09:10:16.1312050Z adding: test/test-reports/cpp.scalar_test_1.1_2c14dfe410c68aa1_.log (deflated 73%) 2025-07-17T09:10:16.1312762Z adding: test/test-reports/cpp.undefined_tensor_test_1.1_0937195d2b70b399_.log (deflated 72%) 2025-07-17T09:10:16.1313451Z adding: test/test-reports/cpp.wrapdim_test_1.1_9b17c23e4206b312_.log (deflated 72%) 2025-07-17T09:10:16.1314155Z adding: test/test-reports/cpp.tensor_iterator_test_1.1_614d0f516d39b12f_.log (deflated 88%) 2025-07-17T09:10:16.1314845Z adding: test/test-reports/cpp.test_api_1.1_2cc7cdc748cad679_.log (deflated 73%) 2025-07-17T09:10:16.1328730Z adding: test/test-reports/cpp.test_api_1.1_66722138e76d66e9_.log (deflated 93%) 2025-07-17T09:10:16.1329399Z adding: test/test-reports/cpp.test_tensorexpr_1.1_a4dbb42aaf14e4a1_.log (deflated 73%) 2025-07-17T09:10:16.1352603Z adding: test/test-reports/cpp.test_tensorexpr_1.1_851cb2d2e33d8fa3_.log (deflated 94%) 2025-07-17T09:10:16.1353330Z adding: test/test-reports/cpp.static_runtime_test_1.1_0f7e312418963635_.log (deflated 73%) 2025-07-17T09:10:16.1386265Z ##[group]Run # Remove any previous debugging artifacts if they exist 2025-07-17T09:10:16.1386800Z # Remove any previous debugging artifacts if they exist 2025-07-17T09:10:16.1387207Z rm -f debug-*.zip 2025-07-17T09:10:16.1387491Z if [ -d 'test/debug' ]; then 2025-07-17T09:10:16.1387836Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2025-07-17T09:10:16.1388177Z fi 2025-07-17T09:10:16.1393357Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:16.1393724Z env: 2025-07-17T09:10:16.1393932Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:16.1394411Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:16.1394918Z DEVICE_NAME: 2025-07-17T09:10:16.1395139Z DEVICE_TYPE: 2025-07-17T09:10:16.1395438Z FILE_SUFFIX: test-default-1-5-linux.4xlarge_46154839625 2025-07-17T09:10:16.1395804Z ##[endgroup] 2025-07-17T09:10:16.1477694Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-07-17T09:10:16.1478086Z with: 2025-07-17T09:10:16.1478306Z s3-bucket: gha-artifacts 2025-07-17T09:10:16.1478618Z s3-prefix: pytorch/pytorch/16337959907/1/artifact 2025-07-17T09:10:16.1478970Z retention-days: 14 2025-07-17T09:10:16.1479218Z if-no-files-found: warn 2025-07-17T09:10:16.1479476Z path: test-jsons-*.zip 2025-07-17T09:10:16.1479723Z name: artifact 2025-07-17T09:10:16.1479941Z region: us-east-1 2025-07-17T09:10:16.1480148Z env: 2025-07-17T09:10:16.1480355Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:16.1480819Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:16.1481318Z DEVICE_NAME: 2025-07-17T09:10:16.1481621Z DEVICE_TYPE: 2025-07-17T09:10:16.1481842Z ##[endgroup] 2025-07-17T09:10:16.4928792Z NOTE: s3-prefix specified, ignoring name parameter 2025-07-17T09:10:16.4929360Z With the provided path, there will be 1 file uploaded 2025-07-17T09:10:16.4929837Z Uploading to s3 prefix: pytorch/pytorch/16337959907/1/artifact 2025-07-17T09:10:16.4976962Z Starting upload of test-jsons-test-default-1-5-linux.4xlarge_46154839625.zip 2025-07-17T09:10:16.6849224Z Finished upload of test-jsons-test-default-1-5-linux.4xlarge_46154839625.zip 2025-07-17T09:10:16.7040390Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-07-17T09:10:16.7040739Z with: 2025-07-17T09:10:16.7040953Z s3-bucket: gha-artifacts 2025-07-17T09:10:16.7041281Z s3-prefix: pytorch/pytorch/16337959907/1/artifact 2025-07-17T09:10:16.7041641Z retention-days: 14 2025-07-17T09:10:16.7041901Z if-no-files-found: error 2025-07-17T09:10:16.7042172Z path: test-reports-*.zip 2025-07-17T09:10:16.7042439Z name: artifact 2025-07-17T09:10:16.7042687Z region: us-east-1 2025-07-17T09:10:16.7042908Z env: 2025-07-17T09:10:16.7043123Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:16.7043607Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:16.7044205Z DEVICE_NAME: 2025-07-17T09:10:16.7044440Z DEVICE_TYPE: 2025-07-17T09:10:16.7044671Z ##[endgroup] 2025-07-17T09:10:17.0213208Z NOTE: s3-prefix specified, ignoring name parameter 2025-07-17T09:10:17.0213711Z With the provided path, there will be 1 file uploaded 2025-07-17T09:10:17.0214177Z Uploading to s3 prefix: pytorch/pytorch/16337959907/1/artifact 2025-07-17T09:10:17.0259991Z Starting upload of test-reports-test-default-1-5-linux.4xlarge_46154839625.zip 2025-07-17T09:10:17.2313036Z Finished upload of test-reports-test-default-1-5-linux.4xlarge_46154839625.zip 2025-07-17T09:10:17.2503622Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-07-17T09:10:17.2503971Z with: 2025-07-17T09:10:17.2504185Z s3-bucket: gha-artifacts 2025-07-17T09:10:17.2504536Z s3-prefix: pytorch/pytorch/16337959907/1/artifact 2025-07-17T09:10:17.2504895Z retention-days: 14 2025-07-17T09:10:17.2505158Z if-no-files-found: ignore 2025-07-17T09:10:17.2505422Z path: logs-*.zip 2025-07-17T09:10:17.2505669Z name: artifact 2025-07-17T09:10:17.2505903Z region: us-east-1 2025-07-17T09:10:17.2506122Z env: 2025-07-17T09:10:17.2506346Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:17.2506827Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:17.2507333Z DEVICE_NAME: 2025-07-17T09:10:17.2507562Z DEVICE_TYPE: 2025-07-17T09:10:17.2507790Z ##[endgroup] 2025-07-17T09:10:17.5697927Z NOTE: s3-prefix specified, ignoring name parameter 2025-07-17T09:10:17.5698768Z With the provided path, there will be 1 file uploaded 2025-07-17T09:10:17.5699251Z Uploading to s3 prefix: pytorch/pytorch/16337959907/1/artifact 2025-07-17T09:10:17.5744724Z Starting upload of logs-test-default-1-5-linux.4xlarge_46154839625.zip 2025-07-17T09:10:17.7593818Z Finished upload of logs-test-default-1-5-linux.4xlarge_46154839625.zip 2025-07-17T09:10:17.7786209Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-07-17T09:10:17.7786552Z with: 2025-07-17T09:10:17.7786794Z s3-bucket: gha-artifacts 2025-07-17T09:10:17.7787151Z s3-prefix: pytorch/pytorch/16337959907/1/artifact 2025-07-17T09:10:17.7787597Z retention-days: 14 2025-07-17T09:10:17.7787861Z if-no-files-found: ignore 2025-07-17T09:10:17.7788140Z path: debug-*.zip 2025-07-17T09:10:17.7788381Z name: artifact 2025-07-17T09:10:17.7788607Z region: us-east-1 2025-07-17T09:10:17.7788837Z env: 2025-07-17T09:10:17.7789054Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:17.7789525Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:17.7790040Z DEVICE_NAME: 2025-07-17T09:10:17.7790272Z DEVICE_TYPE: 2025-07-17T09:10:17.7790488Z ##[endgroup] 2025-07-17T09:10:18.0949920Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2025-07-17T09:10:18.1147277Z ##[group]Run # shellcheck disable=SC2156 2025-07-17T09:10:18.1147678Z # shellcheck disable=SC2156 2025-07-17T09:10:18.1148269Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-07-17T09:10:18.1154395Z shell: /usr/bin/bash -e {0} 2025-07-17T09:10:18.1154688Z env: 2025-07-17T09:10:18.1154897Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:18.1155382Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:18.1155904Z DEVICE_NAME: 2025-07-17T09:10:18.1156135Z DEVICE_TYPE: 2025-07-17T09:10:18.1156348Z ##[endgroup] 2025-07-17T09:10:18.4228732Z Prepare all required actions 2025-07-17T09:10:18.4229150Z Getting action download info 2025-07-17T09:10:18.5260515Z ##[group]Run ./.github/actions/upload-utilization-stats 2025-07-17T09:10:18.5260911Z with: 2025-07-17T09:10:18.5261122Z job_id: 46154839625 2025-07-17T09:10:18.5261519Z job_name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T09:10:18.5261982Z workflow_name: pull 2025-07-17T09:10:18.5262236Z workflow_run_id: 16337959907 2025-07-17T09:10:18.5262520Z workflow_attempt: 1 2025-07-17T09:10:18.5262863Z env: 2025-07-17T09:10:18.5263070Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:18.5263554Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:18.5264079Z DEVICE_NAME: 2025-07-17T09:10:18.5264315Z DEVICE_TYPE: 2025-07-17T09:10:18.5264530Z ##[endgroup] 2025-07-17T09:10:18.5289629Z ##[group]Run echo "workflow_id: 16337959907" 2025-07-17T09:10:18.5290053Z echo "workflow_id: 16337959907" 2025-07-17T09:10:18.5290377Z echo "workflow_attempt: 1" 2025-07-17T09:10:18.5290701Z echo "workflow_Name: pull" 2025-07-17T09:10:18.5291013Z echo "job_id: 46154839625" 2025-07-17T09:10:18.5291493Z echo "job_name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)" 2025-07-17T09:10:18.5292017Z echo "artifact_prefix: " 2025-07-17T09:10:18.5292332Z python3 --version 2025-07-17T09:10:18.5298230Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:18.5298626Z env: 2025-07-17T09:10:18.5298851Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:18.5299333Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:18.5299848Z DEVICE_NAME: 2025-07-17T09:10:18.5300081Z DEVICE_TYPE: 2025-07-17T09:10:18.5300309Z ##[endgroup] 2025-07-17T09:10:18.5323999Z workflow_id: 16337959907 2025-07-17T09:10:18.5324312Z workflow_attempt: 1 2025-07-17T09:10:18.5324569Z workflow_Name: pull 2025-07-17T09:10:18.5324811Z job_id: 46154839625 2025-07-17T09:10:18.5325201Z job_name: linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge) 2025-07-17T09:10:18.5325661Z artifact_prefix: 2025-07-17T09:10:18.5338003Z Python 3.9.23 2025-07-17T09:10:18.5383284Z ##[group]Run nick-fields/retry@v3.0.0 2025-07-17T09:10:18.5383594Z with: 2025-07-17T09:10:18.5383807Z shell: bash 2025-07-17T09:10:18.5384040Z timeout_minutes: 5 2025-07-17T09:10:18.5384274Z max_attempts: 5 2025-07-17T09:10:18.5384517Z retry_wait_seconds: 30 2025-07-17T09:10:18.5385093Z command: set -eu python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3 dataclasses_json==0.6.7 2025-07-17T09:10:18.5385793Z polling_interval_seconds: 1 2025-07-17T09:10:18.5386075Z warning_on_retry: true 2025-07-17T09:10:18.5386348Z continue_on_error: false 2025-07-17T09:10:18.5386613Z env: 2025-07-17T09:10:18.5386818Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:18.5387308Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:18.5387830Z DEVICE_NAME: 2025-07-17T09:10:18.5388048Z DEVICE_TYPE: 2025-07-17T09:10:18.5388277Z ##[endgroup] 2025-07-17T09:10:18.8659529Z Defaulting to user installation because normal site-packages is not writeable 2025-07-17T09:10:18.9403849Z Collecting python-dateutil==2.8.2 2025-07-17T09:10:18.9558439Z Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) 2025-07-17T09:10:19.9181869Z Collecting boto3==1.35.42 2025-07-17T09:10:19.9219251Z Downloading boto3-1.35.42-py3-none-any.whl (139 kB) 2025-07-17T09:10:20.4255484Z Collecting pandas==2.1.3 2025-07-17T09:10:20.4312533Z Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB) 2025-07-17T09:10:20.5627396Z Requirement already satisfied: dataclasses_json==0.6.7 in /home/ec2-user/.local/lib/python3.9/site-packages (0.6.7) 2025-07-17T09:10:20.5652027Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0) 2025-07-17T09:10:20.5697866Z 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) 2025-07-17T09:10:20.5703686Z 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) 2025-07-17T09:10:20.5707763Z 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) 2025-07-17T09:10:21.4561819Z Collecting numpy<2,>=1.22.4 2025-07-17T09:10:21.4598251Z Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB) 2025-07-17T09:10:21.6800209Z Collecting tzdata>=2022.1 2025-07-17T09:10:21.6835025Z Downloading tzdata-2025.2-py2.py3-none-any.whl (347 kB) 2025-07-17T09:10:21.6933042Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1) 2025-07-17T09:10:21.6964492Z Requirement already satisfied: typing-inspect<1,>=0.4.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (0.9.0) 2025-07-17T09:10:21.6968729Z Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (3.26.1) 2025-07-17T09:10:21.7061978Z 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) 2025-07-17T09:10:21.7155362Z Requirement already satisfied: packaging>=17.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses_json==0.6.7) (25.0) 2025-07-17T09:10:21.7258675Z Requirement already satisfied: mypy-extensions>=0.3.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (1.1.0) 2025-07-17T09:10:21.7262810Z Requirement already satisfied: typing-extensions>=3.7.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (4.14.1) 2025-07-17T09:10:21.8903331Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3 2025-07-17T09:10:26.8177196Z Attempting uninstall: boto3 2025-07-17T09:10:26.8178363Z Found existing installation: boto3 1.35.33 2025-07-17T09:10:26.8276910Z Uninstalling boto3-1.35.33: 2025-07-17T09:10:26.8290245Z Successfully uninstalled boto3-1.35.33 2025-07-17T09:10:26.8846900Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.2 2025-07-17T09:10:27.6187913Z Command completed after 1 attempt(s). 2025-07-17T09:10:27.6244376Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-07-17T09:10:27.6245191Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-07-17T09:10:27.6245710Z  --workflow-run-id "16337959907" \ 2025-07-17T09:10:27.6246061Z  --workflow-name "pull" \ 2025-07-17T09:10:27.6246417Z  --workflow-run-attempt "1" \ 2025-07-17T09:10:27.6246735Z  --job-id "46154839625" \ 2025-07-17T09:10:27.6247217Z  --job-name "linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)" \ 2025-07-17T09:10:27.6247726Z  --local-path "" \ 2025-07-17T09:10:27.6248029Z  --artifact-prefix "" 2025-07-17T09:10:27.6253790Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:27.6254182Z env: 2025-07-17T09:10:27.6254410Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:27.6254886Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:27.6255514Z DEVICE_NAME: 2025-07-17T09:10:27.6255751Z DEVICE_TYPE: 2025-07-17T09:10:27.6255970Z ##[endgroup] 2025-07-17T09:10:29.3293252Z repo: pytorch/pytorch 2025-07-17T09:10:29.3293655Z Search for test log in s3 bucket: ossci-utilization 2025-07-17T09:10:29.3294429Z Downloading logs-test-default-1-5-linux.4xlarge_46154839625.zip 2025-07-17T09:10:29.3295071Z extracting usage_log.txt from zip file logs-test-default-1-5-linux.4xlarge_46154839625.zip 2025-07-17T09:10:29.3295679Z Converted Log Model: UtilizationMetadata: 2025-07-17T09:10:29.3297038Z UtilizationMetadata(level='metadata', workflow_id='16337959907', job_id='46154839625', workflow_name='pull', job_name='linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)', usage_collect_interval=1.0, data_model_version=1.0, start_at=1752735905, gpu_count=0, cpu_count=16, gpu_type=None, error=None) 2025-07-17T09:10:29.3298565Z [Db Segments] detected pytest cmd: 17, generated segments: 17 2025-07-17T09:10:29.3298978Z [db model] Peek db timeseries 2025-07-17T09:10:29.3299264Z :{ 2025-07-17T09:10:29.3299467Z "created_at": 1752743429, 2025-07-17T09:10:29.3299764Z "type": "utilization", 2025-07-17T09:10:29.3300026Z "tags": [ 2025-07-17T09:10:29.3300231Z "record" 2025-07-17T09:10:29.3300452Z ], 2025-07-17T09:10:29.3300672Z "time_stamp": 1752735905, 2025-07-17T09:10:29.3300947Z "repo": "pytorch/pytorch", 2025-07-17T09:10:29.3301231Z "workflow_id": 16337959907, 2025-07-17T09:10:29.3301508Z "run_attempt": 1, 2025-07-17T09:10:29.3301745Z "job_id": 46154839625, 2025-07-17T09:10:29.3302014Z "workflow_name": "pull", 2025-07-17T09:10:29.3302449Z "job_name": "linux-jammy-py3.13-clang12 / test (default, 1, 5, linux.4xlarge)", 2025-07-17T09:10:29.3302897Z "json_data": "{}" 2025-07-17T09:10:29.3303135Z } 2025-07-17T09:10:29.3303651Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/16337959907/1/46154839625/metadata 2025-07-17T09:10:29.3304613Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/16337959907/1/46154839625/metadata 2025-07-17T09:10:29.3305605Z Writing 1487 documents to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/16337959907/1/46154839625/time_series 2025-07-17T09:10:29.3306635Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/16337959907/1/46154839625/time_series 2025-07-17T09:10:29.4251764Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2025-07-17T09:10:29.4252215Z with: 2025-07-17T09:10:29.4252425Z env: 2025-07-17T09:10:29.4252638Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:29.4253126Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:29.4253646Z DEVICE_NAME: 2025-07-17T09:10:29.4253877Z DEVICE_TYPE: 2025-07-17T09:10:29.4254093Z ##[endgroup] 2025-07-17T09:10:29.4280292Z ##[group]Run set -eou pipefail 2025-07-17T09:10:29.4280626Z set -eou pipefail 2025-07-17T09:10:29.4280986Z  2025-07-17T09:10:29.4281368Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-07-17T09:10:29.4281832Z for _ in $(seq 1440); do 2025-07-17T09:10:29.4282177Z  # Break if no ssh session exists anymore 2025-07-17T09:10:29.4282538Z  if [ "$(who)" = "" ]; then 2025-07-17T09:10:29.4282832Z  break 2025-07-17T09:10:29.4283118Z  fi 2025-07-17T09:10:29.4283338Z  echo "." 2025-07-17T09:10:29.4283588Z  sleep 5 2025-07-17T09:10:29.4283829Z done 2025-07-17T09:10:29.4289544Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:29.4289933Z env: 2025-07-17T09:10:29.4290159Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:29.4290643Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:29.4291163Z DEVICE_NAME: 2025-07-17T09:10:29.4291394Z DEVICE_TYPE: 2025-07-17T09:10:29.4291621Z ##[endgroup] 2025-07-17T09:10:29.4315434Z Holding runner for 2 hours until all ssh sessions have logged out 2025-07-17T09:10:29.4397630Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-07-17T09:10:29.4398233Z # ignore expansion of "docker ps -q" since it could be empty 2025-07-17T09:10:29.4398676Z # shellcheck disable=SC2046 2025-07-17T09:10:29.4399035Z docker stop $(docker ps -q) || true 2025-07-17T09:10:29.4399383Z # Prune all of the docker images 2025-07-17T09:10:29.4399722Z docker system prune -af 2025-07-17T09:10:29.4405120Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:29.4405496Z env: 2025-07-17T09:10:29.4405719Z GIT_DEFAULT_BRANCH: main 2025-07-17T09:10:29.4406200Z DOCKER_CONTAINER_ID: a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:29.4406801Z DEVICE_NAME: 2025-07-17T09:10:29.4407039Z DEVICE_TYPE: 2025-07-17T09:10:29.4407272Z ##[endgroup] 2025-07-17T09:10:40.5334277Z a8f5572e1eb0 2025-07-17T09:10:42.2695173Z Deleted Containers: 2025-07-17T09:10:42.2695676Z a8f5572e1eb072a1a970ae66c0129f43b3fb5bfeb5ae9dfc7265d3e464e54030 2025-07-17T09:10:42.2696042Z 2025-07-17T09:10:45.7562602Z Deleted Images: 2025-07-17T09:10:45.7563458Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-01345e7669bb7198df9fce7a02a4a12ce8c84f2d 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'third_party/protobuf' 2025-07-17T09:10:46.2912019Z Entering 'third_party/protobuf/third_party/benchmark' 2025-07-17T09:10:46.2964744Z Entering 'third_party/protobuf/third_party/googletest' 2025-07-17T09:10:46.3020834Z Entering 'third_party/psimd' 2025-07-17T09:10:46.3074552Z Entering 'third_party/pthreadpool' 2025-07-17T09:10:46.3128200Z Entering 'third_party/pybind11' 2025-07-17T09:10:46.3182430Z Entering 'third_party/python-peachpy' 2025-07-17T09:10:46.3236832Z Entering 'third_party/sleef' 2025-07-17T09:10:46.3291092Z Entering 'third_party/tensorpipe' 2025-07-17T09:10:46.3345676Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-07-17T09:10:46.3397813Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-07-17T09:10:46.3449619Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-07-17T09:10:46.3503304Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T09:10:46.3554943Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T09:10:46.3627990Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-07-17T09:10:46.3647110Z http.https://github.com/.extraheader 2025-07-17T09:10:46.3655836Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-07-17T09:10:46.3685199Z [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' || :" 2025-07-17T09:10:46.3977368Z Entering 'android/libs/fbjni' 2025-07-17T09:10:46.4017351Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4050801Z Entering 'third_party/FP16' 2025-07-17T09:10:46.4088125Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4121669Z Entering 'third_party/FXdiv' 2025-07-17T09:10:46.4158930Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4191584Z Entering 'third_party/NNPACK' 2025-07-17T09:10:46.4228661Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4261570Z Entering 'third_party/NVTX' 2025-07-17T09:10:46.4298862Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4331719Z Entering 'third_party/VulkanMemoryAllocator' 2025-07-17T09:10:46.4369170Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4401505Z Entering 'third_party/XNNPACK' 2025-07-17T09:10:46.4438300Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4485380Z Entering 'third_party/aiter' 2025-07-17T09:10:46.4523301Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4557077Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-07-17T09:10:46.4593173Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4635005Z Entering 'third_party/benchmark' 2025-07-17T09:10:46.4673024Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4707017Z Entering 'third_party/composable_kernel' 2025-07-17T09:10:46.4744344Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4785184Z Entering 'third_party/cpp-httplib' 2025-07-17T09:10:46.4821652Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4854066Z Entering 'third_party/cpuinfo' 2025-07-17T09:10:46.4890392Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4923400Z Entering 'third_party/cudnn_frontend' 2025-07-17T09:10:46.4959746Z http.https://github.com/.extraheader 2025-07-17T09:10:46.4992708Z Entering 'third_party/cutlass' 2025-07-17T09:10:46.5029686Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5071678Z Entering 'third_party/fbgemm' 2025-07-17T09:10:46.5108383Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5143419Z Entering 'third_party/fbgemm/external/asmjit' 2025-07-17T09:10:46.5180644Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5212756Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-07-17T09:10:46.5249494Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5291321Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-07-17T09:10:46.5327788Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5360668Z Entering 'third_party/fbgemm/external/cutlass' 2025-07-17T09:10:46.5396981Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5437513Z Entering 'third_party/fbgemm/external/googletest' 2025-07-17T09:10:46.5473411Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5505576Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-07-17T09:10:46.5541880Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5574677Z Entering 'third_party/fbgemm/external/json' 2025-07-17T09:10:46.5611666Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5647402Z Entering 'third_party/flash-attention' 2025-07-17T09:10:46.5685347Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5718927Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-07-17T09:10:46.5755503Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5795035Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-07-17T09:10:46.5831096Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5872613Z Entering 'third_party/flatbuffers' 2025-07-17T09:10:46.5909500Z http.https://github.com/.extraheader 2025-07-17T09:10:46.5945223Z Entering 'third_party/fmt' 2025-07-17T09:10:46.5982729Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6014698Z Entering 'third_party/gemmlowp/gemmlowp' 2025-07-17T09:10:46.6055714Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6089063Z Entering 'third_party/gloo' 2025-07-17T09:10:46.6126981Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6159990Z Entering 'third_party/googletest' 2025-07-17T09:10:46.6198047Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6230391Z Entering 'third_party/ideep' 2025-07-17T09:10:46.6267717Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6300024Z Entering 'third_party/ideep/mkl-dnn' 2025-07-17T09:10:46.6336718Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6377409Z Entering 'third_party/ittapi' 2025-07-17T09:10:46.6413818Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6446528Z Entering 'third_party/kineto' 2025-07-17T09:10:46.6484342Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6518235Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-07-17T09:10:46.6554896Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6589199Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-07-17T09:10:46.6625719Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6660120Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-07-17T09:10:46.6697625Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6730526Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-07-17T09:10:46.6766892Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6800008Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-07-17T09:10:46.6837043Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6870135Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-07-17T09:10:46.6906221Z http.https://github.com/.extraheader 2025-07-17T09:10:46.6940757Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-07-17T09:10:46.6977087Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7009581Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-07-17T09:10:46.7045532Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7080103Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-07-17T09:10:46.7116158Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7151217Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-07-17T09:10:46.7189932Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7224506Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-07-17T09:10:46.7261408Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7293899Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-07-17T09:10:46.7330282Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7365299Z Entering 'third_party/kleidiai' 2025-07-17T09:10:46.7402527Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7436672Z Entering 'third_party/mimalloc' 2025-07-17T09:10:46.7473487Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7505924Z Entering 'third_party/nlohmann' 2025-07-17T09:10:46.7542412Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7576852Z Entering 'third_party/onnx' 2025-07-17T09:10:46.7613145Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7663287Z Entering 'third_party/onnx/third_party/pybind11' 2025-07-17T09:10:46.7704023Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7738947Z Entering 'third_party/opentelemetry-cpp' 2025-07-17T09:10:46.7776854Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7810991Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-07-17T09:10:46.7847051Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7883929Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-07-17T09:10:46.7919635Z http.https://github.com/.extraheader 2025-07-17T09:10:46.7951794Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-07-17T09:10:46.7988576Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8020158Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-07-17T09:10:46.8056148Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8090404Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-07-17T09:10:46.8126296Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8160734Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-07-17T09:10:46.8197315Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8229675Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-07-17T09:10:46.8265706Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8298013Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-07-17T09:10:46.8334436Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8368973Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-07-17T09:10:46.8405586Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8440182Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-07-17T09:10:46.8476216Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8528491Z Entering 'third_party/pocketfft' 2025-07-17T09:10:46.8565380Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8599086Z Entering 'third_party/protobuf' 2025-07-17T09:10:46.8637587Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8674719Z Entering 'third_party/protobuf/third_party/benchmark' 2025-07-17T09:10:46.8710783Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8743023Z Entering 'third_party/protobuf/third_party/googletest' 2025-07-17T09:10:46.8780096Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8813522Z Entering 'third_party/psimd' 2025-07-17T09:10:46.8852851Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8886273Z Entering 'third_party/pthreadpool' 2025-07-17T09:10:46.8923626Z http.https://github.com/.extraheader 2025-07-17T09:10:46.8955922Z Entering 'third_party/pybind11' 2025-07-17T09:10:46.8993053Z http.https://github.com/.extraheader 2025-07-17T09:10:46.9025821Z Entering 'third_party/python-peachpy' 2025-07-17T09:10:46.9062304Z http.https://github.com/.extraheader 2025-07-17T09:10:46.9096167Z Entering 'third_party/sleef' 2025-07-17T09:10:46.9132375Z http.https://github.com/.extraheader 2025-07-17T09:10:46.9164879Z Entering 'third_party/tensorpipe' 2025-07-17T09:10:46.9201394Z http.https://github.com/.extraheader 2025-07-17T09:10:46.9233884Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-07-17T09:10:46.9270011Z http.https://github.com/.extraheader 2025-07-17T09:10:46.9302256Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-07-17T09:10:46.9338880Z http.https://github.com/.extraheader 2025-07-17T09:10:46.9371338Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-07-17T09:10:46.9407751Z http.https://github.com/.extraheader 2025-07-17T09:10:46.9440246Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-07-17T09:10:46.9476263Z http.https://github.com/.extraheader 2025-07-17T09:10:46.9507820Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-07-17T09:10:46.9545983Z http.https://github.com/.extraheader 2025-07-17T09:10:46.9665275Z A job completed hook has been configured by the self-hosted runner administrator 2025-07-17T09:10:46.9692292Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-07-17T09:10:46.9697626Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-07-17T09:10:46.9698032Z ##[endgroup] 2025-07-17T09:10:46.9809186Z [!ALERT!] Swap in detected! [!ALERT!] 2025-07-17T09:11:05.6797859Z Cleaning up orphan processes